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Thought Leader Interview with Howard Guttman: "When Goliaths Clash - Managing Executive Conflict
by: David Creelman
HR.com's Industry Gurus Newsletter
This article is based on an HR.com Webcast. Access to the audio version of the Webcast is available to members of HR.com. You can register for a free membership at www.hr.com.
DC: Conflict is a very important area, and it is a tough area. HR is the function meant to be most sensitive to the dynamics and how you can use conflict productively to move an organization forward, so I hope listeners take it to heart as being one of your core competencies.
HG: As an introduction, when you look at why this is really a key area, there are a few reasons. The first is that it is a complex world right now. It is a global matrix game with many of the companies that we work with. In many cases managers are trying to influence people they do not have authority over. Some of the organizations we work with are so complex that the organizational structures look like the back of a television set. A second aspect is that, when I think about all the companies we work with, the level of pressure to sustain a competitive advantage keeps increasing.
For HR to be seen as a player at that table, they need to be seen as the keeper of the culture, so they are required to become a role model. There is almost, in some cases, an unrealistic expectation of what the organization requires of HR, because they also expect them to be a change agent. So what it really says is this, "in a complex matrix world, you are constantly influencing people that you don't have authority over, and there is an expectation that to retain competitive advantage companies are going to move very quickly. HR is expected to be the role model, and in this sense the keeper of the processes to sustain best practices in the organization," and this is very difficult when you are also part of the game itself.
DC: And it is also difficult when you have a big set of administrative responsibilities, running a whole suite of processes from recruitment to training, to performance management and compensation. When we are having this kind of discussion I think as an HR leader: what should my job description look like? What should my goals look like for the coming year? I would really like to see some goals or some accountability around these kinds of issues you focus on. It's not just about making sure that training stays within budget.
HG: That is exactly right. In fact, one of the key things to think about - and this is always one of the challenges - is that, if you want to be seen as a player in an organization today, the expectation is that you are going to be challenging the status quo and moving the ball ahead. But the reality is that every time you do that you are going to increase the likelihood that you could be creating conflicts, so the expectation in today's world is that you can challenge the status quo, push things ahead, create conflicts, and still preserve the business relationship, but one of the things that you look at, when you are looking at HR, is HR needs to be seen as the model for what that level of behavioral best practice looks like, so it does require a higher level of EQ on the part of the HR folks.
In terms of the role of HR in conflict management, there are about five areas that HR is expected to handle. One is just to ensure that you have the right players on the senior team in terms of skill sets. The second is that from a behavioral standpoint, the senior team players have a picture of what good practice looks like, and you are able to hold up the mirror to the players in assessment mode. Every time we work with senior executive teams, in essence, the HR person is the process keeper, they are the custodians of the team-alignment process.
They have to be the principal person to support the senior executive to make sure that accountability is fostered, and, ultimately, they are the ones who really are the custodians of any effort.
When we do consulting work with senior teams the key for us is that we have a key client who wants to move things ahead; we have somebody in the HR role who has an ability to maintain the process; and the last point is that we have coachable players on the team itself.
DC: Would you say that most senior management teams recognize that they do need to have a custodian of team alignment? Is team alignment something that they are alert to as an issue?
HG: The answer is no, unfortunately. We try to enroll them because it serves us. I mean, sincerely, it is far better for us when we have an internal partner. When you try to do consulting work at the top from an organization development standpoint there is no question that an internal/external model is a much more productive model than if it was just us coming in isolation. But in many cases, the HR person inside has never really done the job to enroll the leader or the leadership team around what the breadth of their role could be, other than in many cases, as you said earlier, a transactional HR administrative role. So, they don't always have a three-dimensional picture of what HR can bring to the party.
DC: Do they recognize that they need help with team alignment at the top level? I would imagine that even the best companies would recognize that team alignment at the executive level is something that they continually have to work on.
HG: The answer is yes. Because of the competitive pressure and the complexity and the level of the mergers and organizational change, the reality is that, with a lot of the work we do, whether it be facilitating the Pfizer J&J consumer merger with some of the key teams or a Chico's women's retailer going from a $300 million to a $1.8 billion organization over three years, they really see the alignment work that we do as an accelerator. It's not like the old days of 20 years ago when they would bring you in because things were broken and they wanted you to fix it, now it's more where it's really a part of the way that they attempt to try to maintain a competitive advantage in the marketplace. So they see us as another offensive tool in their arsenal.
DC: It's great to be working on something offensive, instead of being a mechanic trying to fix something broken.
HG: That was the legacy of this field, back in the 70s and early 80s, but, again, it is a function of the world we live in today. So, if you look at part of what HR is responsible to do, which is ensuring the right capability set, the right skill set on the senior team, this really gets into the issue of what type of competency is required at the top. What are the skills of an effective executive? Ultimately, people get paid for three core competencies: technical skills; their ability to engage in the EQ area--the interpersonal or the leadership; and their conceptual or strategic skills. So, at the first line level of management you are really getting paid to be technically buttoned up and to be able to engage well with others.
By the time you get into senior executive ranks, you are really paid to be technically strong but the real game is can you lead? Can you coach? Can you enroll the players below you to follow you? And when you make a move conceptually/strategically, are you aware of the dominos that will fall? But the trickiest area is really that area in the middle, and in many cases the reason it's tricky is because most people tend to get promoted based on the success they have had at the level below and, unfortunately, as we all know, that is not necessarily a marker that they are going to succeed in the next span.
You could have someone who is very technically strong from an R&D standpoint, but that doesn't mean they can lead an R&D organization; or the expert salesperson who can't necessarily manage the sales organization; or the HR expert in staffing or compensation, who thinks they have the ability to play an enterprise leader role. It's very challenging to try to move into that grey zone, and that is really the discomfort zone for a lot of executives, and managing conflict is a key part of that.
Audience Question: We have been talking about the skills of an effective executive at the senior management level. What are the skills that a best-in-class HR professional has who is able to manage team conflict?
HG: The first is that you can influence people without positional authority. That means that the HR person has the capacity to go to the operations person, the salesperson, R&D, whatever it is, and quickly be able to get to the core issues. So when you look at core skill requirement, the first is they have a very good ability to listen and see things in a neutral light. The second is they have to able to assert, to be able to confront effectively and establish boundaries and again, from my experience, this many times is a difficult arena for many HR people to play in because many of them maintain a story called "I am HR, I am not line, so when the line has an issue it is not necessarily my patch to be able to deal with that unless it falls into the HR space."
The third is that they actually have conflict management skills. The last point is they are able to be authentic and deal with issues in real time, but, again, what you tend to find is that the HR person falls on the less assertive side of the spectrum.
DC: Can you give us an example of what an effective dialogue might sound like?
HG: The first point is being able to hold up a mirror, so the HR person would need to feel comfortable going to someone and saying, "Look, I have an observation, I have a concern that from a standpoint of the new product launch your players are not aligned or that the decision making is not clear and it is something we need to discuss around how we are going to be able to move that ahead." So the first step is the HR person has to be willing to put something on the table. Once they are able to facilitate putting something on the table then at that point the goal is to be able to land a plane and be able to hold that peer accountable to come up with some type of action.
The second is to clarify that person's take on the situation. The third is to confirm some options to consider and finally the last is to contract for observable actions to move the game ahead.
DC: I think that is an interesting example and a different role than HR people are used to thinking they ought to be playing.
HG: I think that's exactly right. Over the course of the month we align probably 10 to 15 teams a month, sometimes more than that. From an HR standpoint we do alignment work with companies from across the board. So what I am talking about isn't an industry issue; it's more an issue of the stories that HR people make up about what they are entitled to do.
DC: I know there are HR people who are sitting there saying, 'I am looking at what's going on here and there is something really wrong and I wish something would happen about it.' But they don't feel empowered to say anything. I think it is partly because HR people undervalue the fact that they have a special set of skills. If an engineer is looking at the jet engine and it doesn't seem right, they'll speak up because that's their expertise. If an HR person is looking at an organization and something doesn't seem right they should speak up too.
HG: That's true and the thing that is interesting about it is that from an inclusion standpoint, many times they are not brought into the conversation because the perception would be that they don't add content to it, they don't have technical expertise. In the end it becomes the process/change management competence, which is going to enable the HR people to be invited to the party. Whereas sometimes the HR people define the admission price as predicated solely on their business content knowledge, that's not the game they can win because they are never going to have the same level of content knowledge as the line executive.
Part of the HR person's role is to assess the conflict management behavior of people on the senior team. If you look at this in terms of the methods to deal with conflict, all of us have essentially four options. The first option is we can confront it head on. The second is we can let it go. The third is we can leave. And the last is we can play a victim role.
Let me explain what I mean. Let's say you are my boss, David, and I am not comfortable with my compensation. The first option would be to say, "David, I would like to sit down and have a conversation with you around comp. Are you open to that?"
The second is I say to myself, 'Well, my comp is not great, but I love my job and so I let it go. But letting it go literally means it's gone. I am not doing a psychodrama in my head anymore about it. The third option is I tell you that you and I need to have an amicable divorce and I leave cleanly. The fourth one is when I pretend I am OK but I am not. So, when you look at what is a functional play at the senior level, it really is to either confront, let it go, or if you can't get it done you leave. The playing victim is the part that is dysfunctional.
So, again, when you go back to the issue of an HR person, an HR person as the keeper of the process, as the steward of functionality in a sense for the senior team, they need to be the one who is constantly in a position to model what behavioral best practices would look like for the other players because the other players hang their hat many times on their business-content expertise. The HR person is the one person who by virtue of what their skill set should be able to look at the dynamics more than the other folks who tend to be very focused in terms of their patch.
DC: So HR people need to be aware of how valuable their insights are.
HG: That is exactly right, and the sharpest HR people, the most successful, have to be able to re-contract with their boss and the players around them to redefine that aspect of their role, that facilitation, holding up a mirror, creating a level and behavioral best practices for the rest of the team. Now that's a bit of a risk because you are putting yourself out there. But in the absence of that they are not adding as much value as they could and they will not be invited to the party because people will not perceive them as adding business content in the various arenas. This crosses business lines.
DC: What I see you are getting at is HR leaders should not be thinking "I wish I was in an organization where they treated me like this" but rather, if you want to be treated like this you have to be the one demonstrating 'this is how HR adds value.'
HG: Yes, that's right. Part of this is the skill set; to what degree an HR person is adept at understanding how to be an internal consultant.
In terms of getting our needs met in any organization we have three options. We do things in an aggressive fashion, in an assertive fashion, or a non-assertive fashion. It's important to define what this looks like because this has very interesting ramifications for HR.
If you are not assertive, that means there were issues there but you are not going to necessarily raise them, you are going to pretend that things are okay. You may resent it and inevitably one day there will be the proverbial straw that breaks the camel's back. You will go underground, eventually you will go off, you become aggressive, lose it, so to speak, then you will feel the sense of guilt because you are no longer looking good and in control so you go back into the closet again.
When someone is playing out non-assertive behavior, what you are basically saying is, "I have got needs and I am not going to convey to you what they are, but if you do not guess right I am going to get angry, although I am never going to let you know."
On the other side is aggressive behavior that basically says 'I have got needs and you don't' or 'I have got needs and so do you, but mine count more.'
In the middle is assertive, which is saying, 'I am going to let you know if you step on my toes.' At the high end of assertive is 'I am going to try to get my needs met, but I recognize it needs to be a partnering game because it's more of a win-win.'
Where would you think most HR executives are on this non-assertive to aggressive scale?
DC: Far left, non-assertive.
HG: There you go. That's the game, that's exactly right.
DC: It's always interesting to see how valuable these very simple conceptual models can be.
HG: Yes and here becomes the challenge. If you go back to the role of HR in conflict management, if you are trying to set up a high-performing team, the HR person is not going to be able to have the impact required if they are non-assertive. That's the issue.
Audience Question: As an HR person looking at this, this is a useful model for me when I am looking at managers around the organization, but how do I rate somebody without playing shrink?
HG: This is not a psychotherapeutic model; it's a behavioral model. And if you think about it very simplistically it is just observing behaviors, nothing more than that. Individuals that are aggressive tend to push their own agendas, and they don't have a strong willingness to partner. Non-assertive people try to play more of a nice guy game where they do not want to offend, they want to preserve business relationships. But they are not strong; they play it safe. Assertive people in the middle protect their boundary and they push. So this isn't trying to figure a person out psychotherapeutically or what their Myers-Briggs is, it's nothing about that. It's simply on a behavior level.
If you want to have a team that is functional, people need to play in the assertive space. The further people go to the aggressive or the non-assertive side, the more they inadvertently begin to dismantle the functionality of that senior executive team. And you can pick that up by observation. If you think about being in any type of staff meeting, it becomes very clear when the aggressive people are talking over one another or shutting people down. It becomes very clear when certain people are playing it safe and remaining silent. So this isn't something where one has to have a Ph.D. in psychology to be able to pick up the vibes.
When we go into most organizations, and the goal is to align the organization, there are three types ofplayers you tend to see right off the bat. You see the risk takers, who are the players, and that's good. You see the vacationers who play it safe. And then there are the prisoners who are the people who are like the unhappy vacationers. They don't necessarily like the way the game is getting played but they don't do anything about it. They complain. They go underground. So part of the HR role is to support the organization hiring and sustaining talent at the top, at the player level. Vacationers do not add a hell of a lot of value and the prisoners ultimately are toxic.
So when you talk about creating a high-performing team and the HR person's role, this gets into a very interesting definition of what the HR person feels they're accountable for. What we are talking about here is an active keeper of the culture looking at the talent, as something that they need to create and sustain. So in the end, the HR person needs to model risk-taking behavior to really add the value required, particularly from a conflict management standpoint.
DC: And it is interesting that risk-taking behavior is both what they need to do to be successful, and it's also one of the behaviors they have to model in order to fulfill their role in the organization.
HG: That's exactly right. Now let me give you an example of where HR folks tend to fall into a trap. Let's say there is an issue between two people. One person sees himself as being the victim, and usually you would think that somebody is doing it to them so that other person is the persecutor. Usually, the third person in the play is the rescuer.
In dysfunctional conflict resolution, what happens is that the person who feels victimized goes to a third party, who may be the boss. The boss may then go and have a conversation with the persecutor, and suddenly all the roles get changed. Now instead of it being a business issue between two people, it becomes a trust issue, and the person whom the boss talked to feels like they are being victimized.
Where HR comes in is that many times someone will go to HR to complain. HR does not necessarily hold the individual accountable to handle the issue. So under the guise of confidentiality or wanting to provide a good ear or a shoulder to lean on, HR may be inadvertently sabotaging conflict resolution in that they do not press and coach the individual to handle the initial presenting issue.
Many times you will see HR people fall into that trap under the guise called "I want people to feel comfortable to come and see me." That is the provocative area and gets right to the core of what does an HR person see as their accountability.
DC: It is very natural when somebody comes to you for help to want to be able to rescue them as opposed to saying, "Ok now I have listened to you, go back and solve it."
HG: Yes, you need to say, "OK now given this situation, what options have you considered? What would you see as the next step? What would closure look like?" What you are now doing is starting to move from good listener to playing coach, to move that person to action. When that does not happen then the problem is that the HR person falls in the trap of being the internal social worker and they are not getting paid to do that. They are paid to move the enterprise ahead.
Part of being the custodian of team alignment is that we have to define what alignment looks like. There are five factors that really determine alignment:
• Goals / business priorities / focus
• Individual roles / accountabilities
• Protocols / rules of engagement
• Business relationships / mutual expectations
When you think about your department or you think of the company, it's these five factors that really determine alignment. They also represent the core reason why conflicts emerge in an organization. If you and I do not agree on the strategy, or our priorities, David, we will inadvertently compete for resources. If we don't agree on accountabilities, I am going to have an expectation you are going to do something, but you don't see this as your job.
Let me define protocols / rules of engagement. These are not values, these are not things like 'respect one another' or 'value diversity.' Rules of engagement would be when issues come up, how do we escalate them? How do we force closure? When are decisions made? When are they consultative? When are they unilateral?
And then finally with the business relationships, this is where we are interdependent or not. So when you think about an aligned team or an aligned organization, these five factors ultimately lie at the very bedrock of trying to create sustainability with the senior executive team.
DC: And based on what I have seen in your book, the whole business of rules of engagement is avery rich area for HR people to become familiar with.
HG: Yes, you are right, and what's interesting if you look at this model is if you think about the role of HR people as process keepers, most companies inevitably expend most of their energy at the top of this house. They think that if we focus on the strategy, which obviously you have to, and we can get tighter around the business priorities, then ultimately things should work out. The issue of rules of engagement is like a phantom zone or a twilight zone, because the problem is that if you don't have rules of engagement regarding conflict resolution or decision making or interdependencies, etc., then people just make up their own rules and it's impossible to create functional sustainability within the organization.
This is a great example of where a switched on HR person would be conscious of that and coach the leader and/or the senior team that the job isn't necessarily done just by ensuring that they have a strategic direction.
DC: Yes and the point that keeps coming up is that leaders aren't necessarily going to know how team alignment works. They have got some gut feel for it, but they just won't have thought it through the way Howard has.
HG: The next point is around the HR person's role to help the leader redefine what accountability looks like. The first level of accountability is when the player is accountable for themselves, which is obviously a good thing. Somebody takes personal accountability for whatever they do. The second level up is when you are accountable for your direct reports. In other words if the direct reports do well or don't do well they own that as the leader.
The third level is a high-performing team where you are accountable for your peers. The fourth level is you are accountable for your leader's success. The last level is the level of your accountability for the organization at large. So when you are looking at this whole issue of working in a high performance organization, what you are trying to do is redefine people's notions of what they are accountable for.
In a classic siloed organization, people are accountable for themselves and their direct reports. If you want to play a bigger game, then we are talking about level 3 and up. But you can see that is a much different game when you do that. Because if you ask most people what is the most challenging aspect to play in a more horizontal or best practice mode, they would always say it is about peers holding peers accountable.
The next area is making sure that the senior team is working as a high-performing team. Here are the eight attributes of a high-performing team. The first is that the people are clear regarding the missions, the goals and the priorities—which is pretty straightforward. The second issue: have the right players. The third attribute is that they have the ability to understand, here is my role, here is yours, this is where we intersect horizontally. The fourth one, which is a big idea, is that we are committed to what it will take to move the business ahead versus our functional or parochial self interest. So when we get into conversations about headcount, resource allocation etc., we look at it like a business case versus we are playing a game of 'win as much as you can poker.' The fifth attribute is that decision making is clear, when is it unilateral, when is consultative, when is it consensus?
The next attribute, and this to me is the big one, is the team working as a managing board of directors. This is where it basically says that it is a fair game to deal with any issue that impacts the business. If I am in HR and I have a concern about what's going on in operations, I have the conversation. If I am in R&D, I have a concern about sales, I have the conversation, but that implies #7--that people can depersonalize conflict. In the 8th, the team periodically self-assesses. So, this is a horizontal or a high-performing team.
Now, how do you ultimately get there? There are some predictable stages that every team or organizationgoes to get to this horizontal team. The first stage is in going from hierarchical to horizontal. The first stage is a testing phase where the issues are under wraps. People do not want to talk about them because it is not permissible. Stage two, we put them on the table, but there could be some discomfort in the air, because they had previously been underground.
The third stage is when you start to tighten up a new game. Protocols, rules of engagement will be talked about. And then finally you have this high-performing team.
When you look at the role of the HR person, the goal is to help the leader accelerate that senior team to high performance, but that implies being able to help, getting those issues on the table as rapidly as you can and doing it in a neutral light.
What is the HR skill mix to be able to do this role of conflict resolution? You have to be a good internal consultant, you have to be able to coach people, you have to facilitate the team, be able to hold up a mirror, you have to have good skills, and you have to model the behaviors. That is really what we are talking about. So, in looking at the HR person's role from a conflict management standpoint, we will take them from being seen as an HR transactional administrator to taking them into being an extremely value-added important player on this team.
DC: It is exciting to hear those characteristics of a high-performing team because then you can really start to picture it. At the same time, we see how far most teams are from that kind of behavior.
HG: Yes, and I would tell you that in the companies that we have worked in, and again we are talking about primarily Fortune 100 or 500, the reality is that you have got to work horizontally. You cannot work as a hub-and-spoke. The bigger a company gets the more impossible it is to have a little star chamber at the top. So, one way or the other, companies are moving down this road.


