Once the weather turns, and autumn sets in we want to eat hearty, comforting meals. There are plenty of beef and lamb stews and casseroles recipes, but we know that wild venison and its lean meat has a delicious and intense flavour and because it is so lean, it works best in long and slow cooked dishes. Wild deer eat a natural diet of grass, fruits and berries, acorns, apples and chestnuts which is why venison meat that is so lean and low in saturated fat.
Soak the porcini mushrooms in boiling water for 20 minutes, then drain and ensuring you keep the mushroom liquid, roughly chop the mushrooms.
Heat a pan on medium head and add the smoked bacon lardons cook until golden and then put to one side.
Toss the diced venison in seasoned flour and the mixed spice ensuring each piece has a thin dusting of flour. Using the same pan as you used to cook the lardons, heat a big splosh of oil and add the venison, in small batches, browning all sides and then putting to one side (this may take 3 or 4 batches).
Add the onions and garlic to the pan (adding more oil if necessary), and cook for 2 minutes until softening, then add the carrots and parsnips, turning then in to the onion and garlic covering them with all the oil and juices from the pan.
Bring everything to the boil and then turn down to simmer on a low heat, cover the pan and cook for an hour. Now add the sliced mushrooms, cover the pan and continue to cook for another 30-40 minutes.


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Minsk 2019 European Games can be visited without a Visa
The foreign fans have started to arrive in Minsk. They have been granted Visa-free access to the country for the 2nd European Games 2019, which will be held in June.
The Visa-free entry to Belorussia started to operate on June 10th for the foreign fans of the 2nd European Games and will be open for the fans till 10th of July. To enter Belarus without a visa is possible not only through the national airport "Minsk" but also through other crossing points, as well as from the territory of the Russian Federation. The Visa-free entry for the period of the tournament is provided to the tourists from 74 countries.
The documentation process at the border crossing is being carried in priority order by the separate lines, which can be distinguished with the events logo. The first fans have crossed the border on 10th of July. The participants of the 2nd European Games arrived to the city primarily by plane, whilst fans have used cars and trains for travel.
The 2nd European Games 2019 will take place in Minsk from 21st till the 30th of June. According to the program, it is planned to play approximately 200 series of medals in 15 sport types. It is anticipated that games in Minsk will be visited by 4 000 sportsmen, trainers, and personnel, as well as 30 000 foreign fans. Moreover, a number of higher officials from several countries has been invited to the opening ceremony.
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Published on May 19, 2020 in Music/The Café by Marlena Fitzpatrick
Pianist, Composer and Arranger Carolina Calvache is pleased to announce the release of her second album as a bandleader, Vida Profunda, out May 29th, 2020 on Sunnyside Records. On this dynamic new release, Calvache explores meaningful poetry by world-renowned poets, augmenting the text with her stunning compositions, lush instrumentation and striking vocal performances by Ruben Blades, Claudia Acuña, Haydee Milanes, Aubrey Johnson, Marta Gomez, Sofia Ribeiro, Luba Mason, Lara Bello, and Sara Serpa. The first single "Te Conoci de Nuevo", featuring Ruben Blades, will be available on April 17, 2020.
The diverse list of instrumentalists on this release includes drummers Jonathan Blake and Keita Ogawa; bassists Petros Klampanis, Ricky Rodriguez and Peter Slavov; violinists Tomoko Omura, Leonor Falcon, Ben Russell, Annaliesa Place, and Adda Kridler; violists Allysin Clare, Jocelin Pan; cellists Brian Sanders and Diego Garcia; flautist Hadar Noiberg; oboist Katie Scheele; trombonist Achilles Liarmakopoulous; bass clarinetist Paul Won Jin Cho; bongo and djembe player Samuel Torres; plus special guests: harmonica player Grégoire Maret and trumpeter Michael Rodriguez.
The album's title track Vida Profunda (Profound Life), perfectly exemplifies the message of this heartfelt project. "Vida Profunda" is a poem that the composer's father would recite to her everytime she felt frustrated and lost. Carolina remarks: "[My father] is the one that actually encouraged me to find in poetry some answers to my feelings and emotions, and also to put music to the text, since that is my way of expression." Inspired by her father, who is a literature teacher and lover of poetry, Carolina set out on a path to find meaning through the synthesis of two distinctly different artforms: poetry and jazz. The track is based off of the poem by Porfirio Barba Jacob, a poem that speaks to our humanity; the vulnerability of man. As an album, Vida Profunda utilizes the work of such notable poets as Pablo Neruda, Robert Duncan and Gabriel Garcia Marquez, as well as Carolina's own written word.
For the second track, "El Parajo yo", the infectious melody and supple harmony came first. Carolina asked her mother what her emotional response was to the music and her mother responded, "…it is like flying like a bird." Her mother's visceral reaction led her to the poem "El Pajaro yo" by Nobel Prize Winning Chilean Poet-Diplomat Pablo Neruda. Sofia Ribeiro lends her vocals to this gorgeous track which also features a stirring solo by flautist Hadar Noiberg. The legendary Ruben Blades is featured on the next track "Te Conocí de Nuevo" (I Met You Once More), which features lyrics written by Carolina. His warm unwavering vocals tell the tale of a failed couple that meets together again, this time for good. Carolina notes: "it is like a promise that says "I want you for the rest of my nights and days, I don't want to lose you again." The sincere refrains of Calvache's lyrical piano solo display the composer's stunning facility on the keys. "Working with Ruben was incredible. He just has an amazing ear and he is very particular when he records. It was wonderful to hear a legend like him record one of my original songs," said Calvache.
Another track featuring Calvache's original lyrics, "Sin un Despido" was inspired by the LaMia Flight 2933 tragedy- the fatal plane crash that killed the Brazilian soccer team Chapecoense on their way to the 2016 Copa Sudamericana Finals. Transfixed by the unfolding events of this terrible catastrophe, Carolina imagined losing a husband or family member in that way, robbed of the ability to say goodbye. This track features the strength and grace of renowned vocalist Claudia Acuña.
"Stella" was written for Carolina's mother for her 50th birthday. This gorgeous diatribe from daughter to mother expresses Carolina's following in her mother's footsteps as a musician. This piece, reminiscent of Horace Silver's classic "Lonely Woman" features harmonica player Grégoire Mare and vocalist Haydee Milanes. On the final track on the album, "Let Me Come With You", singer Luba Mason sings in english a piece based on the poem "Moonlight Sonata" by Greek poet Giannis Ritsos. Carolina was particularly drawn to this line: "I know that each one of us travels to love alone, alone to faith and to death. I know it. I've tried it. It doesn't help. Let me come with you." At the time of recording this song, Carolina was listening to a great deal of broadway vocalists, and was very attracted to Luba Mason's romantic and somewhat broadway delivery. "I fell in love with her voice in this particular style…It was a dream come true to have her on this album."
On Vida Profunda, Calvache strives to connect with the listener on an intimate and personal level in ways that only poetry can do. "This album is a mirror of experiences and the emotions that can be found in everyone's life. Feelings of hope, grief, solitud, frustration, dreams, love, inspiration are part of life… and that is why I called this album "Profound Life", or Vida Profunda."
Vida Profunda is Calvache's first release since her debut Sotareño. Released by Sunnyside in 2014, Sotareño marked her arrival as the first Colombian musician to be signed to the storied label and featured the pianist alongside Antonio Sanchez, Michael Rodriguez, Jaleel Shaw, Hans Glawischnig and Ludwig Afonso. Calvache, born in Cali, Colombia, first rose to prominence in 2011 when she was selected for the 2011 Mary Lou Williams Women in Jazz Festival in Washington, DC. That same year, she moved to New York City and began collaborating with an impressive roster of musical voices including Henry Cole, Jorge Roeder, David Binney, Samuel Torres, Tivon Pennicott, John Elis, Jaleel Shaw, Linda Oh, Rodrigo Recabarren, Luba Mason, Ross Pederson, Camila Meza, Uri Gurvish, Roman Filiu, Rodney Green, Sara Serpa, Ricky Rodriguez, Annette Aguilar, and Yasushi Nakamura, among many others. She holds degrees from Universidad de Valle and North Texas University.
Follow Carolina Calvache @carolinacalvach
Tags: AlbumJazzLatin JazzRuben BladesVida Profunda
Vida Profunda
A chat with Carolina Calvache, a classically trained jazz pianist from Colombia
Tobias Meinhart: A 'Silent Dreamer' Speaking Out Through Jazz
"Apart from music and in a broader sense, I'm convinced the more we learn and get
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HomeConcertsMegadeth End Tour With Sold Out Performance At The Armory
Megadeth End Tour With Sold Out Performance At The Armory
September 29, 2021 Langen Goldstien Concerts 0
Although still recovering from the absolutely amazing trip I had to Punk Rock Bowling in Vegas, well, there's no sleep for the wicked… right? One of the heavier tours to hit the road this year made an appearance at The Armory in Minneapolis on Tuesday night and who was I to miss it. I'll be the first to admit, the line-up wasn't full of bands that I listen to on a daily basis but it was full of bands that I have mad respect for so I somehow gathered the strength, pulled it together, and made it downtown just in time to catch the first of the four acts on the bill.
A personal favorite of mine, Hatebreed, was first up. Although I was a bit in a mood as I walked in just due to stress, exhaustion, and the fact that a giant spider had disappeared somewhere into my kitchen and I didn't have the time to find it and spend minutes trying to figure out what to do with it (I absolutely despise spiders), it all melted away as the band took the stage just seconds after I walked into the enormous Armory. Vocalist Jamey Jasta mentioned that, as the opening band, Hatebreed's job was to warm things up– get people moving and get those heads banging. To say they did that and then some would be an understatement. Hatebreed wasted no time jumping into a ten-track set that instantly set the sold-out audience on fire. From the first note of "Empty Promises" and until the final note of "I Will Be Heard" was left ringing in everyone's ears, the audience had turned into one big, happy, sweaty mess. Hatebreed will always leave me stunned with the perfection and energy they bring to the stage and their performance on Tuesday night was no different. Their sound, albeit heavy and aggressive, was flawless and their energy was just what I needed to help me get through the remaining three acts of the show.
I'll be honest, the next band to hit the stage, Trivium, has never quite been my thing when it comes to just listening to their music but their live show is something that will keep me coming back and last night was a good reminder of that. The sheer musicianship of this band shined as bright as the epic light show that accompanied them. Their eight-song set was full of epic guitar solos and absolutely slamming drum licks magnified only by the excitement felt throughout the crowd. Much like the band to follow them, I was absolutely floored by the amount of passion that could be felt radiating from the audience to the stage. Although I know very little about Trivium and really have no major emotional attachment to the band, it was amazing to get that feeling from the people around me and it added a very cool element to the night.
That sense of admiration continued to reign supreme as Trivium left the stage and made way for Lamb of God. The set changeovers were quick and the crack of fireworks that kicked off Lamb of God's set caught me a bit off guard but also sent the audience into a beautiful frenzy. Much like Trivium, LoG is another band where, although their music really does very little for me on a day-to-day basis, their live show will keep me coming back time and time again. Complete with balls of fire and an epic stage presence, this band blew through their twelve-song set like it was nothing and left no survivors by the time it was all said and done. After sleeping on how the show went, although I want to say Hatebreed stole the show (which they did musically for me), it was LoG who really brought that fuel and fire to the stage and left me completely speechless. Again, like during Trivium, I found myself lost in a sea of people who were screaming along to every word with such a sense of conviction that I found myself questioning my overall thoughts on this band. There's something about them that makes them have this almost cult-like following and I know well enough to know that it's not just their live show. There's something about their music that seems to draw their fans in and I plan on spending some time of LoG's music to try and figure out what exactly that is.
Metalhead or not, you either know the name Dave Mustaine, the word Megadeth, or you've at least seen their highly recognizable logo. Megadeth are legends… there's no denying that and it was proven during their headlining set on Tuesday. Just hearing this band play will leave you speechless. Flawless guitar solos, epic basslines, slamming drum parts, and powerful vocals to top it all off– this band is one of those bands that, although they have been around for decades, never seem to falter when it comes to the talent they bring to the music scene. Even though their set was only about ten tracks long, each track came with epic musicianship that had me completely lost in the moment and losing track of time. Compared to the explosive production value that Lamb of God brought to the night, Megadeth's set seemed a bit understated but that's what made it perfect. There was nothing to distract from the pure musicianship of the members of this band and it really gave the audience a chance to just appreciate everything this band has done for the metal scene. I'm not a diehard metalhead or even Megadeth fan. If you know me, you know that I actually tend to shy away from major bands from the past like this just because I feel like I connect with newer music on a deeper level so I really couldn't tell you what songs Megadeth played or much of their backstory other than the drama that has surrounded this band. What I can tell you is they had me completely captivated throughout their set last night and left me feeling satisfied and energized.
Jamey Jasta
Cool Shows Next Week 10/4 – 10/10/2021
Don't Miss Chase Atlantic's Beauty in Death North America Tour 10/2 at The Fillmore
Antistar CD Release Show at The Park in Fridley, MN on Saturday, April 9th, 2016
April 11, 2016 Jeremy Larsen 0
If you were looking for a dose of local music, then The Park in Fridley was the place to be on Saturday, April 9th!! Antistar, Gabriel and the Apocalypse, My Zero, and Midwest Mayhem played […]
Hanson Sells Out Mystic Showroom
November 10, 2019 Lk Gannett 0
Sometimes the universe conspires against you and you must arrive 1 hour and 20 minutes late for a show. You will wonder if it will really be worth a 40 minute drive just to catch […]
Live Interview: New Album From The Plott Hounds Ready For Release
June 2, 2015 Dave Rubene 0
Twin Cities Media has expanded our reach and is now on YouTube!! You can expect to see some really cool interviews and media event highlights in the very near future. We caught up with The […]


