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Paper: Is change the only constant? Profile change perspective on #LokSabhaElections2019

Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few. The profile attributes that users adopt can reflect their interests, belief, or thematic inclinations. Literature has proposed the implications and significance of profile attribute change for a random population of users. However, the use of profile attribute for endorsements and to start a movement have been under-explored. In this work, we consider #LokSabhaElections2019 as a movement and perform a large-scale study of the profile of users who actively made changes to profile attributes centered around #LokSabhaElections2019. We collect the profile metadata for 49.4M users for a period of 2 months from April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how the profile changes vary for the influential leaders and their followers over the social movement. We further differentiate the organic and inorganic ways to show the political inclination from the prism of profile changes. We report how the addition of election campaign related keywords lead to spread of behavior contagion and further investigate it with respect to "Chowkidar Movement" in detail.

Dataset Collection and Description
Our data collection process is divided into two stages. We first manually curate a set of political accounts on Twitter. We use this set of users as a seed set and then collect the profile information of all the followers of these handles.
Seed set: To answer the RQ1, we first manually curate a set of $2,577$ Twitter handles of politicians and official/unofficial party handles involved in Lok Sabha Elections 2019. Let this set of users be called $\mathbf {U}$. Of the $2,577$ curated handles, only $1,293$ were verified at the beginning of April. Let this set of verified handles be called $\mathbf {P}$. We collect a snapshot of the profile information of $\mathbf {P}$ daily for two months from 5th April - 5th June 2019.
Tracked set: We use the set $P$ of verified Twitter handles as a seed set and collect the profile information of all their followers. We hypothesize that the users who follow one or more political handles are more likely to express their political orientation on Twitter. Thus, we consider only the followers of all the political handles in our work. The total number of followers of all the handles in set $\mathbf {P}$ are 600 million, of which only $55,830,844$ users are unique. Owing to Twitter API constraints, we collect snapshots of user-profiles for these handles only once a week over the two months of April 5 - June 5, 2019. There were a total of 9 snapshots, of which 7 coincided with the phases of the election. The exact dates on which the snapshots were taken has been mentioned in Table TABREF10. As we collect snapshots over the of two months, the number of followers of political handles in set $P$ can increase significantly. Similarly, a small subset of followers of handles in $P$ could also have been suspended/deleted. To maintain consistency in our analysis, we only use those handles for our analysis that are present in all the 9 snapshots. We call this set of users as $\mathbf {S}$. The set $\mathbf {S}$ consists of 49,433,640 unique accounts.
Dataset: Our dataset consists of daily and weekly snapshots of profile information of users in set $\mathbf {S}$ and $\mathbf {P}$ respectively. We utilize the Twitter API to collect the profile information of users in both the above sets. Twitter API returns a user object that consists of a total of 34 attributes like name, screen name, description, profile image, location, and followers. We further pre-process the dataset to remove all the handles that were inactive for a long time before proceeding with further analysis.
In set $\mathbf {S}$, more than 15 million out of the total 49 million users have made no tweets and have marked no favourites as well. We call the remaining 34 million users as Active users. Of the Active users, only 5 million users have made a tweet in year 2019. The distribution of the last tweet time of all users in set $S$ is shown in Figure FIGREF14. The 5 million twitter users since the start of 2019 fall in favor of the argument that a minority of users are only responsible for most of the political opinions BIBREF7. We believe there is a correlation between Active users and users who make changes in profile attribute. We, therefore use the set of 34 million users(Active Users of set $\mathbf {S}$ ) for our further analysis.

Profile Attribute Analysis
In this section we characterize the profile change behavior of political accounts (Set $P$) and follower accounts (Set $S$).