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Home » World News » Scientists call for pause on AstraZeneca vaccine rollout
Scientists call for pause on AstraZeneca vaccine rollout
mediabest 01/12/2021 AcallForonpausescientists
The Australian and New Zealand Society for Immunology says the federal government should immediately pause the planned rollout of the AstraZeneca vaccine because it may not be effective enough to generate herd immunity.
Phase three clinical trials of the vaccine, which is the centrepiece of Australia's vaccination strategy, show it is only 62 per cent effective in preventing COVID-19 when given in the recommended dose. Trials suggest vaccines from Pfizer and Moderna are about 95 per cent effective.
Ian Cormack prepares to receive a dose of AstraZeneca's vaccine in Edinburgh, Scotland, this month. Credit:Getty Images
Immunology society president Professor Stephen Turner said based on current trial evidence the AstraZeneca vaccine should not be widely rolled out.
"The AstraZeneca vaccine is not one I would be deploying widely, because of that lower efficacy," he said. "You cannot rely on it to establish herd immunity."
"Given we have fantastic vaccines against this, I think it would be wise to not rely on the AstraZeneca vaccine for controlling the virus in Australia. But it could be used as a tool to blunt the effect of COVID until those vaccines could be deployed."
Professor Stephen Turner, president of the Australian and New Zealand Society for Immunology. Credit:Joe Armao
Speaking before the virology society's position changed, vice-president and spokeswoman Professor Heidi Drummer said 62 per cent effectiveness was not enough to achieve herd immunity.
"We should wait to see what data AstraZeneca provides to demonstrate higher levels of efficacy can be achieved with their vaccine and opt to use vaccines that achieve the highest level of efficacy to achieve herd immunity, whichever they are," she said.
Professor Heidi Drummer, vice-president of and spokeswoman for the Australasian Virology Society.
"If it is 62 per cent efficacy, and that comes out in their further trials, I think there is a really good argument to make for the federal government to invest in the Pfizer and Moderna vaccines and use that in the wider community."
Professor Drummer later said she "had to stand by what the president says".
"I don't oppose the rollout of the vaccine provided we can see more data that shows that 90 per cent efficacy can be achieved," she said.
Herd immunity means that such a large portion of the population is immune that a virus can no longer circulate and is eliminated.
Associate Professor James Wood, a University of NSW vaccine modeller and member of the federal government's Australian Technical Advisory Group on Immunisation, said herd immunity was "the long game", and providing a vaccine that could protect life immediately was much more important, particularly given Australia had so far only bought enough Pfizer vaccine for 5 million people.
Phase three trial results from the AstraZeneca vaccine published in The Lancet this month show the vaccine was 62.1 per cent effective at preventing disease when given in standard doses.
However, in a small group of volunteers who accidentally received a lower dose, that rate rose to 90 per cent, creating significant uncertainty over the vaccine's true effectiveness. Not a single person given proper doses of the vaccine developed severe COVID-19.
Vaccines from Moderna and Pfizer have achieved greater than 90 per cent effectiveness, probably high enough to force the virus out of circulation if widely administered, Professor Drummer said.
The Australian government has secured 53.8 million doses of the AstraZeneca vaccine, with two injections required per person.
It remains unproven if any of the vaccines can prevent transmission of coronavirus.
Professor Drummer said clinical data suggested the Pfizer and Moderna vaccines were far more likely to do so than AstraZeneca's, but more studies needed to be conducted.
Dr Andrew Miller, president of the Australian Medical Association's Western Australia branch, said a vaccine that did not provide herd immunity would not meet the public's expectations about returning to pre-COVID normal.
"We need it to be over 70 per cent effective – preferably over 80 per cent – to prevent significant transmission still continuing in the community," he said.
"We've got plenty of money, and we have got plenty of time because we have very good disease control. Why would we settle for the second-best option?"
Professor Julian Rait, president of the AMA's Victorian branch, said judgment on the AstraZeneca vaccine should be reserved until more data was available.
Burnet Institute director Professor Brendan Crabb said the federal government did a good job with limited information in choosing its vaccine candidates, but it should now review Australia's reliance on the AstraZeneca vaccine.
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He said while AstraZeneca still might prove more effective in reality than clinical trials have suggested, at this stage the Pfizer and Moderna vaccines were looking significantly better at stopping people from getting COVID-19.
"Personally, I'd much rather have AstraZeneca than nothing. I would have it tomorrow. It is a highly safe and effective vaccine," he said.
"But would I have a more effective one if I could? Yes I would."
Health Minister Greg Hunt denied on Tuesday the suggestion that administering AstraZeneca's vaccine essentially meant the government was giving up on herd immunity.
"We are listening to the Australian government medical experts. They are the ones choosing the vaccines. They have chosen on the basis an mRNA vaccine has never ever been done before for anything.
"There is a very strong history of different types of conventional vaccine being rolled out with high effectiveness. It's important to note that the results also show up to 90 per cent effectiveness more generally, with final results to come. And up to 100 per cent in relation to severe illness."
A spokesman for the Health Department said people "should be cautious around making assessments of its efficacy until the full data is released and the [Therapeutic Goods Administration] approval process has been conducted".
The government confirmed last year that people would be allowed to purchase vaccines privately.
A spokeswoman for AstraZeneca said the company had always maintained that various vaccines would be needed to combat COVID-19.
"More data will continue to accumulate, refining the efficacy reading and characterising vaccine efficacy over a longer period of time."
The number of people who need to be vaccinated to achieve herd immunity is known as the "critical vaccination level". It is calculated based on how effectively a virus is transmitting in a population.
The more infectious the virus, the higher the critical level. But our behaviour also plays a role. In places with better social distancing, we are already making it harder for the virus to spread, lowering the critical level.
Evidence suggests on average around the world every person infected with COVID-19 will go on to infect between two and three other people.
To achieve herd immunity and end the pandemic, vaccines need to lower that average to below one – so that, over time, the virus dies out.
This may have been achievable in Australia using AstraZeneca's vaccine, Professor Wood said.
However, the emergence of at least one more-transmissible variant of COVID-19 in Britain changed that picture and raised the critical vaccination level, probably to a level beyond that which AstraZeneca could achieve.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Dry July. View from a Doctor's Desk – Dr Lisa Surman
August 19, 2021 July 2, 2018 by Harmony Sanderson
A month of sobriety every year does not reduce your lifetime risk of harm from alcohol, but does act as a circuit-breaker to help reassess your relationship with regular drinking. You may get to assess the effects it may be having on productivity, relationships and review the amount and circumstances you are drinking under (for example the automatic pouring of a larger glass of wine while cooking dinner, or sitting down to watch the television). Research has repeatedly demonstrated that we underestimate how much we drink. A break may also highlight the social pressures we are under to consume alcohol. There has been little research into whether campaigns such as Dry July have any effect on long-lasting change in alcohol consumption, but there is some evidence from the UK of reduced consumption in the 1-2 months following participating in these campaigns.
The short-term benefits from a month alcohol-free for regular drinkers include a more refreshing sleep. Although alcohol acts as a sedative, it is associated with increased snoring, sleep apnoea and wakening after the effect wears off at 4-6 hours. Alcohol acts as a diuretic and this effect may also interrupt sleep.
Alcohol contains significant calories and a break can be associated with weight loss and reduced restaurant bills. Although often without physical symptoms, your liver function also improves when not processing regular alcohol.
In Australia, almost 6000 deaths a year can be attributed to alcohol, with approximately 400 hospitalisations per day. Many people remain unaware that the long-term health risks associated with regular alcohol intake are not only confined to very heavy drinkers. There is a growing list of alcohol-related diseases: bowel cancer, mouth and oesophageal cancer, breast cancer, heart disease, respiratory infections, mental health problems and perhaps prostate cancer. There is also the foetal alcohol spectrum disorders occurring as a result of exposure to alcohol in the womb. Alcohol affects judgement and affects risk-taking, with injuries, vehicle accidents, drownings and violence associated.
Individual vulnerability and the context in which you drink are relevant, but in general low-risk drinking to avoid alcohol-related death is two standard drinks or less per day and a single occasion low-risk drinking is four standard drinks or less. The safest option for those thinking about pregnancy, who are pregnant and for those under 18 years old is not to drink.
The National Alcohol and Other Drug Hotline offers professional, confidential and free advice to connect you to the most appropriate services locally at 1800 250 0115 (anytime and anywhere) should you be concerned about your level of alcohol use.
Daybreak is a helpful, free App designed around a Drink Less Support Community created by the Hello Sunday Morning team, see hellosundaymorning.org for more information about the App and Facebook group promoting hang-over free Sundays, for those interested in a longer term approach to changing their relationship with alcohol.
Funds raised though Dry July are donated to cancer support organisations across Australia, with $30 million being raised for more than 75 cancer organisations since the first Dry July in 2008.
Dr Lisa Surman, CBD West Medical Centre, Perth, WA
Member of Best Practice Software's Clinical Leadership Advisory Committee
"Often patients spend time talking about current medical and social issues, taking valuable time away from dealing with what they have really come in to discuss. One of our solutions is to direct them to news articles on our website written by a doctor in our Practice that outline current issues and offer strategies to manage the problem and links to relevant, reputable websites".
Categories All Posts, Healthcare Industry Post navigation
New software function for Australian Defence Force Post-discharge General Practitioner Health Assessment launched
Is Breast Best? A Look at Breast Feeding by Dr Lisa Surman