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Motherwell ready to bounce back after tough run
Motherwell, Scotland – Motherwell coach Stuart McCall was "bitterly disappointed" by his team's exit from the Scottish League Cup in midweek, but now the Steelmen will turn their attention back to the league for a home match with Aberdeen.
Motherwell began its league campaign with 10 points from four games, but the club has earned just three from its last four matches while being outscored 12-4.
And now following a defeat on penalty kicks to Hibernian in the League Cup, McCall's team must regain its early-season form.
Motherwell still sits in third place in the SPL table despite its dip in results, and Aberdeen should give the club a chance to get back into the win column.
The Dons have won one of eight games to begin the season and enter the contest on the back of successive draws.
Rangers has won six straight games in the league and aims for a seventh at Dunfermline on Saturday, while Celtic tries to bounce back from a 4-2 defeat to Rangers last weekend when the Hoops host last-place Inverness.
Dundee United has won just one of its last six games and visits Hibernian, Kilmarnock and St Mirren get together at St Mirren Park and Hearts, which is unbeaten in five league games, tries to extend that streak on Sunday at St Johnstone.


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Home News Reports IndiaFacts to launch a report on discrimination and violation of Human Rights against Hindus in India
IndiaFacts to launch a report on discrimination and violation of Human Rights against Hindus in India
OpIndia Staff
Recently the media had thrown is weight behind the much publicised #NotInMyName campaign which claimed to protest against the supposed rise in lynching the country after Narendra Modi ascended to power. That campaign was unfortunately extremely lopsided in nature which only focused on Muslim victims while disregarding the many victims from other communities.
Now IndiaFacts which is a website which publishes content related to Indology, cross-civilisation issues, dharma among others has decided to bring out the other side of the story with the launch of a Hindu Human Rights Report 2017 . This report deals with the systemic and episodic human rights violation which take place against Hindus in India. This according to IndiaFacts also includes state sanctioned discrimination which is carried out by the state itself.
As pointed out by them, there is no dearth of Human Rights reports produced in India but most of these reports exclude a sizeable number of violations on the mistaken premise that such incidents can only happen against the "minority community". As stated by them, they created this report in order to rectify this key omission and also to provide a corrective to the one sided reporting by International organisations. The report has among other things discussed the persecution of the majority by the ruling ideologies in Tamil Nadu among other places.
This IndiaFacts Hindu Human rights report prepared by Mayank Patel and edited by Nithin Sridhar, Sankrant Sanu focuses on incidents which have largely taken place in 2015, 2016 while other incidents have also been added to provide a historical context.
This report would be officially launched on August 19th at 4:30 PM in a ceremony at INTACH, 71, Lodhi Estate in New Delhi. Retired IPS officers and authors of the book, "The Majority Report", Ram K Ohri & J P Sharma would launch it. Madhu Kishwar, the Founder Editor of Manushi and Professor at ICSSR is the guest speaker at the event.
Here's a poster of the event for more details:
IndiaFacts
Madhu Kishwar
NotInMyName
OpIndia reached out to SP of Baghpat police station who has asserted that Imam Imlaq-Ur-Rehman was not made to chant Jai Shree Ram, his beard was not pulled and there was no religious undertone to this assault
Facing backlash, India Today removes Hinduphobic statement of Hauz Qazi march being 'marred' with Jai Shri Ram slogans
In the earlier version of the report, India Today had mentioned that the Unity March was 'marred' with chants of 'Jai Shri Ram'.
Citizens of India, please make up your mind: a 'Secular' Nation or a Hindu Rashtra
vigoe - July 12, 2019
There should be no doubt that the mandate was against the decades-old politics of Muslim appeasement and 'secularism' and was for the revival of old Indian civilization values.


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Simon Community Scotland boosted by law firm donation
Homelessness charity Simon Community Scotland (SCS) has received a boost after the Glasgow office of law firm Irwin Mitchell raised £6,555 to support its work.
The money was raised as part of the firm's Charity of the Year programme, a nationwide initiative developed by the Irwin Mitchell Charities Foundation (IMCF).
Established in 1997, IMCF is an independent registered charity set up by the firm which has now donated more than £2 million to third sector organisations and raised £284,000 for good causes throughout the UK in 2018.
Simon Community Scotland was nominated by the Glasgow office in recognition of the vital work the charity performs helping people in and around the Glasgow area affected by homelessness or who need advice related to accommodation issues.
The money raised will go towards providing sleeping bags for rough sleepers, survival kits for extreme weather and welcome packs for young people to stay in a volunteer's home for the night.
Mark Higgins, regional managing partner at Irwin Mitchell's Glasgow office, said: "We are absolutely delighted to be able to provide this donation to this fantastic cause within the local community. Our Charity of the Year initiative has always been something that our employees are very passionate about and they worked extremely hard to ensure we can generate funds to support SCS. It is a truly brilliant organisation doing some amazing work and we hope this donation will provide vital support."
Lorraine McGrath, chief executive of Simon Community Scotland, added: "We are hugely grateful to everyone at Irwin Mitchell's Glasgow office for this fantastic donation, which will allow us to make a huge difference to a lot of people's lives. Working with the firm was an absolute pleasure and we truly appreciate the hard work and effort that has gone into raising these funds."
Tags: Simon Community Scotland, homelessness, Irwin Mitchell
Cadder Housing Association mourns loss of vice-chair New homes mark first step in regeneration of Paisley's West End New council houses officially opened in East Renfrewshire