Profile Attribute Analysis ::: Political Handles
Increase in the Internet user-base and widespread adoption of smartphones in the country has led to an increase in the importance of social media campaigns during the elections. This is evident by the fact that in our dataset (Set $U$), we found that a total of 54 handles got verified throughout 61 snapshots. Moreover the average number of followers of handles in set $P$ increased from $3,08,716.84$ to $3,19,881.09$ over the 61 snapshots. Given that the average followers of set $P$ increased by approximately $3\%$ and 54 profile handles got verified in the span of 61 days, we believe that efforts of leaders on social media campaign can't be denied.
We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. The username is a unique handle or screen name associated with each twitter user that appears on the profile URL and is used to communicate with each other on Twitter. Some username examples are $@narendramodi$, $@RahulGandhi$ etc. The display name on the other hand, is merely a personal identifier that is displayed on the profile page of a user, e.g. `Narendra Modi', `Rahul Gandhi', etc. The description is a string to describe the account, while the location is user-defined profile location. We considered the 5 profile attributes as stated above since these attributes are key elements of a users identity and saliently define users likes and values BIBREF2.
For the set of users in $P$, the total number of changes in these 5 profile attributes over 61 snapshots is 974. Figure FIGREF12 shows the distribution of profile changes for specific attributes over all the 61 snapshots. The most changed attribute among the political handles is the profile image followed by display name. In the same Figure, there is a sharp increase in the number of changes to the display name attribute on 23rd May, 2019. The increase is because @narendramodi removed Chowkidar from the display name of his Twitter handle which resulted in many other political handles doing the same. Another interesting trend is that none of the handles changed their username over all the snapshots. This could be attributed to the fact that Twitter discourages a change of usernames for verified accounts as the verified status might not remain intact after the change BIBREF8.

Profile Attribute Analysis ::: Similarity analysis
Change in an attribute in the profile involves replacing an existing attribute with a new one. We analyze the similarity between the current, new display names and usernames used by users in set $\mathbf {P}$ and $\mathbf {S}$ respectively. For this analysis, we consider only the users who have changed their username or display name at least once in all 9 snapshots. We use the Longest Common Subsequence (LCS) to compute the similarity between two strings. Thus, for each user, we compute the average LCS score between all possible pairs of unique display names and usernames adopted by them. A high average LCS score indicates that the newer username or display name were not significantly different from the previous ones.
The graph in Figure FIGREF18 shows that nearly $80\%$ of the users in set $S$ made more significant changes to their Display Names as compared to the political handles. (This can be seen by considering a horizontal line on the graph. For any given fraction of users, the average LCS score of set $S$ (Followers) is lesser than set $P$ (Politicians) for approximately $80\%$ percent of users). For around $10\%$ of the users in set $P$, the new and old profile attributes were unrelated (LCS score < $0.5$) and for users in set $S$, this value went up to $30\%$.
INSIGHT 3: Political handles tend to make new changes related to previous attribute values. However, the followers make comparatively less related changes to previous attribute values.

Topic modelling of users
A large number of people were part of the Chowkidar movement. Hence, it is important to gauge their level of interest/support to the party in order to accurately judge the impact of the campaign. We thus perform topic modelling of the description attribute of these users. We utilize the LDA Mallet model BIBREF10 for topic modelling after performing the relevant preprocessing steps on the data. We set the number of topics to 10 as it gave the highest coherence score in our experiments. The 10 topics and the 5 most important keywords belonging to these topics are shown below in Figure FIGREF27. These topics reveal several interesting insights. The most discussed topic amongst these users involves Narendra Modi and includes words like “namo” (slang for Narendra Modi), supporter, nationalist and “bhakt” (devout believer). A few other topics also involve politics like topic 2 and topic 5. Topic 2, in particular contains the words chowkidar, student, and study, indicating that a significant set of users who were part of this campaign were actually students. Topic number 0 accounts for more than $40\%$ of the total descriptions, which essentially means that most of the people involved in this movement were supporting BJP or Narendra Modi. We also plot the topics associated with each user's description on a 2D plane by performing tSNE BIBREF11 on the 10 dimensional data to ascertain the variation in the type of topics. The topics are pretty distinct and well separated on the 2D plane as can be seen in Figure FIGREF30.
INSIGHT 6: The users who were part of the chowkidar movement were mostly Narendra Modi supporters and engaged in political topics

Related Work
Social Media profiles are a way to present oneself and to cultivate relationship with like minded or desired audience. BIBREF0, BIBREF12. With change in time and interest, users on Twitter change their profile names, description, screen names, location or language BIBREF2. Some of these changes are organic BIBREF13, BIBREF2, while other changes can be triggered by certain events BIBREF14, BIBREF15. In this section, we look into the previous literature which are related to user profile attribute's change behavior. We further look into how Twitter has been used to initiate or sustain movements BIBREF14, BIBREF15, BIBREF1. Finally we investigate how the contagion behavior adaptation spread through opinion leaders BIBREF6, BIBREF9 and conclude with our work's contribution to the literature.