A lovely villa, which is in a plot of 4,130 square meters and has wonderful sea views, is for sale in Skala, in Kefalonia, in Greece. The main house of this exquisite property is 248 sq.m and consists of the following: On the upper floor, there is the main entrance, a living room with a hot air fireplace which heats the rooms, sat TV, kitchen, dining room, foyer toilette, a 40 sq. m veranda with a tile roof, a main bedroom with air condition and own bathroom and an office space. The furnishings are all vintage as all lights and accessories are made of Murano glass.
From an inside stair, you can reach the lower floor which has two guest bedrooms, two bathrooms, a laundry room with a washing machine and a huge 40 sq. m garage.The Guesthouse is 90 sq.m, on two floors and its terrace is 78 sq.m. There are two bedrooms with 4 beds and a fully equipped modern kitchen. On the upper floor, there is a 67 sq. m terrace with a 24 sq. m wooden gazebo covered with tiles, surrounded by full glass windows 360° with three sliding doors measuring about 2,60 meters each. It has two outside "Pergolas" 13 sq. m each with white hemp awnings providing shade. There is also a dining table, chairs, an outside breakfast table and a SAT-TV.
On the lower floor, there are two large bedrooms with two beds (with custom made mattresses), air conditioning, two large toilettes with hot-cold shower, WC and sink, chest of drawers with a Verona Marble top.
The Villa, which enjoys a 110 sq.m pool, is surrounded by a Mediterranean garden with Palm trees, Jasmine with their perfumed white flowers, Hibiscus, white and pink Daphne and more than 90 Leyland and Cypresses. Birds, Swallows, Hawks and Owls provide the "musical background". The sandy beach is just 80 meters walk down from the villa (the villa is 32 meters above sea level). It is a 5-minute car ride to the famous Skala beach where there are taverns and restaurants and it is nearby Kaminia beach which is 3km long that is perfect for walking barefoot on the sand.
The agency ENOS PROPERTIES Real Estate Services in Kefalonia, has this property for sale priced at 1.075.000 €.