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@thesocycinema / videos
Piracy, Pierre Bourdieu, and the Unmasking of a Sexist Habitus
Pirates sing about respecting women in this skit from Key & Peele's comedy show.
Tags: bourdieu, culture, gender, inequality, theory, habitus, patriarchy, sexism, sexual assault, sexual harassment, subtitles/CC, 00 to 05 mins
Access: YouTube
Summary: Women have been stepping forward with allegations of sexual harassment and assault against powerful men, but it would be a mistake to fall victim to the belief that women's outrage is entirely new. However, what is new is the deluge of allegations, the sustained public attention to the matter, and that the women making the accusations are largely presumed to be telling the truth. With each epiphany, Americans are forced to confront the uncomfortable proposition that sexual harassment and assault are a constitutive part of office politics. Moreover, sexism is alive and well in the halls of American government, and it circulates within the private orbits of our most worshiped celebrities. So while it is certainly true that not all men harass women, women know that everywhere they go, there is the possibility of being harassed by men. It's as certain as the shark-infested waters beneath the plank of a pirate ship. • Back in 2015, when such allegations were more readily ignored and dismissed, the comedic duo Key & Peele seemed to grasp the scale of the problem and developed a comedy sketch featuring a pub full of gravely-voiced pirates. These drunken men of the sea are shown gathered in a dimly-lit tavern as the archetypal masculine men occupying a quintessentially masculine space. However, the brilliance of this skit is that Key & Peele play against type for laughs, and instead of bellowing songs that objectify and degrade women, they offer matter-of-fact statements of men's shared humanity with women: "I once met a lass so fine, 'twas drunk on barley wine, I'd been to sea for months a'three, I knew I could make her mine. And the lass was past consent, and we threw her in bed, and rested her head, and left cuz that's what gentlemen do." • Enter the social theorist Pierre Bourdieu and his concept habitus, which refers to a system of dispositions that vary by time, place, and social position. Drawing on this concept, one might say that the clip is funny because the pirates defy expectations in that they fail to embody and employ a sexist habitus. As viewers, we implicitly understand that casually disrespecting women is, in a sense, the lingua franca among these types of men in these types of spaces. This video can be used to illustrate Bourdieu's famous concept, but students can also be challenged to think beyond Key & Peele's fictional characters. What of the sexist habitus in the halls of government and in corporate boardrooms? What are the dispositions that define these spaces?
Lester Andrist, Ph.D.
The Truth about Jessica Alba's Race
Tags: biology, knowledge, race/ethnicity, social construction, subtitles/CC, 00 to 05 mins
Summary: In this short video from 2011, Jessica Alba appears on the talk show Lopez Tonight and endures George Lopez's comedic preamble before hearing the results of her ancestry DNA test. According to Lopez and the language used by the testing company, Alba is 87 percent European and 13 percent Native American. But to the astute observer, the show is actually claiming to reveal more than the details of Alba's genetic composition. It is also claiming to reveal information about her "true" race, and therein lies the problem. • As I have noted elsewhere on The Sociological Cinema, racial categories do not consistently correspond to biological observations (see here and here). To put it another way, race is not based on biology; it merely claims to be. This point often confuses people, for they reason that in some sense race must be biological. After all, skin color is largely a genetically determined characteristic, and so the thinking goes, race must be too. But while genetic instructions largely determine the amount of melanin a body produces, it is through socialization that people become predisposed to notice skin color as one of the most salient features a body can have. The thickness of one's eyebrows, the shape of their ears, or the color of their of eyes—these are also genetically determined characteristcs, but people mostly discount these features as the bearers of useful information. To socially construct race, then, is to teach people which physical characteristics are the salient markers of a racial group, and racism becomes possible once people begin assigning meanings to those salient racial markers. • At about the 45-second mark, Lopez explains that the DNA analysis distinguishes between four ancestral groups, and perhaps sensing that it would resonate with his audience, he incorrectly equates each ancestral group with a race. The Europeans, according to Lopez, are white, Sub-Saharan Africans are black, East Asians are Asian, and the Indigenous American group refers to Native Americans. The results confuse Alba, who racially identifies as Latina and knows her last name comes from Spain. But it is instructive to dwell a bit on the basis of her confusion, for it highlights the incompatibility between ancestral DNA and race. In the United States people with Spanish heritage tend to be racially categorized as Hispanic or Latino, which is a category believed to be distinct from white. However, DNA analyses show that the original inhabitants of the Iberian Peninsula (i.e. Spain, Portugal, the small UK overseas territory of Gibraltar, and the Principality of Andorra) are a part of the same general migratory group of homo sapiens that settled the rest of Europe. As with all racial categories, contemporary distinctions between whites and Hispanics in the United States are socially created and not based on some deeper biological truth.
Submitted By: Lester Andrist
Do You Have a Black Friend?
Chuck struggles to name any black friends he has.
Tags: race/ethnicity, racial homophily, social networks, white habitus, 00 to 05 mins
Summary: In this clip from Jimmy Kimmel Live's Pedestrian Question segment (where they ask people on the street some type of question), his crew asks whites whether or not they have a black friend and the audience predicts their response. For example, a white woman from Australia admits not having a black friend, and Chuck from Ohio claims to have a black friend but then cannot name him/her, and a black man is asked if he has any white friends and he says "not really" because "white people are kind of scary." A couple white respondents do report having a black friend, but are sometimes asked to "prove it." While tackling a socially sensitive topic with humor, the clip works on several levels: it reveals the reality of racially homogeneous networks and the audience's disbelief of some whites having a black friend underscores the tendency of people to claim such connections when they don't exist. This phenomenon must be understood in the context of racial segregation, which has persisted in a very high degree within the US. Through segregation, blacks, whites, and Hispanics all develop group cohesion and identity, and develop networks that are often racially homogenous. However, this practice of racial homophily (in which people tend to associate with members of their own race) is strongest among whites because they experience the highest degree of racial isolation. In Racism Without Racists, Eduardo Bonilla-Silva (2013) conceptualizes this in terms of the white habitus, or whites' racial taste, perceptions, feelings, and emotions and views on racial matters, including a greater degree of comfort interacting with other whites. Bonilla-Silva argues that this results from a process of racial socialization that comes from all areas of life, including who you interact with in your neighborhood, schools, and social networks. Furthermore, because whites do not want to be perceived as racist in such a society, it is common for them to claim having black (or nonwhite) friends. Chuck's statement that he has a black friend in the clip, but was unable to name him/her, was actually a theme found in Bonilla-Silva's research. For additional context, see this series of clips from Anderson Cooper: this video reveals how this process starts in childhood; this video shows children's views on interracial friendships; and this video shows why racial diversity matters in forming views on race.
Submitted By: Paul Dean
Is there White Privilege in Soul Music?: Blue-Eyed Soul and the Myth of Meritocracy
Adele, Jazmine Sullivan, and privilege in soul music stardom.
Tags: art/music, culture, inequality, race/ethnicity, black culture, meritocracy, pop culture, privilege, soul, 00 to 05 mins
Summary: This video examines the history and legacy of soul music, and the advantage "blue-eyed soul" singers may have over Black soul singers in today's music scene. The video begins noting the traditional dimensions of soul, including ad-libbing, belting, and unpredictability, which "historically symbolized resistance against racist oppression" and the idea of soul music as a form of racial protest and activism. But the history of black music shows that it has been underrated, uncredited, stolen, and absorbed into other genres. At the same time, white artists like Adele and Sam Smith dominate the genre. An argument of meritocracy falsely suggests this is a result of fewer Black artists, that they are not trying hard enough, or they are not writing and making enough soul music (a similar argument was made about the lack of black performances on the 2016 Academy Award nominees). Several examples (e.g. Leon Bridges) are offered to show that this argument of a meritocracy in soul music is a myth, and that black artists tend to get rejected from music competitions and awards at early stages, despite their ability. It explores the role of privilege in the process. It notes that Adele and Sam Smith do indeed deserve success, but emphasizes that all people, regardless of their race, should have an equal shot at it. This video is the third episode from a series by Amraj Lally, called "POPTOPICS". This series is a critical and theoretical analysis on trending pop culture topics, aiming to stimulate intellectual discussion. For a multimedia analysis of the institutionalization of white privilege and how to fight against it, check out our blog post.
Submitted By: Amraj Lally
News Media Falsely Depicts Black 4-Year Old as Violent Criminal
News clip editing reproduces stereotypes of black boys.
Tags: crime/law/deviance, inequality, media, prejudice/discrimination, psychology/social psychology, race/ethnicity, confirmation bias, framing, linguistic intergroup bias, journalism, news media, 00 to 05 mins
Summary: This video is put together by the Maynard Institute, which is "dedicated to helping the news media accurately portray all segments of society, particularly those often overlooked, such as communities of color." The beginning of the video shows a real Chicago CBS news segment that covers the shooting of two teenagers, which was witnessed by several children, including a 4-year old. The commentator describes the "disturbing reaction" by the 4-year old boy, who is shown saying that "I'm not scared of nothing." When the reporter asks him if he will stay away from guns, the boy responds: "I'm going to have me a gun!" The segment ends with Anchor Steve Bartelstein saying, "that is scary indeed." The issue with this clip is what was omitted from the story. In the actual interview with the 4-year old boy, the reporter next asks "Why do you want to have [the gun]?" The boy states "I'm going to be the police!" and the reported responds "Okay then you can have one." Subsequent content from the video, and this Maynard Institute analysis, outline some of the problems with the edit, including its portrayal of African American boys in a stereotypical manner. While it is unclear why this particular video was edited in this way (the news station removed the story once the problem was identified), the reality is that such editing decisions frequently occur and continue reproducing stereotypes. Indeed, "studies of media content consistently find that black criminal suspects are portrayed more frequently and more menacingly than white suspects in television news stories of violent crime" (Peffley et al 1996). It could be a result from a news editor's confirmation bias, where an individual looked for content in the video that confirmed his expectations, thereby using that footage and eliminating the (real) footage that would have led to an alternative framing. It might also derive from the economic drive for media to present controversial news that they believe viewers will be more interested in watching (thereby consuming more advertisements). For example, research shows that viewers find crime news stories more memorable when the perpetrator is dark-skinned male. What is the impact of this on race relations? Experimental studies show that such "even a brief visual image of an African American male suspect in a televised crime story was capable of activating racial stereotypes, which in turn heavily biased whites' evaluations of the suspect along racial lines" (Peffley et al 1996). Such race-related TV news stories also lead to linguistic intergroup bias, where "people use more abstract language to describe stereotype-congruent behaviors, particularly when that person is a member of an out-group" (Gorham 2006). Thanks to Logan Webster for suggesting this video.