Related Work ::: Profile attribute change behavior
Previous studies have focused on change in username attribute extensively, as usernames in Twitter helps in linkability, mentions and becomes part of the profile's URL BIBREF16, BIBREF13, BIBREF17. While the focus of few works is to understand the reason the users change their profile names BIBREF16, BIBREF2, others strongly argue username change is a bad idea BIBREF17, BIBREF13. Jain et al. argue that the main reason people opt to change their username is to leverage more tweet space. Similarly, the work by Mariconti et al. suggests that the change in username may be a result of name squatting, where a person may take away the username of a popular handle for malicious reasons. More recent studies in the area have focused on several profile attributes changes BIBREF2. They suggest that profile attributes can be used to represent oneself at micro-level as well as represent social movement at macro-level.

Discussion
In this paper, we study the use of profile attributes on Twitter as the major endorsement during #LokSabhaElectios2019. We identify $1,293$ verified political leader's profile and extract $49,433,640$ unique followers. We collect the daily and weekly snapshots of the political leaders and the follower handles from 5th April, 2019 to 5th June, 2019. With the constraint of Twitter API, we collected a total of 9 snapshots of the follower's profile and 61 snapshots of Political handles. We consider the 5 major attributes for the analysis, including, Profile Name, Username, Description, Profile Image, and Location.
Firstly, we analyze the most changed attributes among the 5 major attributes. We find that most of the users made at-most 4 changes during the election campaign, with profile image being the most changed attribute for both the political as well as follower handles. We also show that political handles made more changes to profile attributes than the follower handles. About $80\%$ changes made by the followers in the period of data collection was related to #LokSabhaElectios2019. While the profile handles made changes, the changes mostly included going back and forth to same profile attribute, with the change in attribute being active support to a political party or political leader.
We argue that we can predict the political inclination of a user using just the profile attribute of the users. We further show that the presence of party name in the profile attribute can be considered as an organic behavior and signals support to a party. However, we argue that the addition of election campaign related keywords to the profile is a form of inorganic behavior. The inorganic behavior analysis falls inline with behavior contagion, where the followers tend to adapt to the behavior of their opinion leaders. The “Chowkidar movement” showed a similar effect in the #LokSabhaElectios2019, which was evident by how the other political leaders and followers of BJP party added chowkidar to their profile attributes after @narendramodi did. We thus, argue that people don't shy away from showing support to political parties through profile information.
We believe our analysis will be helpful to understand how social movement like election are perceived and sustained through profile attributes on social media. Our findings provide baseline data for study in the direction of election campaign and to further analyze people's perspective on elections.

Figure 5: Distribution of frequency of change for 5 attributes over the period of data collection as shown in Table 1 for set S .

Table 3: List for frequency of presence, addition and removal of party names to given profile attributes in the dataset.

Figure 10: The most discussed topics in a descending order along with the 5most representative words of these topics.

Question: What profile metadata is used for this analysis?
{"answer": ["username", "display name", "profile image", "location", "description"], "evidence": ["We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. The username is a unique handle or screen name associated with each twitter user that appears on the profile URL and is used to communicate with each other on Twitter. Some username examples are $@narendramodi$, $@RahulGandhi$ etc. The display name on the other hand, is merely a personal identifier that is displayed on the profile page of a user, e.g. `Narendra Modi\u0027, `Rahul Gandhi\u0027, etc. The description is a string to describe the account, while the location is user-defined profile location. We considered the 5 profile attributes as stated above since these attributes are key elements of a users identity and saliently define users likes and values BIBREF2."]}