text:
Mechanical Properties of Flux-Cored Wire Welds in C-Mn Steel
Study of the Mechanical Properties of Flux-Cored Wire Welds in C-Mn Steels
Study of the Mechanical Properties of Flux-Cored Wire Welds in C-Mn Steels [pdf / 1.36MB] Member Report 241/1984
By R L Jones and H G Pisarski
The mechanical properties of butt welds in C-Mn steel test panels prepared with a range of cored wires have been established using arc welding procedures which reflect typical fabrication practice. Weld metal mechanical properties were assessed by tensile tests, Charpy V tests and, for selected welds, CTOD tests. A comparison with the relevant AWS code showed that the majority of consumables tested met the specification requirements in terms of yield and tensile strength, but not always in terms of Charpy V toughness. A range of weld metal toughness values was measured depending on the consumable and the welding procedure used. Good low temperature Charpy V and CTOD weld metal toughness could be achieved wiih both gas-shielded and self-shielded wires, albeit with careful control of weld procedure. The use of flux-cored wires for applications with critical mechanical property requirements, e.g. fabrication of pressure vessels and offshore structures, has been considered. Correlations between the Charpy V and CTOD toughness data have also been explored.


Honey Nut Cheerios is a top performing cereal brand for General Mills. However, in an increasingly competitive environment and changing consumer attitudes towards cereal in general, the brand was at risk of losing significant ground.
To reach millennials, the brand needed to find a way to show that it cares as much about the collective world as it does about selling cereal. The rapidly declining honey bee population, which is responsible for the pollination of one third of fruits and vegetables, became the cause in which the brand decided to position itself around.
As iconic as Honey Nut Cheerios' "Buzz the Bee" is, the brand decided he needed to disappear to make people feel the impact of a declining bee population. So Cossette removed Buzz from the cereal box, drawing attention to an important cause as well as to the Cheerios brand.
However, it wasn't enough to raise the issue without providing a constructive solution. So the brand invited consumers to #BringBackTheBees by registering online to have a wildflower seed pack (which also included a coupon for the cereal and a lawn sign) sent to their house. They could then plant the seeds to provide a food source for the honeybees.
Several children created videos discussing the campaign while sometimes eating cereal. Adults also posted pictures of their cereal boxes, seed packs and flower gardens on Instagram.
Baseline sales increased 11.8% and the brand exceeded the initial goal of 35 million seeds in the first week, distributing a total of 115 million seeds. And the video garnered 10 million views online.


text:
Poll: Three-quarters of all Americans would prefer to live anywhere but a big city
n overwhelming majority of Americans would prefer to live anywhere but a big city, according to a new Just the News Daily Poll with Scott Rasmussen.
A total of 73% of poll respondents said they would prefer to live in either a suburban area or a rural setting, rather than a large city or urban area. Only one out of every five Americans – 21% – said they would like to live in a city. Just 5% said they were "not sure" which they would prefer.
The number of respondents in favor of suburban and rural living follows several months of sustained and often violent social justice protests in dense city centers. New York, the country's most densely populated city, was among the hardest hit by the coronavirus at the start of the pandemic.
The national survey of 1,200 registered voters was conducted Jul. 30 – Aug. 1, 2020 by Rasmussen, a polling veteran. The margin of sampling error is +/- 2.8% for full sample.
To see the full demographic cross-tabulations for this polling question, click here.
To see the methodology and sample demographics for this polling question, click here.
By Daniel Payne
READ MORE ON JUST THE NEWS
News Aggregate Contributor
KY Stargazzer
Certified news nut hobby songwriter/poet, writer, & citizen journalist. From the Bluegrass State of Kentucky. Home of the Kentucky Derby and best bourbon in the world. I enjoy anything astronomy, space program, into all sports, and all forms of music. I enjoy people, humor, and good conversation. And I support Donald Trump and make no excuses for it!
KY stargazer


این مقاله تقریباً شامل 5115 کلمه می باشد.
This paper aims to study the stability issue in a Cournot duopoly with codetermined firms. We show that when both firms codetermine employment together with decentralised employees' representatives, a rise in wages acts as an economic (de)stabiliser when the wage is fairly (high) low, while under profit maximisation a rise in wages always acts as a stabilising device because the parametric stability region monotonically increases with the wage in such a case. Moreover, a rise in the union's bargaining power has a de-stabilising effect, except when the wage is low and the firm power is already high. Therefore, under codetermination a change either in the wage or firm power in the Nash bargaining plays an ambiguous role on stability. We also show with numerical simulations that complex dynamics can also occur.
این مقاله شامل 5115 کلمه می باشد.