Youth Scrutinize the Hidden Curriculum, Why Don't Adults?
The latent vs. manifest functions of education.
Tags: art/music, children/youth, education, inequality, knowledge, adolescence, hidden curriculum, latent functions, manifest functions, pedagogy, performance poetry, spoken word, structural functionalism, youth studies, 00 to 05 mins
Summary: In this spoken word performance entitled "Somewhere in America," three high school students share their experiences as youth in America's educational system. The video can be used to illustrate features of the structural-functionalist perspective, and specifically how sociologists working within this framework—e.g., Emile Durkheim, Robert Merton, and Talcott Parsons—distinguished between the manifest and latent functions (and dysfunctions) of various social phenomena. Here, the teenagers discuss the functions and implications of the manifest versus latent curriculum, and how these two curriculums play out in the lives of children. The poets argue that the manifest curriculum, which is the content teachers are required to teach, is not what students remember most; rather, the girls suggest that the latent or hidden curriculum is the knowledge that becomes more deeply embedded in students' memories and daily interactions. The hidden curriculum refers to the values, beliefs, and attitudes that are transmitted to students through the education system; a latent function of these hidden lessons is that these help to socialize young individuals to form a more "cohesive" society. In doing so, the views and values of the dominant culture are coached into the minds of young school-goers. Arguing that the biggest lessons in school "won't come from a syllabus," the words of these poets reinforce decades of social scientific research stating that schools go farther than simply advancing the academic success of children; schools also instill social and cultural ideas within students (for one prominent example, see Ann Arnett Ferguson's research on the role of public schools in constructing Black masculinity). The group closes by saying, "The greatest lessons [in school] are the ones you don't remember learning." While we've chosen to highlight the structural-functionalist contours of the poem, viewers are encouraged to think about how the teens' poem can be read through other foundational sociological lenses, namely, social conflict theory and symbolic interactionism. For example, the students' attention to power inequalities that emerge from the education system resemble a social conflict approach. This supplementary video explaining the three foundational sociological perspectives and how they relate to education can help viewers with this analysis.
Submitted By: Jordan Grier and Valerie Chepp
Bourdieu's Theory of Cultural Capital
Sociologist Pierre Bourdieu (1930-2002)
Tags: class, inequality, knowledge, cultural capital, embodied state, institutionalized state, objectified state, pierre bourdieu, 00 to 05 mins
Summary: This short tutorial video summaries Pierre Bourdieu's (1930-2002) theory of cultural capital, a concept that is defined in this video as "the cultural knowledge that serves as currency that helps us navigate culture and alters our experiences and the opportunities available to us." The video goes on to elaborate by discussing three different forms of cultural capital—embodied state, objectified state and institutionalized state—and provides examples of each type that students can apply to their own lives. At the end of the video, discussion questions are included that can assist students in applying the concept of cultural capital to what is happening in the world today. This is one of several videos on The Sociological Cinema that illustrate Bourdieu's ideas, including his theory of taste (here, here, and here), different forms of capital, habitus, and social distance. In addition to this tutorial video, The Sociological Cinema's has several videos that use examples from popular culture to teach Bourdieu's concept of cultural capital, which includes clips from the popular reality TV show Wife Swap, scenes from The Wire (here and here), the movie Clueless, and this excerpt from the PBS documentary film People Like Us: Social Class in America.
Submitted By: Sociology Live!, Cindy Hager
Consent: Not Actually That Complicated
Tea making: a helpful analogy for understanding consent.
Tags: emotion/desire, sex/sexuality, consent, safe sex, sex-positive, 00 to 05 mins
Summary: This video was made by Sexplanations, a video show focused on "shame-free, comprehensive efforts to educate on everything sex." This particular episode is based on a post from the blog Rockstar Dinosaur Pirate Princess, titled "Consent: Not Actually That Complicated." The piece serves as a nice counter-narrative to the pervasive discourse that frequently surrounds discussions about sexual consent, which too often suggest an element of confusion or "blurred lines" when it comes to consent. In order to make the case that consent is not actually that complicated, the author likens initiating sex to making a cup of tea, and walks the listener/reader through the various scenarios in which people might find themselves initiating sex. When placed in the context of tea making and drinking, suddenly ideas about consent come across as obvious and non-controversial. The author ends the post asking, "Do you think this is a stupid analogy? Yes, you all know this already – of course you wouldn't force feed someone tea because they said yes to a cup last week. Of COURSE you wouldn't pour tea down the throat of an unconscious person because they said yes to tea 5 minutes ago when they were conscious. But if you can understand how completely ludicrous it is to force people to have tea when they don't want tea, and you are able to understand when people don't want tea, then how hard is it to understand when it comes to sex?" In addition to being useful in a classroom setting, this video would also be effective to show during educational workshops and trainings on consent, safe sex, and/or sexual assault.
Submitted By: Valerie Chepp
A Youth Perspective on Society: Tupac Shakur at Age 17
17-year-old Tupac Shakur reflects on society in this 1988 interview.
Tags: children/youth, class, education, inequality, intersectionality, race/ethnicity, social mvmts/social change/resistance, adolescence, sociology of youth, standpoint theory, youth studies, 21 to 60 mins
Summary: Scholars working within the interdisciplinary field of Youth Studies often highlight the limited ways in which youth and their unique lived experiences are portrayed in popular discourse and academic literature. For example, discourses around adolescent sex and sexuality—and specifically adolescent female sexuality—frequently rely on ideologies of fear, shame, and restraint (Fields 2008; Fine and McClelland 2006; Fine 1988). Andreana Clay (2012) points to the ways sociologists tend to focus on "deviant" behavior within youth culture: "By focusing on gangs or the consumption of fashion, music, and the media, scholars have pointed to a crisis among youth, particularly youth of color and working class youth. Recent attacks on affirmative action, increases in police brutality and racial profiling, and new anti-youth legislation have exacerbated this sense of crisis, urgency, and hopelessness among critics, community activists, scholars, and the youth themselves" (Clay 2012:3). Often missing from popular portrayals of youth and youth culture is a perspective that comes directly from youth themselves. • Filmed prior to his experience with stardom, in this 1988 interview, rapper Tupac Shakur (1971-1996) articulates his perspective on society, told from the standpoint of being a teenager, Black, and poor. Only 17-years-old at the time, the interview is full of wisdom and insight, as Tupac talks about various aspects of society from this unique intersectional vantage point. Prominent themes include his reflections on what it feels like to be a teenager growing up in the late 1980s, youth stereotypes, and the deep desire youth have for being respected. He provides context and nuance for why his generation seems angry, rebellious, and scared, pointing in part to the ways in which prior generations of adults have left behind a world in crisis that the younger generation must fix. He also critiques America's antiquated education institution, and how school curriculums fail to prepare his generation for today's world. Advocating for a more socially and intellectually relevant adolescent education, Tupac suggests classes on drugs, "real" sex education, scams, religious cults, police brutality, apartheid, American racism, poverty, and food insecurity. Using the example of foreign language education, Tupac underscores the irrelevancy of learning something like German ("When am I going to Germany?! I can hardly pay my rent in America!") and the need for young people to learn the basics of English, as well as "politicians' double talk." Citing rising homicide, suicide, and drug abuse rates, Tupac provides a glimpse of his gift for poetry and incisive social commentary when he argues, "More kids are being handed crack than being handed diplomas." He further advocates for his own unique perspective of society and its significance when he proposes that adults and youth, and rich and poor, temporarily switch roles, so that each group can understand and experience the others' realities. • Tupac concludes the interview talking about social change, and the role of youth within movements for change. Throughout the interview, Tupac reflects frequently on his mother, Afeni Shakur, who was an active member of the Black Panther Party in the late 1960s and early 70s. The influence of this political legacy is evident in Tupac's own political consciousness. Asked what he can do when he grows up, Tupac talks about the challenges of social change, and how the structures of society make change difficult. Using the metaphor of a maze of blocks in which mice roam, Tupac says, "Society is like that. They'll let you go as far as you want, but as soon as you start asking too many questions and you're ready to change, boom, that block will come." Tupac expresses his disillusionment with our political leaders and democratic process, but he also alludes to his own sense of hope, as he is actively engaged in political organizing around issues of safe sex and teen violence. At the time of the interview, he and his high school friends are trying to reinvigorate the Black Panthers' political efforts, particularly their vision around education and Black pride.
Social Class, Parenting Styles, and Intergenerational Inequality
In the movie Clueless, self-mastery is a skill taught to rich kids.
Tags: children/youth, class, education, inequality, marriage/family, bourdieu, cultural capital, parenting, self-efficacy, self-mastery, socialization, status, 00 to 05 mins
Summary: Several generations of sociological researchers discovered parenting styles vary by social class. Post World War II, Melvin Kohn ([1969] 1977) found middle-class parents stress self-mastery and creativity in their children; working-class parents focus on instilling conformity and making their children obedient to authority. At the turn of the century, Annette Lareau's ([2001] 2011) research found working-class parents focus on providing basic necessities for their children while largely leaving their kids alone to socialize themselves; middle- and upper-class parents focus on instilling self-mastery in their children, often through activities, constructive interactions with others, and the learning of making choices and actions to bring about desirable outcomes. More recently, Jessica McCrory Calarco (2014) found classed parenting styles influence education, as there is a positive relationship between class status and a student's likelihood of taking a proactive role in their learning. These cross-class parenting styles and their influence on education is depicted in this edited clip from the movie Clueless (1995), where a wealthy and powerful father encourages his daughter to achieve high academic marks not through hard work and study, but through creative negotiation and maybe even the outright manipulation of teachers. According to generations of research on cross-class parenting styles, it would be unlikely working-class parents would emphasize negotiation as a life skill, perhaps because these fathers and mothers often feel powerless themselves. Thus, alongside the economic resources that are especially important to intergenerational mobility, middle- and upper-class parents also pass along a form of social capital to their child that offers advantages in modern workplaces that do not reward subordination to authority, but rather incentivize the ability to bend and manipulate authorities towards one's own interests and desires.
Submitted By: Jason T. Eastman
61+ Mins
Abortion/Reproduction
Aging/Life Course
Demography/Population
Discourse/Language
Economic Sociology
Government/The State
Historical Sociology
Immigration/Citizenship
Organizations/Occupations/Work
Politics/Election/Voting
Rural/Urban
Subtitles/CC
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text:
\section{Introduction}
\label{sec:introduction}

The amount of information shared on online social media has been growing at unprecedented rates during recent years. Platforms such as Twitter are used for spreading news and opinions \cite{kwak2010twitter,bakshy2011everyone}, coordinating social protest efforts \cite{conover2013geospatial,conover2013digital},
aggregating individuals with common interests \cite{wu2011says}, and more. The uncontrolled nature of social media makes them vulnerable to exploitation for spreading spam, rumors, slander, and other types of misinformation~\cite{chew2010pandemics,metaxas2010obscurity}. In the domain of politics, the more subtle phenomenon of \emph{astroturfing} has received attention in the recent literature \cite{ratkiewicz2011detecting,ratkiewicz2011truthy}. Astroturfing arises when one or a few individuals make a coordinated effort to create the false impression of a spontaneous (\emph{grassroot}) movement, inducing users to deem the information reliable and feed its propagation.

The detection of these kinds of orchestrated campaigns is becoming a key problem, and a challenging one. It requires the ability to classify the massive amount of content continuously produced on online social media. Manual labeling is infeasible on a large scale. The task is also difficult due to the limitations of the textual content typical of online social media. For example, Twitter enforces a maximum length of publishable messages (\emph{tweets}) of 140 characters. Therefore, we postulate that the unit of classification should not be a single tweet, but rather a \emph{meme}, defined as a unit of information --- an idea or a concept --- that can spread from person to person through the social network. Equivalently, we can think of a meme as the \emph{set of tweets} carrying the same piece of information. Once a meme is identified, supervised learning methods can be applied to classify different types of communication.

\begin{figure*} \centering
	\includegraphics[width=.8\textwidth]{architecture_horiz}
	\caption{DESPIC architecture for meme clustering and classification}
	\label{fig:architecture}
\end{figure*}

We are developing a platform for Detecting Early Signatures of Persuasion in Information Cascades (\emph{DESPIC}), whose architecture is depicted in Fig.~\ref{fig:architecture}. There are two core components: a message clustering algorithm that takes a stream of tweets and groups them into memes, and a meme classification algorithm that labels these memes according to categories of interest. In this paper we focus on the clustering framework using Twitter as a test-bed scenario for our analysis.

Classic document clustering techniques based on lexical analysis alone are again ineffective due to the sparsity of text, the limited context of individual tweets, and the use of references to external content. We therefore propose a strategy that leverages various sources of available metadata in addition to text. Tweets may contain \emph{hashtags}, informally defined textual tokens that are used to identify topics of discussion; \emph{mentions} of other users that are used to address messages to their attention and help identify the contributors of a conversation; and \emph{URLs} that point to external resources. We can easily group messages based on these atomic entities, that we call \emph{protomemes}.

None of these entities alone, however, is necessarily capable to capture a meme at the appropriate level of granularity; a protomeme can be too specific or too general as a concept. Furthermore, each protomeme may only capture a particular aspect of a conversation, while a meme may require a more nuanced description. We argue that  combinations of protomemes may provide meaningful signatures of memes. Operationally, we propose a pre-clustering step based on protomemes.

\subsection*{Contributions and outline}

This paper formalizes the problem of meme clustering and proposes an operational definition of \emph{memes} as overlapping clusters of related tweets, aggregated according to content- and network-based features. In the remainder of the paper we make the following contributions.

\begin{itemize}

\item We introduce the notion of \emph{protomemes} that provide an effective way to pre-cluster messages in real-time, streaming social media scenarios

\item We define several similarity measures between memes, leveraging various content, metadata, and network features of tweets; and we propose different ways to combine them for clustering memes

\item We compare multiple clustering algorithms, finding that
hierarchical clustering outperforms K-means in terms of the trade-off between quality of the clusters and their number and size

\item We show that pre-clustering based on protomemes is an effective strategy compared to clustering the original tweets.

\item Finally, we compare different similarity measures and their combinations. We show that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters for robust performance.
Our algorithm outperforms baseline methods, including one that exploits full information about the underlying social network.

\end{itemize}

\section{Clustering framework}
\label{sec:methodology}

The meme clustering problem is defined for any social media platform used to spread messages on a directed social network; microblogging systems like Twitter, Google Plus, and Yahoo! Meme are popular examples. In these systems, users are connected by directed links: using Twitter terminology, one \emph{follows} others to see their messages. Users can re-post (\emph{retweet}) any seen post (\emph{tweet}), spreading it to their \emph{followers}.

In the following we introduce the notion of \emph{protomeme} and describe how protomemes have been incorporated into our clustering methodology.

\subsection{Defining protomemes}
\label{sub:protomemes}

Let us define a set of features that can be easily extracted from a tweet and used to at least partially identify the topic of the tweet~\cite{ratkiewicz2011detecting,ratkiewicz2011truthy}:

\begin{description}

\item[Hashtag:] Twitter users can incorporate in the text of their tweets one or more \emph{hashtags}, textual tokens prefixed by hash marks (\#), that identify the topic of the message.

\item[Mention:] We say that a tweet \emph{mentions} a user when it includes the target's username preceded by the `@' symbol, thus addressing that specific user.

\item[URL:] Tweets may include links to external sources of information. A \emph{URL} is the Web address identifying a linked resource.

\item[Phrase:] The textual content of a tweet that remains after removing hashtags, mentions, URLs, stop words, and punctuation, and after stemming words~\cite{Porter80}, is defined as a \emph{phrase}. Phrases may capture semantically equivalent lexical variations of textual messages.

\end{description}

\begin{figure*}[t!]
\centerline{
\includegraphics[height=2.2in]{protomeme_project2}
\includegraphics[height=2.2in]{protomeme_space}
}
\caption{Left: Relations among protomemes, tweets, users, and tweet content. Right: Projections of protomemes onto spaces based on their tweet, user, and content features that inform corresponding similarity measures.}
\label{fig:protomeme_space}
\end{figure*}

Hereafter we will refer to instances of these features as \emph{entities}.
Consider the tweet \textsl{``@All\_4Given Gingrich: Romney Most Likely Nominee http://t.co/CectDLni \#All4Given''} as an example. This tweet contains four entities: the hashtag \texttt{\#All4Given}, the mention \texttt{@All\_4Given}, the URL \url{http://t.co/CectDLni}, and the phrase \texttt{gingrich romnei most like nomine}. In the following we will denote as \emph{protomeme} the set of all tweets that contain a specific entity. Think of a protomeme as a primitive meme. One consequence of this representation is that protomemes overlap; the tweet in the above example belongs to four protomemes.

Additionally, any tweet is accompanied by a plethora of \emph{metadata}, such as author information (e.g., username, number of tweets and followers, self-reported user location), temporal and geographical information (e.g., timestamp and latitude/longitude coordinates of the tweet), retweet information, and so on. The set of features could be expanded to exploit such metadata.

Protomemes leverage the \emph{wisdom of the crowd}~\cite{golder2006usage,mika2007ontologies}: users exploit content features that allow for the effective identification of discussion topics (hashtags), ongoing conversations (mentions), or external resources (URLs). Thus, by adopting protomemes we intend to alleviate the problem of text sparsity, which has proven to hinder the application of topic modeling techniques to Twitter~\cite{hong2010empirical}.