Union members and consumer groups, including the state Public Interest Research Group, are challenging Gov. Andrew Cuomo's plans to shut down the New York State Physician Profile website to save $1.2 million.
The governor has proposed the complete elimination of the physician profiles program — both its website and phone assistance, NYPIRG's legislative director Blair Horner said in an email. "We're lobbying the legislature to restore the cut, but we won't know what they want to do until they put together their budget resolutions," he said.
"Our view is that the governor's proposal is indefensible and makes it harder — and in some ways virtually impossible — for consumers to get important health information," said Horner.
The executive budget proposal says that information is available through private sources, and the Department of Health doesn't need to provide it.
" … (M)uch of this information is duplicative of data currently available through other publicly available sources, such as WebMD," according to an Article VII budget bill (see Section A, Page 9 here).


text:
Q: java ScheduledExecutorService giving weird results Code:
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;

public class Test implements Runnable {

    private Integer xyz = 1;

    public static void main(String[] args) throws InterruptedException {
        ScheduledExecutorService executorService = Executors.newScheduledThreadPool(2);

        Test test = new Test();
        test.xyz = 20;

        Test test2 = new Test();

        System.out.println("Values of xyz = " + test.xyz + " , " + test2.xyz);

        executorService.scheduleWithFixedDelay(test, 100, 1000, TimeUnit.MILLISECONDS);
        executorService.scheduleWithFixedDelay(test2, 100, 100, TimeUnit.MILLISECONDS);
        executorService.awaitTermination(3000, TimeUnit.MILLISECONDS);
        executorService.shutdown();
    }

    @Override
    public void run() {
        this.xyz += (int) Thread.currentThread().getId();
        System.out.println(Thread.currentThread().getId() + " " + this.xyz);
    }
}

Output:
Values of xyz =
20 1  |
10 30 |
11 12 |
11 23 |
11 34 |
11 45 |
11 56 |
11 67 |
11 78 |
11 89 |
11 100 |
11 111 |
10 40 |
11 122 |
11 133 |
11 144 |
11 155 |
11 166 |
11 177 |
11 188 |
11 199 |
11 210 |
11 221 |
10 50 |
11 232 |
11 243 |
11 254 |
11 265 |
10 275 |   (10 should have been 60 here)
10 285 |
10 295 |
10 305 |
10 315 |

A: ScheduledExecutorService uses thread allocated to run the submitted task at the rate specified. The misunderstanding was that each thread is associated with 1 task and is only used to run that particular task at the frequency specified. But that is not the case, so it makes sense.

redpajama_set_name: RedPajamaStackExchange


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We have 7 offices in located in Queensland, NSW and NT.
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text:
\section{Introduction}
Today social media has become popular among people worldwide as their primary source of news. For example, about half of U.S. adults get news from social media \cite{shearer2021news}.
Moreover, online social media is becoming the main platform for people to exchange information, opinions, and experiences about different events \cite{matsa2018news}.
These platforms have significantly helped the free speech, political and social awareness (e.g., the \#metoo movement \cite{xiong2019hashtag}), and raising the voice of minorities and oppressed groups  (e.g., the Black Lives Matter movement) \cite{reuter2015online}. Despite all the benefits, social media can be harmful as well. One of the harms of social media is the spread of misinformation.\\
Misinformation and its propagation are not limited to the modern information age and social media.
A famous example is the misinformation campaign launched in ancient Rome by Caesar Augustus (Octavian), the first Roman emperor, against his opposition, Roman politician, and general, Mark Antony. Specifically, Octavian used slogans etched onto coins to smear the reputation of Antony \cite{kaminska2017module}.\\
In the information age, the lack of proper monitoring and fact-checking and capability of the spread of news by bots \cite{shao2017spread} have turned social media into a key conduit for fake{} news. \\
The spread of misinformation on these platforms can significantly affect different aspects of our lives.
For instance, some believe that the spread of targeted misinformation has contributed to the 2016 U.S. presidential election \cite{allcott2017social}.
Some research also indicates that exposure to fake{} news can cause attitudes of inefficacy and cynicism toward certain political candidates \cite{balmas,allcott2017social}. The influence of misinformation is not limited to Politics. Another study shows that 1225 fake news stories were spread during COVID-19, with half of them coming from social media, putting public health in danger \cite{naeem2021exploration}.
\\
This significant effect of misinformation on society has motivated extensive research on fake{} news, especially on Twitter as a major social media. Some of these studies focus on the statistical behavior of fake{} news. A recent study by Vosoughi et al. shows that diffusion parameters of fake{} news are different from real{} news on Twitter. They showed that ``falsehood'' diffuses deeper and faster \cite{vosoughi2018spread}. In another study, fake{} and real{} news propagate different diffusion topologies on social platforms such as Weibo and Twitter \cite{zhao2020fake}.\\
In parallel, some efforts focus on the detection of fake{} news. There were about 137 active fact-checking projects in the world in 2018 \cite{pavleska2018performance}.
For instance, there are platforms such as PolitiFact \footnote{https://www.politifact.com/}, which uses editors to determine news veracity. Unfortunately, manual fact-checking and fake{} news detection is no match for a large amount of misinformation that is spread on a daily basis, and despite all these efforts in detecting fake{} news, there is a lot of fake{} news which is not detected.
Automatic fake{} news detection provides an effective alternative solution that reduces the human cost for detection and prevents the spread of fake{} news.\\
Various models have been developed to automate the process of fake{} news detection. Each of these models can accurately detect fake{} news among a specific tweet category. We define this characteristic of models as the \emph{effective zone} of the model. An effective zone might contain political tweets, tweets about a famous hurricane, or tweets by Twitter bots. Also, each model uses features such as the text of the tweet, the user who tweets it, tweet characteristics (e.g., number of retweets), and diffusion topologies to detect whether it is fake{} or not. We call this characteristic of a model the \emph{features} of the model. Finally, the \emph{ensemble} is the data that we use to train and test our model, which can characterize the effective zone of the model. Table \ref{table:others_examples} shows some examples of such studies.

\begin{table*
\centering
{
\renewcommand{\arraystretch}{1.0}
\begin{tabular}
{|>{\centering\arraybackslash}m{.15\linewidth}|>{\centering\arraybackslash}m{.3\linewidth}|>{\centering\arraybackslash}m{.15\linewidth}|>{\centering\arraybackslash}m{.4\linewidth}|}
\hline
\multicolumn{4}{|c|}{Related Work Description} \\
\hline \centering
Article& Ensemble & Features & Effective Zone \\
\hline \centering
 Kwon et al. & 102 news topics, each containing at least 60 tweets and their diffusion networks&  Characteristics of diffusion network& The collection of tweets that the diffusion network is available for them\\
\hline \centering
 Helmstetter et al. &  Tweets from a trustworthy source labeled as real news, and tweets from an untrustworthy source labeled as fake news &  User, text, and tweet features & Fake news tweeted from users who almost always tweet fake\\
\hline \centering
Buntain et al. & CREDBANK and PHEME datasets & Tweet, characteristics of diffusion network, user, text features & when we have the structure of tweets, many tweets with the same topic, and users who spread only fake or real most of the time.\\
\hline \centering
Agarwal et al.,2019 & 12.8 K manually labeled short statements & Text features & Since most of the tweets are political, works better in political tweets, but otherwise can be used for other ensembles
\\ \hline \rowcolor{lightgray} \centering
Our Approach & 4k random tweets & Text features & Topics which are mentioned in reliable sources
\\ \hline
\end{tabular}
}
\caption{Related works comparison: This table shows the kind of tweets different papers have focused on their verification and features they used to study them. }
\label{table:others_examples}
\end{table*}