Moreover, protomemes can aid in the task of clustering messages in a streaming scenario, as they are easily extracted in real time by defining a set of matching rules, such as regular expressions. Incoming tweets are seamlessly added to existing protomemes, which form natural initial tweet clusters. Protomemes therefore provide an efficient pre-clustering strategy to aggregate messages.

Our social media clustering framework uses protomemes as the fundamental units. Natural similarity measures can be defined over the protomemes (sets of tweets), to aggregate related protomemes into broader memes.

\subsection{Similarity measures}
\label{sub:measures}

Fig.~\ref{fig:protomeme_space} (left) illustrates the mutual relations between protomemes, the tweets they contain, their content, the users who post them, and the underlying follower network. We can define similarity measures between protomemes by considering the projections of the protomemes onto spaces induced by these features, also depicted in Fig.~\ref{fig:protomeme_space} (right).

Let us provide a few preliminary definitions. Let $P_{\ell}$ be a set of tweets, $U_{\ell}$ be the set of users that produced tweets in $P_{\ell}$ ($|U_{\ell}| \leq |P_{\ell}|$ because a user may post more than one tweet), and $W_{\ell}$ be the set of terms  obtained by concatenating all tweets in $P_\ell$. We can now  define a set of measures, which we will apply to compute the similarity between protomemes.

\begin{definition}[Common user similarity] $S_{u}$ between protomemes $P_i$ and $P_j$ is  the cosine similarity between their user frequency vectors
\begin{equation}
S_u(P_i, P_j) = \frac{\sum_{u \in U_i \cap U_j} P_{iu} P_{ju}}{\sqrt{\sum_{u \in U_i}P_{iu}^2}  \sqrt{\sum_{u \in U_j} P_{ju}^2}}
\label{eq:cus}
\end{equation}
where $P_{\ell u}$  is the number of times user $u \in U_{\ell}$ adopts protomeme $P_{\ell}$.
\end{definition}

\begin{definition}[Common tweet similarity] $S_t$ between protomemes $P_i$ and $P_j$ is  the cosine similarity between their (binary) tweet vectors
\begin{equation}
S_t(P_i, P_j) = \frac{|P_{i} \cap P_{j}|}{\sqrt{|P_{i}|} \sqrt{|P_{j}|}}.
\label{eq:cts}
\end{equation}
\end{definition}

\begin{definition}[Content similarity] $S_c$ between protomemes $P_i$ and $P_j$ is the cosine similarity between their TF-IDF vectors
\begin{equation}
S_c(P_i, P_j) = \frac{\sum_{w \in W_i \cap W_j} P_{iw} P_{jw}}{\sqrt{\sum_{w \in W_i}P_{iw}^2}  \sqrt{\sum_{w \in W_j} P_{jw}^2}}
\label{eq:cds}
\end{equation}
where $P_{\ell w}$ is the TF-IDF weight assigned to term $w \in W_{\ell}$.
\end{definition}

Since we cannot assume that information about the follower social network is available to the clustering algorithm, let us exploit mention and retweet metadata as a proxy for the underlying network structure to define a forth similarity measure. Let $N_{\ell} = U_{\ell} \cup M_{\ell} \cup R_{\ell}$ be the diffusion set of $P_{\ell}$, where $M_{\ell}$ is the set of users mentioned in tweets in $P_{\ell}$, and $R_{\ell}$ is the set of users who have retweeted posts in $P_{\ell}$.\footnote{Note that $R_{\ell}$ is not necessarily a subset of $U_{\ell}$ when only a sample of the tweets are considered in the stream; the sample may include a retweeted message but not the original one.}

\begin{definition}[Diffusion similarity] $S_d$ between protomemes $P_i$ and $P_j$ is the cosine similarity between their diffusion (binary) vectors
\begin{equation}
S_d(P_i, P_j) = \frac{|N_{i} \cap N_{j}|}{\sqrt{|N_{i}|} \sqrt{|N_{j}|}}.
\label{eq:cns}
\end{equation}
\end{definition}

\subsection{Combinations}
\label{sub:combinations}

There are many ways to combine different similarity measures. One of the goals of our experimentation will be to explore how different similarity measures and combinations of them affect the quality of the meme clusters. In the following we introduce two different methods incorporated in our framework.

The \emph{pairwise maximization strategy} aims at choosing the measure that provides the highest value every time we compute the similarity between two protomemes. The rationale is to capture the characteristics of a particular pair of protomemes; the relatedness of two protomemes may be best described by their content while that of two other protomemes may be more obvious by considering users, say.
Given a set of similarity measures $S_1, \dots, S_n$, the \emph{maximum pairwise similarity} is formally defined as
\begin{equation}
MAX(P_i, P_j) = \max_k \{S_k(P_i, P_j)\}.
\label{eq:maximization}
\end{equation}

A second approach is a \emph{linear combination} of similarity measures, extending the  idea of averaging~\cite{Qazvinian12,reisinger2010multi}.
Formally,
\begin{equation}
\mathcal{L}(P_i, P_j) = \sum_k \omega_k S_k(P_i, P_j)
\label{eq:combination}
\end{equation}
with the constraint that $\sum_k \omega_k = 1$,  allowing for a normalized combination such that $\mathcal{L}(P_i, P_j) \in [0,1]$ (assuming $ \forall k \; S_k \in [0,1]$).
The set of parameters $\omega_1, \dots, \omega_n$ introduces an $(n-1)$-dimensional parameter space whose exploration is instrumental for understanding what combinations of similarity measures provide the best performance in terms of clustering. This aspect will be investigated in detail in our experiments.

\subsection{Clustering algorithms}
\label{sub:clustering}

The ideal clustering framework should allow for the detection of memes (clusters) at different levels of granularity --- in some cases one might prefer small clusters of tightly related protomemes, in other cases a smaller number of broader groups may be required. \emph{Hierarchical clustering algorithms} are naturally designed to span a range of granularities. On the other hand, if the desired number of clusters is known in advance, \emph{K-means} offers an efficient alternative. Since different clustering algorithms work best for different tasks, we will evaluate whether hierarchical or K-means clustering is better suited for our task.
Of course there are many other clustering methods; we only consider these two widely adopted techniques as we are more interested in the role of the protomemes and various similarity measures in determining cluster quality.\footnote{In the next section we extensively discuss criteria for determination of cluster quality.} Once a set of objects to cluster (tweets or protomemes) and similarity measures among objects are defined, it is possible to apply any off-the-shelf clustering algorithm --- or to design new ones.

Both algorithms can appropriately work in our platform, taking as input the protomemes produced in the pre-clustering detection system from the datastream. In our system the clustering is intended as an \emph{asynchronous}, \emph{off-line} process, that at predetermined time intervals analyzes and clusters the amount of protomemes captured in a recent time window (say, the last hour of data). New tweets incoming from the social stream can be assigned to the existing clusters via the protomeme pre-clustering processing until the next execution of the clustering algorithm will further refine the existing clusters, exploiting additional data points and disregarding  protomemes not observed in recent data.

Regarding hierarchical clustering, we adopt the \emph{average-linkage} method to determine the similarity among clusters. The similarity between two clusters is simply the average pairwise similarity among all protomemes belonging to them. This approach alleviates the sensitivity of the algorithm in the presence of outliers. The similarity matrix among all protomemes is computed once at the start.

\begin{figure}
	\includegraphics[width=\columnwidth]{dendrogram_sim}
	\caption{Number of clusters (top) as a function of the similarity threshold used to cut the dendrogram (bottom) in hierarchical clustering.}
	\label{fig:dendrogram_sim}
\end{figure}

Once the dendrogram has been generated by the hierarchical clustering algorithm, a dendrogram cut is applied by picking a similarity threshold $\tau$. This process  allows for tuning the performance of hierarchical clustering to find the correct trade-off between number and size of clusters, as shown in Fig.~\ref{fig:dendrogram_sim}. In our experimental trials we will show how the number of clusters (determined by the choice of $\tau$) affects the quality of the clustering solution. While K-means clustering requires to select the number of clusters in advance, one can run K-means for different numbers of clusters, exploring a similar range of solutions.

\section{Evaluation}
\label{sec:experiments}

In this section we discuss a systematic evaluation process carried out to assess performance in the meme clustering task. First we describe a dataset adopted as ground truth in our evaluation. Then we introduce a quality metric, motivating its adoption and giving some intuition for how it works.

\subsection{Ground truth dataset}

For evaluation purposes, we used an existing dataset of hand-curated tweets as ground truth.
Tweets about political news regarding the US presidential primaries were collected during April 2012 using the Twitter APIs (\url{dev.twitter.com/docs/api}). To verify the completeness of the dataset, we compared it with Twitter's \emph{gardenhose} sample, which collects about 10\% of all public tweets. We observed that the number of tweets in the dataset was roughly ten times larger than those in the gardenhose data. This suggests that the dataset includes nearly all of the tweets that were published about these topics during the observation period. The dataset comprises of 5,523 tweets containing 2,866 URLs, 2,780 hashtags, and 1,848 mentions.
Note that exact duplicates and retweets were removed from the dataset. This possibly penalizes the performance of the clustering algorithms by biasing the similarity measures. For example, retweet information is not available when computing diffusion similarity.

We annotated the set of tweets in two steps.
We first manually reviewed all tweets in the dataset identifying topics consisting of at least three tweets.
Then we labeled each tweet with one or more topics.
Our annotations resulted in a set of clusters corresponding to 26 topics.
Some examples of tweets contained in the dataset and relative cluster assignments are reported in Table \ref{tab:examples}.