Papers such as Kwon et al \cite{kwon2013prominent} study diffusion network of tweets in order to identify rumors. They use temporal, structural, and linguistic characteristics of diffusion as features of the model on 102 news topics, each containing at least 60 tweets as their ensemble. This model's effective zone is the collection of tweets that the diffusion network is available for them. An issue for using network features in detecting fake{} news is that repeating false information, even in a fact-checking context, may increase an individual's likelihood of accepting it as true \cite{ecker2017reminders}.
Therefore, it is crucial to detect fake{} news as soon as possible, which is not always possible in studying the diffusion network of news.\\
Another feature to use in studying fake{} news is the information we have on the user who tweeted it, the number of followers, favorites, and Twitter handle, to mention a few. For instance, \cite{helmstetter2018weakly} ensemble contains tweets from a trustworthy source labeled as real{} news and tweet from an untrustworthy source labeled as fake{} news. This model uses features such as user, tweet, topic, and sentiment, and its effective zone is detecting fake{} news tweeted from users who almost always tweet fake{}, such as rumor bots. As another example, Buntain et al. \cite{buntain2017automatically} uses structural, user, and content features of tweets to detect fake{} news. In order for this model to be effective, we should have the structure of tweets, many tweets with the same topic, and users should spread only fake{} or real{} most of the time. Using user features, although applicable to detect fake{} news bots, might lead to a bias through certain users and disables us to predict fake{} news among the tweets of users who often tweet real{} news.\\
Among other features, extracting features from news' text using NLP methods can help us find a solution for the fake{} news detection problem. Many previous studies on tweet texts focus on detecting the veracity of tweets on popular threads on Twitter \cite{qazvinian2011rumor}. These models benefit from an abundance of labeled tweets (as fake{} or real{}) in the case of popular threads. Despite their good results in the case of popular threads, these models might not be as useful in the case of news content that has not been tweeted as much. Most relevant to our study is the 2019 VasuAgarwal et al. \cite{agarwal2019analysis}. This study uses a set of political tweets as the ensemble to train a fake{} news detector based on features extracted from the tweet's text. This set is diverse in terms of political news content, which makes this model effective for detecting fake{} news among both popular and unpopular political threads.\\
In this paper, we present a method for automating fake{} news detection on Twitter. Our approach leads to a model which can also predict the veracity of unpopular threads in any given subject accurately.
Our approach is based on human behavior when faced with new information or news.
People usually search for reliable news agencies or fact-checking websites to cross-check news and to see if it is real{} or not.\\
Our idea is to automate this process using NLP techniques to build a machine learning model. The objective of the model is to cross-check the content of a given tweet with reliable sources{}   and make a prediction about the veracity of the tweet.
Thus, in addition, we need to prepare a group of reliable sources{} in order to cross-check the tweets with it. In other words, our dataset consists of two main data; one is target tweet{}s, which should be labeled for the machine learning part, and the other part contains reliable tweets that are needed in cross-checking target tweet{} with them. We select our reliable sources{} from news agencies located in the green zone of the "media bias chart"\footnote{https://www.adfontesmedia.com/static-mbc/}. This includes Reuters, BBC, AP, etc. The green box in this chart shows the most reliable  news agencies. In order to compare a target tweet{} with trustable sources, we collect tweets from the chosen reliable sources. We collect tweets published in the same time interval as the target tweet{}. We call this dataset the reliable dataset.\\
This paper is organized as follows. The section Methods presents the model, the feature extraction techniques, and the entire pipeline. In the Results, we evaluate the performance of our model. The Discussion and Conclusion section summarizes our work and discusses some aspects of the work that can be improved in future studies.

\section{Method and Data}

Figure \ref{fig:flowchart}-\rom{1} shows our full pipeline for the classification of a target tweet{} as fake{} or real{}. We first will briefly discuss the pipeline as illustrated in figure \ref{fig:flowchart}.

\begin{figure*
\centering
\includegraphics[scale=0.34]{Figure1.pdf}
\caption{This diagram shows the different parts of our model to process the tweets and find out their veracity. The first step is gathering data from reliable sources{}. After encoding tweets' texts into vectors, we find the cluster of stories associated with the target tweet{}, $\mathbb{C}^{*}$. The final step is to cross-check the target tweet{} with reliable tweets in $\mathbb{C}^{*}$. The data is represented by blue circles, pre-processing by a yellow diamond, and green rectangles are the symbol of models.  }
\label{fig:flowchart}
\end{figure*}

We assume that we are given a tweet, and our task is to check its veracity. This could be a new tweet that shows up on Twitter. We refer to this as the ``target tweet{}.''
We also assume that we have access to reliable sources of information that we can use as reference and cross-check the target tweet{} against these sources. We refer to these sources as ``reliable sources{}.'' For our work, we use tweets from Twitter accounts of well-established news agencies, such as Reuters, BBC, Associated Press, etc., for the reliable sources.

We collect all tweets from reliable sources{} in the three days leading to the target tweet{} and the three days following it (figure \ref{fig:flowchart}-\rom{2}). For example, for the tweet ``No marines were killed in the Kabul airport attack, they were just injured.'' which was posted on 26th of August 2021, we collect tweets from news agencies in the time interval between 23rd of August 2021 and 29th of August 2021. We refer to this collection as `` $\mathcal{C}$'' of tweets. We need a mathematical representation of texts of tweets, for which we use TF-IDF encoding \cite{ullman2011mining} (figure \ref{fig:flowchart}).
\\

Set $\mathcal{C}$ comprises many different news stories, and not all of them are related to the target tweet{}.
The next step is to cluster $\mathcal{C}$ into news stories. For this, we use a clustering algorithm that uses the text from the tweets and clusters them based on the similarity in the vocabulary used in the tweets.
Mathematically, the clustering can be described as a function $f_c$ that assigns an index to any tweet.
The result of the clustering is a set of clusters $\{\mathcal{C}_{i}\}$. Each $\mathcal{C}_{i}$ contains news on a specific news story.
For instance, the time interval 23rd of August and 29th of August 2021 contains five clusters with stories about the Ida hurricane, a regional conference in Iraq, the Kabul airport attack, the Kabul airport evacuation, and the US FDA full approval of Pfizer/BioNTech's COVID-19 vaccine.

Once we have the clusters, we need to identify the cluster to which the target tweet{} belongs. For this, we use the same Clustering model{}, i.e., $f_c$. Given the target tweet{}, the clustering function finds the cluster that best matches the target tweet{}. We refer to this as $\mathbb{C}^{*}$.
For example, the target tweet{} "No marines were killed in the Kabul airport attack, they were just injured" will be placed in the cluster of news about the Kabul airport attack. \\
Figure \ref{fig:flowchart}-\rom{3} schematically shows how tweets are clustered.
Each point represents a tweet. The blue circles are tweets from reliable sources{}, and the red star is the target tweet{}.
Circles represent clusters, i.e., news stories. By this point, we have identified the relevant news story from reliable sources{}.
The next step is to cross-check the content of the target tweet{} against the reliable tweets in its corresponding cluster, $\mathbb{C}^{*}$. If the target tweet{} is not aligned with $\mathbb{C}^{*}$, it is likely fake news.
For example, if the target tweet{} is ``No marines were killed in the Kabul airport attack, they were just injured.'', and in the corresponding cluster, we have a tweet like ``12 U.S. service members killed in terrorist attack outside Kabul airport, U.S. official says'' from The Washington Post, the target tweet{} will be labeled as fake.
This line of reasoning is inspired by how humans usually check the veracity of news. \\

We use a second machine learning model for cross-checking,  and we refer to it as the ``Cross-checking model{}''. A schematic picture of this model is shown in figure \ref{fig:flowchart}-\rom{4}.
The Cross-checking model{} relies on the semantic, sentiment similarities, and emotional characteristics between the target tweet{} and tweets in $\mathbb{C}^*$ to determine if the target tweet{} is aligned with the reliable sources{}. We use separate models to characterize sentiment and semantic similarities, and emotional characteristics.

The Cross-checking model{} (figure \ref{fig:flowchart}-\rom{3}) is different from the Clustering model{} (figure \ref{fig:flowchart}-\rom{4}) in the sense that for clustering, we put more emphasis on the words in the tweet, whereas for cross-checking, we train our model to pay more attention to the meaning. Different techniques can be used for cross-checking. These different techniques do not necessarily agree with each other. As an illustration, a target tweet{} may have the same sentiment as the ones in reliable sources{}, but with different semantics. The final piece of the pipeline is a model that takes the result of all the different comparison techniques (e.g., sentiment, semantic) and decides if the target tweet{} is aligned with the relevant tweets from reliable sources{} (i.e., $\mathbb{C}^{*}$). This model has to consider all the different comparison methods and weigh their results when they do not agree with each other. \\
We now describe each element of the pipeline in more detail. Specifically, in the following subsections, we will describe the model for clustering tweets based on their content, the models used for feature extraction from tweets, and the final classification based on different comparison methods.

\subsection{Clustering Model}

The first element is the clustering model. The Clustering model{} takes the collection of the tweets from reliable sources{} and clusters them to different groups that have similar news content.

For the clustering model, we need a mathematical representation of the text of the tweets in the collection. At this stage, we focus on the words used in tweets. For instance, if two tweets share words like ``Kabul airport'' and ``US military'', it is likely that they are about the same story. In NLP (Natural Language Processing), encodings like ``bag of words'' \cite{harris1954distributional, mctear2016conversational} or TF-IDF can provide representations based on the vocabularies used in the tweets.

The bag-of-words encoding uses a corpus of words and represents any given text with a vector of the length of the corpus. If a word in the corpus is present in the text, the corresponding element of the vector would be the frequency of the word in the text. TF-IDF provides a similar encoding. The difference is that the elements of the vector are weighted based on the ``inverse document frequency (IDF)''. This reduces the weights of words that frequently occur, such as ``the''. On the other hand, it increases the weight of the words that rarely occur \cite{jones1972statistical}.

Typically, for TF-IDF, the corpus is a collection of all the words used in text samples (with some restrictions on their frequencies). This leads to a long and sparse representation. For our purpose, it is more meaningful to form a corpus of significant keywords. For instance, words like ``in'' and ``the'' are likely to appear in various news but carry little weight in discriminating those stories. In contrast, words like ``Kabul'', ``Taliban'', and ``attack'', are more expressive. We use named entities to form our corpus of words. Named entities are objects such as organizations, people,  Geopolitical Entity (GPE). We use a python library called ``SpaCy'' \cite{spacy} to identify the named entities \cite{choi2015depends}.

We use the collection of named entities as the corpus for our TF-IDF model to vectorize the text of a given tweet. Then we apply this encoding to the collection of tweets from reliable sources{}, which gives a vector for each tweet from the collection.\\
Next, we apply the k-means clustering algorithm to the vectorized tweets. This gives clusters of tweets where each cluster contains tweets that have similar named entities. For the k-means algorithm, we need to identify the number of clusters before the clustering. However, the number of clusters depends on $\mathbb{C}^{*}$ and could change. We pick the number of clusters, k, such that the clustering maximizes the mean Silhouette Coefficient \cite{rousseeuw1987silhouettes}. The mean silhouette coefficient characterizes the ratio of intra-cluster distance and inter-cluster distance. Maximizing the mean silhouette coefficient indicates that clusters are well-apart compared to their internal distance. Finally, we have to assign the target tweet{} to one of the clusters. To do so, we apply the same TF-IDF encoding to the target tweet{} and vectorize the text of the target tweet{}. We then apply the k-means model, trained on the collection, to the target tweet{}. This returns the cluster that shares the largest number of named entities with the target tweet{}. Finally, we use the cosine-similarity to filter the closest tweets inside that cluster for the target tweet{}. Table \ref{table:reliables to compare with the example tweet} gives an example of a target tweet{} and tweets in its corresponding cluster, $\mathbb{C}^{*}$. Note that clustering news based on the vocabulary is not perfect. For instance, the vocabularies in one tweet may match multiple clusters.
This is because, fundamentally, it is sometimes difficult to separate news stories, as there may be multiple stories that are related. This leads to overlapping clusters. One may consider soft clustering algorithms for this application \cite{bezdek2013pattern}.

\begin{table}[h!]
\centering
{\renewcommand{\arraystretch}{1.0}
\begin{tabular}{|>{\centering\arraybackslash}m{.2\linewidth}|>{\centering\arraybackslash}m{.8\linewidth}|}
\hline
\multicolumn{2}{|c|}{tweet examples} \\
\hline \centering
Twitter Account&Tweet Text\\
\hline \centering
CNN& ``At least 12 members of the US military were killed and at least 15 more were injured in the attack at Kabul's airport, Gen. Kenneth ``Frank'' McKenzie, head of the US Central Command, said at a briefing.''\\
\hline \centering
New York Times& ``Breaking News: At least 12 U.S. service members and scores of Afghan civilians were killed in the bombings outside the Kabul airport, officials said.''\\
\hline \centering
Associated Press& ``Two U.S, officials say at least 12 U.S. service members were killed in the Afghanistan bombings, including 11 Marines and one Navy medic. Officials say a number of U.S. troops were wounded.''\\
\hline \centering
BBC World& ``Twelve US service members - 11 marines and a Navy medic - were killed in Kabul airport attack, officials tell US media.''\\
\hline \centering
The target tweet& ``No marines were killed in the Kabul airport attack, they were just injured.''\\
\hline
\end{tabular}
}
\caption{Example of $\mathbb{C}^{*}$ for the target tweet{} of  ``No marines were killed in the Kabul airport attack, they were just injured''. By comparing the target tweet{} with these tweets from reliable sources, we can see that this news is not real. }
\label{table:reliables to compare with the example tweet}
\end{table}

\subsection{Cross-Checking Model}
At this point, we have the target tweet{} as well as its corresponding story from reliable sources{} (i.e., $\mathbb{C}^{*}$). The function of the Cross-checking model{} is to compare the target tweet{} with tweets in $\mathbb{C}^{*}$ and determine if the target tweet{} is aligned with our reliable sources{} or not.

To train a model to classify the target tweet{} as real{} or fake{}, we extract some features from tweets. Specifically, we use the semantic, sentiment, and emotion of a tweet to compare the target tweet{} and reliable sources{}. Figure \ref{fig:flowchart}-\rom{4} shows the diagram for our Cross-checking model{}. In the following subsections, we will describe the NLP tools we use to extract each of these in detail.

\subsubsection{Semantic Similarity}
We want to measure how semantically close the target tweet{} is to tweets in $\mathbb{C}^{*}$. This time, we use word-embedding for text representation. A word-embedding can be seen as a mapping from the bag-of-words representation to an encoding that is semantically meaningful. Specifically, word-embedding assigns real-valued vectors to texts or words in a way that preserves their semantic relation. Different word-embedding techniques have been developed for NLP applications. Here, we use a technique known as ``FastText.'' We trained this word embedding on Common Crawl and Wikipedia \cite{grave2018learning}. For two semantically close tweets, their embedding vector should be relatively close. To find the distance between two tweets, we use the cosine between their corresponding vectors. If the two tweets are aligned, the cosine should be close to one. If they are distant, the cosine would be small. We use the cosine as a similarity measure  to compare the two vectors. This provides one of the three elements that will be used for checking the alignment of the target tweet{} with respect to reliable sources{}.

\subsubsection{Sentiment Similarity}
In addition to semantic, we examine the sentiment difference between the target tweet{} and reliable tweets. For this, we train a semantic analyzer model using fastText over 3.6 million reviews from the Amazon dataset \cite{he2016ups,mcauley2015image}. Then we check the sentiment for the target tweet{} and reliable sources{}. We use  the difference between their sentiment values as a feature. For example, the sentiment of the target tweet{} ``The India flood did not have any victims.'' is zero, i.e., neutral sentiment. The corresponding reliable tweet is ``At least 15 dead and dozens missing in India floods '', which has a sentiment of $-1$, i.e., negative. The difference of the sentiment values is $-1$. This means that for this case, we can identify the target tweet{} as fake using its sentiment. \\
However, cross-checking is not always this simple, and we can not always rely on comparing sentiments to cross-check the veracity of the target tweet{}.
It is possible to make fake news with the same sentiment as reliable sources{}. Therefore, for our Cross-checking model{}, we also analyze the emotional content of the target tweet{}.

\subsubsection{Emotion Analysis}
Humans can identify emotions from the text. Emotions that can be received from fake{} news might differ from real{} news. We expect fake{} news to cause emotions such as fear and surprise. We use a tool called text2emotion to extract emotions from the text of the tweets. Text2emotion is a python library that, given an input text, assigns one of the Happy, Angry, Sad, Surprise, or Fear emotions to it. For example, the target tweet{} ``No marines were killed in the Kabul airport attack, they were just injured'' stimulates both sadness and happiness, while reliable tweets from the same cluster cause fear and anger.