Given the brevity of tweets, most (92.1\%) discuss only a single topic.
However, 7.9\% of the tweets were assigned to more than one cluster.
We will exploit this information during our evaluation to assess whether our clustering framework is able to capture such overlap.
Table \ref{tbl:classes} reports the composition of the clusters obtained after the manual annotation.

\begin{table*}[!t] \centering \footnotesize
\caption{Examples of thematically related tweets manually assigned to one of the 26 clusters (``Santorum out the race'').}
\begin{tabular}{l}
\hline
Rick Santorum ends presidential campaign after conceding to Mitt Romney in phone call - The Ticket - Yahoo! News http://t.co/L6IYHt6d \\
Santorum suspends campaign, clearing Romney's path http://t.co/HC3XtptZ \#cnn \\
\#BREAKING: Rick \#Santorum suspends his campaign for president, making Mitt \#Romney likely Republican nominee.\\
MITT ROMNEY EXPRESS: Is Rick Santorum In Or Out Of The 2012 Race? http://t.co/zva9ZK3i	\\
Rick Santorum quits campaign to leave field clear for Romney http://t.co/oKVBUYo3 \#News \#CNN \#Politico	\\
\hline
\end{tabular}
\label{tab:examples}
\end{table*}

\begin{table}[!t] \centering \footnotesize
\caption{Composition of the clusters obtained by manual labeling. The overlap ratio is the percentages of tweets in a cluster that also belong to at least another cluster.}
\begin{tabular}{crr|crr}
\hline
Cluster & Tweets & Overlap  & Cluster & Tweets & Overlap  \\
 &  &  \multicolumn{1}{c|}{ratio} &  &  &  \multicolumn{1}{c}{ratio} \\
\hline
1	&	1,654	&	16.14\%	&	14	&	57	&	28.07\%	\\
2	&	1,522	&	13.14\%	&	15	&	54	&	35.18\%	\\
3	&	405	&	29.62\%	&	16	&	49	&	24.49\%	\\
4	&	376	&	6.64\%	&	17	&	43	&	0.00\%	\\
5	&	355	&	14.92\%	&	18	&	41	&	4.88\%	\\
6	&	343	&	17.20\%	&	19	&	35	&	20.00\%	\\
7	&	245	&	8.57\%	&	20	&	29	&	0.00\%	\\
8	&	230	&	9.56\%	&	21	&	27	&	22.22\%	\\
9	&	128	&	3.91\%	&	22	&	21	&	14.28\%	\\
10	&	97	&	18.55\%	&	23	&	20	&	0.00\%	\\
11	&	86	&	15.11\%	&	24	&	4	&	25.00\%	\\
12	&	71	&	0.00\%	&	25	&	4	&	0.00\%	\\
13	&	63	&	6.35\%	&	26	&	3	&	0.00\%	\\
\hline
\end{tabular}
\label{tbl:classes}
\end{table}

\subsection{Evaluation metric}

To assess the quality of the clusters, we adopt a measure based on \emph{Normalized Mutual Information} (\emph{NMI}) \cite{danon2005comparing}. The NMI assumes the availability of a {\em ground truth} that represents the correct clusters. Let $A$ be the correct cluster assignment, and suppose that it contains $c_A$ clusters. Let $B$ be the output of a clustering algorithm operating on the same data and producing $c_B$ clusters. We can define a $c_A \times c_B$ \emph{confusion matrix} $\mathbf{N}$, whose rows correspond to the clusters in $A$ and whose columns represent clusters in $B$. Each entry $N_{ij}$ of this confusion matrix reports the number of elements of the correct $i$-th cluster that happen to be assigned to the $j$-th cluster by the clustering algorithm. The \emph{Normalized Mutual Information} is defined as

{\small
\[
NMI(A,B) = \frac{-2 \displaystyle\sum_{i = 1}^{c_A}\sum_{j = 1}^{c_B} N_{ij} \log \left(\frac{N_{ij}N}{N_{i\cdot}N_{\cdot j}} \right)}{\displaystyle\sum_{i=1}^{c_A}N_{i \cdot} \log \left(\frac{N_{i \cdot}}{N}\right) + \displaystyle\sum_{j=1}^{c_B}N_{\cdot j} \log \left(\frac{N_{\cdot j}}{N}\right)}
\label{eq:nmi}
\]
}
where $N_{i \cdot}$ (resp., $N_{\cdot j}$) is the sum of the elements in the $i$-th row (resp., $j$-th column) of the confusion matrix, and $N$ is the sum of all elements of $\mathbf{N}$. The output of this measure is normalized between zero (when the clusters in the two solutions are totally independent), and one (when they exactly coincide). Therefore, the higher the value of NMI, the better the quality of the clusters found by the algorithm.

Measures based on mutual information have been shown to best capture different facets of a clustering process, such as how well a clustering algorithm reflects the number, size, and composition of clusters with respect to the ground truth, as opposed to some other widely used measures, which may produce biased evaluations \cite{meilua2007comparing}. Our investigation with accuracy, precision, recall, and $F_1$ confirmed the limitations of these measures, all of which report indistinguishable results due to the dominance of true negatives. Purity, on the other hand, is by definition biased toward rewarding the presence of tiny clusters, which tend to be pure. For these reasons, NMI has been recently adopted in the evaluation of tasks such as event detection in social media~\cite{becker2010learning}.

The previous definition does not work well in the case of overlapping clusters. We therefore adopt in our evaluation a variant called LFK-NMI, which accounts for overlapping clusters. Details on the formulation of LFK-NMI are outside the scope of this paper, and can be found in the paper by Lancichinetti \emph{et al.}~\cite{lancichinetti2009detecting}.

\section{Results}
\label{sub:results}

There are many potential configurations of our clustering framework; next we present experiments designed to evaluate several aspects of meme clustering.

\subsection{Hierarchical vs. K-means clustering}
\label{sub:algorithms}

\begin{figure}[!t] \centering
	\includegraphics[width=.9\columnwidth]{low_HierKmeansClusteringONMIComparison_errorbars}
	\caption{Performance comparison between hierarchical and K-means clustering. The task for this experiment is that of clustering protomemes using a single similarity measure, namely content similarity. The error bars for K-means are standard errors based on five runs for each number of clusters.}
	\label{fig:kmeans_vs_hierarchical}
\end{figure}

The first experiment  aims at choosing one clustering algorithm. Fig.~\ref{fig:kmeans_vs_hierarchical} compares hierarchical and K-means clustering algorithms.   We report the value of LFK-NMI as a function of the number of clusters, obtained with each of the clustering methods. By varying the similarity threshold $\tau$ in hierarchical clustering, we obtained partitions with different numbers of clusters (cf.~Fig.~\ref{fig:dendrogram_sim}). We then ran K-means for each of the corresponding numbers of clusters.

K-means generally performs  well at finding a very small number of clusters, but the quality of discovered clusters quickly deteriorates when the number of clusters increases. Hierarchical clustering outperforms K-means over a broad range of values for the number of clusters, and also achieves a better overall cluster quality.

While the experiment reported in Fig.~\ref{fig:kmeans_vs_hierarchical} is based on protomemes and content similarity, we systematically explored other measures. All experiments provided the same verdict: hierarchical clustering outperforms K-means in our meme clustering task on Twitter. As a result, we employ hierarchical clustering in the rest of our evaluation.

\subsection{Experimental setup}
\label{sub:exp-protomemes}

Our second and more important experiment aims at assessing whether protomemes convey a concrete advantage in the task of clustering memes in social media. To this purpose we compare two different configurations of our clustering framework that operate with protomemes against two configurations that operate directly on individual tweets.
The description of these four configurations follows.

\textsl{Baseline:}
	This configuration is a straightforward implementation of hierarchical clustering of simple tweets.
	The similarity measure adopted to compare tweets and aggregate them is content similarity based on TF-IDF.

\textsl{Baseline+Followers:}
	This configuration is inspired by the event detection system recently proposed by Aggarwal and Subbian~\cite{aggarwal2012event}, namely a tweet clustering algorithm based on both content- and network-based features.
	The main limitation of this approach is that it relies on the full knowledge of the follower network of all users present in the dataset.
	Such  information is very time-consuming to obtain and this task seems unfeasible in real-time, streaming scenarios.
	To compute tweet similarity, the authors adopt TF-IDF, and we reproduced this choice in our implementation. Follower similarity is implemented using Eq.~\ref{eq:cns} but with actual follower sets; this is a variation of the original formulation, which is not applicable in our framework. The two similarity measures are linearly combined with equal weights, and used with hierarchical clustering (note that hierarchical clustering provides better performance than K-means in this case as well.)

\textsl{MAX:}
	In this configuration we adopt protomemes as the atoms of our clustering algorithm.
	We use all four similarity scores defined above ($S_t$, $S_u$, $S_c$, and $S_d$), and combine them via the maximum pairwise similarity (Eq.~\ref{eq:maximization}).