\subsubsection{Data and Classification}
The extracted features represent the semantic and sentiment differences and emotional characteristics between the target tweet{} and the tweets from reliable sources{}. For the last part of our pipeline, we need a model that takes these extracted features as inputs and classifies if the target tweet{} is real{} or fake{}.\\
For this model, we need a labeled dataset. We used the ``Fake and Real News dataset'' published on Kaggle \footnote{https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset}. This dataset is based on Ahmed et al. \cite{ahmed2018detecting}. It includes 9365 fake{} news articles and 18984 real{} articles in English. The Fake and Real News dataset contains article text, article type, article label (fake{} or real{}), article title, and article date. These articles are longer than typical tweets. Therefore, we use article titles to test and train our model. These article titles act similar to tweets. We refer to this dataset as the ``Fake-Real'' dataset. We use the Fake-Real dataset to train and test our Cross-checking model and identify tweets' veracity. Figure \ref{fig:pie_dist_data} shows the distribution of classes in our dataset.\\

\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figure2.pdf}
\caption{Distribution of classes in our Fake-Real dataset. Our dataset has 1921 fake and 2764 real tweets}
\label{fig:pie_dist_data}
\end{figure}

We train a random forest model on this dataset. We use the extracted features (i.e., semantic and sentimental differences and emotional characteristics) of each tweet and its label to train the random forest model. Figure \ref{fig:dataset_schema} includes an example of a tweet containing fake{} news and a tweet from a reliable source, which is not aligned with the target tweet{}.

\begin{figure*
\centering
\includegraphics[scale=0.37]{Figure3.pdf}
\caption{An example Tweet and the corresponding classification process. On the left side, the tweet contains fake{} news, and the right side contains a tweet from the PolitiFact Twitter account. The model cross-checks the target tweet{} with a tweet from a reliable source to find out its veracity.}
\label{fig:dataset_schema}
\end{figure*}

\section{Results}
We use three metrics to evaluate the performance  of our classifiers. These metrics are accuracy, precision, and recall \footnote{https://en.wikipedia.org/wiki/Precision\_and\_recall}. Figure \ref{fig:diagram} shows these metrics for our model. Our random forest model has an accuracy of  0.70, a precision of 0.70, and a recall of 0.70 for the class fake{} of tweets. We balanced the population of fake and real tweets in our training dataset for these results.
\begin{figure
\centering
\includegraphics[width = \columnwidth]{Figure4.pdf}
\caption{The results for detection of fake{} tweets. Random Forest gives an accuracy of 0.70}
\label{fig:diagram}
\end{figure}

We compared our result with \cite{agarwal2019analysis}, which we refer to as the VasuAgarwal model, in Figure \ref{fig:compare_bar_chart_bw_2}. As was explained in the Introduction, this work is comparable to our model in two major senses. First, they only use text for the identification of fake{} tweets. Second, similar to our model, their work is not limited to a specific story and can be used for any given target tweet. Figure \ref{fig:compare_bar_chart_bw_2} shows that our model achieves better performance for all metrics \cite{agarwal2019analysis}.
\begin{figure
\centering
\includegraphics[width = \columnwidth]{Figure5.pdf}
\caption{Comparison of the Random Forest results with \cite{agarwal2019analysis}.
The comparisons were made for three different metrics: f1-score, precision, and recall. For all metrics, our model outperforms the result from \cite{agarwal2019analysis}. In all three metrics, our model shows better results.
}
\label{fig:compare_bar_chart_bw_2}
\end{figure}

We also tested our model to determine the veracity of tweets from some famous politicians. Table \ref{table:tweet_examples} shows some examples, and our model predicts these tweets' veracity correctly.\\
\begin{table}[h!]
\centering
{\renewcommand{\arraystretch}{1.5}
\begin{tabular}{|>{\centering\arraybackslash}m{.15\linewidth}|>{\centering\arraybackslash}m{.44\linewidth}|>{\centering\arraybackslash}m{.18\linewidth}|>{\centering\arraybackslash}m{.18\linewidth}|}
\hline \centering
Tweeter&Tweet Text & Prediction & PolitiFact \\
\hline \centering
Donald Trump&``Michigan just refused to certify the election results'' & fake{} & fake{}\\
\hline \centering
Bernie Sanders&``You know what Amazon paid in federal income taxes last year? zero'' & real{} & real{}\\
\hline
\end{tabular}
}
\caption{Example of tweets containing fake{} and real{} news from famous politicians. These tweets, labeled as fake{} or real{} by PolitiFact, have been correctly classified by our model. }
\label{table:tweet_examples}
\end{table}
There exist some tweets in the dataset that were mislabeled.
Interestingly, our model managed to catch these mistakes.
Table \ref{table:wrong_right_predicted_table} shows an example that was labeled as a fake{} tweet, whereas it is actually real{}. Our model labels it as a real{} tweet. This indicates that the performance of our model is probably even higher than the numbers in figure \ref{fig:diagram}. As a result, we expect to get a higher performance for our model on a more accurate dataset.
\begin{table}[h!]
\centering
{\renewcommand{\arraystretch}{1.5}
\begin{tabular}{|>{\centering\arraybackslash}m{.45\linewidth}|>{\centering\arraybackslash}m{.20\linewidth}|>{\centering\arraybackslash}m{.15\linewidth}|>{\centering\arraybackslash}m{.15\linewidth}|}

\hline \centering
Tweet Text & Prediction (our model) & Reality & Dataset Label  \\
\hline \centering
``Ben Jerry's ice cream founders were arrested at the US capital''&real{}&real{}&fake{}\\
\hline
\end{tabular}
}
\caption{Example of a tweet that was mislabeled in the dataset and our model classified  correctly.}
\label{table:wrong_right_predicted_table}
\end{table}
\newline

\section{Discussions and Conclusion}

We built a model for fake news detection, which is inspired by human behavior. Namely, our model compares tweets with reliable sources{} to find whether they contain fake{} or real{} news. We wanted to build this model in a way that is not limited to viral stories. Therefore, we trained our model on a dataset containing diverse news stories. For this case, other famous datasets exist, such as Liar \cite{wang2017liar}. Although the Liar dataset is more widely used, we chose the Fake and Real News dataset since it is larger and more diverse.\\

There are a few assumptions and details about the work that we need to emphasize. First, in order to compare tweets with reliable sources{}, we collect tweets from Twitter accounts of reliable news agencies. Here we used BBC, Deutsche Welle, The New York Times, Al Jazeera, Associated Press, The Washington Post, etc., for the reliable sources. Choosing more than one reliable source not only helps us to cover more tweets, but also helps us determine its veracity more accurately. Apart from that, based on the subject of news, whether political, scientific, economic, etc., we must choose reliable sources{} related to that subject.\\
Note that in some cases, we might not be able to assign a cluster to the target tweet. For instance, there may be fake{} news that has no reflection in the media. In this case, we will be unable to find reliable news for that specific target tweet{}.

Also, note that the choice of reliable sources{} can significantly influence the classification result. For instance, using Fox News as a reliable source significantly changes the results. This is because fake news detection is, to some extent, subjective. Even in real life, different people may not agree on whether news is fake or real based on what they use to cross-check the news against. We tried to pick more factual news outlets based on some research\footnote{https://www.adfontesmedia.com/static-mbc/} for our choice here. Nevertheless, we understand that not everyone may agree with the choices of reliable sources{}. However, the approach can be adapted to what the user or the target audience trust for reliable information.

Another point that we need to emphasize is that we choose the titles of news articles from the ``Fake and Real news dataset'' as our target tweet{}s. These titles are similar to tweets because they are short texts containing news.
We also use these short texts to extract tweets from Twitter and determine the veracity of extracted tweets using these titles.
We believe that our model can be used for long text as well. In that case, we can use articles from reliable news agencies to compare a long target text with them. In the case of long texts, the three features we extract might not be enough. This is because the overlap between news articles is more in long texts, and we probably will need other features for the cross-checking.

Thirdly, an important aspect of our work is that our model is not limited to a specific story. Often models that are developed to use news content for fake news detection focus on specific stories on social media \cite{qazvinian2011rumor}, and their applications are limited to those specific stories. Our model is not limited to specific stories and can cover a wider range of tweets.
Also, note that, as expected, compared to story-focused models, our approach is  less accurate. However, it outperforms other approaches that are not limited to specific stories. One of the main bottlenecks of our approach is news clustering. Our news clustering is based on vocabulary and is not perfect. For instance, the vocabularies in one tweet may match multiple clusters.
This is because, fundamentally, it is sometimes difficult to separate news stories, as there may be multiple stories that are related. This leads to overlapping clusters. One may consider soft clustering algorithms for this application \cite{bezdek2013pattern}.

Another key point about our approach is that for a given target tweet{}, we collect tweets from reliable sources{} from both before and after the target tweet{} is posted.
First, note that we can easily change this and limit it to tweets before the target tweet{}. We think it is important since news agencies might have delayed broadcasting some news for any reason. The last remark is that we did not put much effort into fine-tuning our model. We expect that our result could significantly improve with fine-tuning the hyper-parameters of the model.
In summary, we presented a model to predict the veracity of news on Twitter by checking the deviations from reliable sources. The results outperform similar works. Our model can be divided into four parts. We first build a collection of tweets from reliable sources from the same week as the target tweet{}. Next, we cluster them based on their stories and identify the cluster related to the target tweet{}. Last, we build a model that cross-checks the target tweet{} with reliable tweets based on their semantic and sentimental differences and emotional characteristics.
There is room to improve the model and results by optimizing different model parts and using a better dataset for training. The high efficiency of the model shows that the model inspired by human behavior can become biased, too, since the main idea of this approach is based on the behavior of checking the reliable source. As discussed, this can become toxic too.

redpajama_set_name: RedPajamaArXiv


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The Canadian government on Thursday unveiled a CAN$1.6 billion (US$1.2 billion) plan to help the country deal with the looming dangers of a warming world, such as floods, wildfires and extreme heat.
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