\textsl{Linear combination:}
	The last configuration also exploits all four similarity measures, combined by way of a linear combination (Eq.~\ref{eq:combination}). We discuss how to determine the parameters of the linear combination later in this section.

\begin{figure}[!t] \centering
	\includegraphics[width=.9\columnwidth]{parameter_search_withoutother_CluNoVSONMIComparison_cus1_cds7_cts0}
	\caption{Performance of different clustering algorithms, as a function of the number of clusters.}
	\label{fig:lfk_nmi_results_noBundle}
\end{figure}

Fig.~\ref{fig:lfk_nmi_results_noBundle} presents a comparison between the performance of the four configurations.
The baseline achieves its best performance for a number of clusters that is comparable to the number of tweets, which is not very helpful in our setting. This is understandable since either the tweets are very similar, or due to the sparsity of the text, their similarity is likely close to zero. The Baseline+Followers algorithm performs better, achieving higher quality and with fewer clusters. This configuration, however, might not be viable for our task in the streaming scenario.
The MAX strategy obtains its best performance for a much smaller number of clusters, even outperforming Baseline+Followers when the number of clusters is closer to that in the ground truth. This is remarkable given that MAX does not have access to the full follower network. We interpret these results as evidence that protomemes provide a significant advantage.
This advantage becomes further evident considering the performance of the linear combination. This configuration provides a slight improvement over the MAX strategy, although the parameters are optimized with knowledge of the ground truth.

\begin{figure}[!t] \centering
\includegraphics[width=.9\columnwidth]{pyramid_alternative_cb}
\caption{Slices of the 3-simplex showing LFK-NMI values originated by the linear combination of the four similarity measures: \emph{common user similarity} ($S_u$) on the bottom edge, \emph{content similarity} ($S_c$) on the right edge, \emph{common tweet similarity} ($S_t$) on the left edge, and \emph{diffusion similarity} ($S_d$) across the slices. The combination yielding highest LFK-NMI in each slice is highlighted.
}
\label{fig:parameter_space}
\end{figure}

To determine the weights of the linear combination of the four similarity measures (Eq.~\ref{eq:combination}), we used a greedy optimization procedure.
We ran the hierarchical clustering algorithm for each set of weights in a 3-dimensional simplex with step 0.1, resulting in 286 parameter configurations.
For each parameter set we found the best LFK-NMI value, irrespective of the number of clusters. We finally selected the best overall setting.

Fig.~\ref{fig:parameter_space} shows that optimal solutions (high LFK-NMI values) are provided by non-trivial combinations of the four parameters.
The highest LFK-NMI peak is obtained with the following weights: $\omega_t=0.0$, $\omega_c=0.7$, $\omega_u=0.1$, $\omega_d=0.2$. Common tweet similarity does not contribute to this particular configuration, although it is to be noted that other parameter settings yield higher LFK-NMI for different values of the number of clusters (for example, another configuration with $\omega_t=0.2$ yields compareble LFK-NMI for fewer clusters.)

\subsection{Cross-validation}
\label{sec:cross-validatin}

The optimization procedure outlined above for tuning the parameters of the linear combination of similarity measures uses knowledge about the ground truth labels for both learning the weights and evaluating the quality of the clusters. This runs the risk of over-fitting on a training set. To assess the robustness of our performance evaluation, we performed a cross-validation analysis.

We opted for 5-fold cross-validation so that the test sets would not be too small for cluster quality evaluation.
We randomly assigned each tweet to one of the 5 folds,
guaranteeing equal fold sizes and unbiased distribution of topics across each fold, in spite of the considerable class imbalance (cf.~Table~\ref{tbl:classes}). For each iteration, we used the combination of 4 folds as training data (to optimize the weights as described in the previous subsection), and the remainder 20\% of the tweets as test set (to measure clustering quality). Performance of the clustering algorithm was computed after splitting the ground truth topics consistently with the generated folds. Results were finally averaged across the 5 runs of the cross-validation test.

\begin{figure}[!t] \centering
	\includegraphics[width=.9\columnwidth]{5foldevaluation_CluNoVSONMIComparison_errbar}
	\caption{Results of 5-fold cross-validation on our dataset. Error bars represent standard errors on the number of clusters and LFK-NMI across folds, for each dendrogram cut.}
\label{fig:cross-validation}
\end{figure}

Fig.~\ref{fig:cross-validation} shows the results of our cross-validation analysis.
It is worth noting that the test sets contain only 20\% of the data, and therefore the number of clusters is smaller than that reported in Fig.~\ref{fig:lfk_nmi_results_noBundle}, where performance was assessed on the entire dataset. To obtain a meaningful comparison, we evaluated the other algorithms on the same test sets. They perform relatively better on this easier clustering task, as reflected in higher values of LFK-NMI compared to Fig.~\ref{fig:lfk_nmi_results_noBundle}.

The cross-validation analysis demonstrates that the performance obtained with the learned linear combination of similarity measures is not statistically better than that obtained with the MAX strategy.
Interestingly, we can achieve close to optimal performance without having to assume prior knowledge of the ground truth, which of course makes our clustering algorithm more amenable to a realistic streaming scenario.

\section{Related work}
\label{sec:related-work}

This work, to the best of our knowledge, is the first to formalize the problem of clustering memes in online social media. Recent literature has discussed related problems, such as the identification of topics or memes  \cite{leskovec2009meme,xie2011visual,simmons2011memes} or emerging events in social streams \cite{sayyadi2009event,cataldi2010emerging,mathioudakis2010twittermonitor,marcus2011twitinfo,lehmann2012dynamical}.

Leskovec \textit{et al.}~\cite{leskovec2009meme} presented \textsl{memetracker}, a platform that tracks memes produced in online media such as mainstream news sites and Weblogs. \textsl{Memetracker} can group together short, distinctive phrases that act as signatures of specific topics and identify small variations of them. This creates groups of news on related topics that can be tracked over time to define patterns of diffusion in the news cycle. \textsl{Memetracker} identifies and aggregates disjoint memes on the basis of textual similarity but no systematic evaluation of the quality of the retrieved memes is provided. Our work instead focuses on the assessment of the quality of the meme clustering process, and allows for overlapping memes.

The problem of tracking news for meme extraction has been tackled also by Simmons \textit{et al.}~\cite{simmons2011memes}. Based on the \textsl{memetracker} dataset, they investigated the extent to which information evolves and mutates due to collective processing of social media users. While defining protomemes, we rooted our work on the findings of both Leskovec \emph{et al.}~\cite{leskovec2009meme} and Simmons \emph{et al.}~\cite{simmons2011memes}, expanding on the aggregation of meme variants based not only on textual similarity, but also on other network and meta-data features.

Our framework shares some similarities also with another line of research on event detection systems.
Aggarwal and Subbian~\cite{aggarwal2012event} presented a clustering algorithm that exploits both content- and network-based features to detect events in social streams. We adapted their algorithm to work in the context of meme clustering. Unfortunately, the algorithm assumes a preexisting knowledge about the follower network of Twitter users. In a streaming scenario, such information is expensive to get, especially when encountering popular users. In our framework, we proposed to rely on mention and retweet diffusion sets, which can be inferred in real-time from the observed data.
Also, we achieved better performance by pre-clustering based on protomemes and relative similarity measures.

Agarwal \emph{et al.}~\cite{agarwal2012real} recently proposed a graph-based algorithm for the real-time discovery of clusters in dynamic networks. The strategy is based on the discovery of dense clusters on the inferred graph of correlated keywords, extracted from tweets in a given time-frame. This method relies on the adoption of the \emph{short cycle property} that allows to find a local approximate solution. Performance of the system has been tested by using a simulated stream of tweets based on events reported by Google news in a given period, yielding high precision/recall in the task of identifying the largest events.

Concluding, Becker \emph{et al.}~\cite{becker2011beyond} presented an event classification system designed for Twitter. The authors used temporal features in addition to social and topical ones. These features are adopted to train a classifier that consumes manually annotated clusters of data points representing specific events on Twitter. The best performance is provided by SVM, being a Naive Bayes classifier used as baseline. The results provided by the authors are promising and represent a starting point in the task of classifying different types of memes in Twitter.

\section{Conclusions}
\label{sec:conclusions}

In this work we formalized the problem of clustering memes from social streams such as Twitter, and we presented a framework to deal with this task.
Our clustering framework adopts a novel pre-clustering procedure, namely protomeme detection, aimed at identifying atomic tokens of information inside tweets.
Due to its efficiency, this strategy should be particularly well suited to work in streaming scenarios. Additional work will be needed to empirically confirm this conjecture.

Several similarity measures among protomemes have been defined, leveraging various features including content- and network-based ones, to build clusters of semantically and structurally related tweets.
The proposed diffusion similarity measure uses mention and retweet information, that can be reconstructed in real-time from the observed data, considering each protomeme diffusion set.
We carried out a systematic evaluation showing the promising performance of the clustering framework, by using a manually-curated dataset as a ground truth.
The best trade-off between quality, number, and size of clusters is obtained by pre-clustering using protomemes, and combining similarity measures exploiting heterogeneous features, with a simple pairwise maximization strategy. This approach performs as well as methods that assume prior knowledge of the data, and better than methods that assume knowledge of the underlying social network.

As for future work, we will extend the set of features incorporated by our clustering framework, considering for instance images. Furthermore, our preliminary analysis suggests that the introduction of time series as  features may yield significant performance improvements.

Our long-term plan is to integrate the meme clustering framework with a meme classifier to distinguish engineered types of social media communication from spontaneous ones. This platform will adopt supervised learning techniques to classify memes and determine their legitimacy, with the aim of early detection of attempts to spread misinformation and deceiving campaigns. The platform will be optimized to work with the real-time, high-volume streams of messages.

\section*{Acknowledgment}
The authors are grateful to Qiaozhu Mei, Sergey Malinchik, and Zhe Zhao for fruitful discussions.
This work is supported by NSF (grants CCF-1101743 and IIS-0968489), DARPA (grant W911NF-12-1-0037), and the McDonnell Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

{\footnotesize
\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


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