8 research outputs found
Identifying Users with Opposing Opinions in Twitter Debates
In recent times, social media sites such as Twitter have been extensively
used for debating politics and public policies. These debates span millions of
tweets and numerous topics of public importance. Thus, it is imperative that
this vast trove of data is tapped in order to gain insights into public opinion
especially on hotly contested issues such as abortion, gun reforms etc. Thus,
in our work, we aim to gauge users' stance on such topics in Twitter. We
propose ReLP, a semi-supervised framework using a retweet-based label
propagation algorithm coupled with a supervised classifier to identify users
with differing opinions. In particular, our framework is designed such that it
can be easily adopted to different domains with little human supervision while
still producing excellent accuracyComment: Corrected typos in Section 4, under "Visibly Opinionated Users". The
numbers did not add up. Results remain unchange
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles
Discriminatory caste attitudes currently stigmatize millions of Indians,
subjecting individuals to prejudice in all aspects of life. Governmental
incentives and societal movements have attempted to counter these attitudes,
yet accurate measurements of public opinions on caste are not yet available for
understanding whether progress is being made. Here, we introduce a novel
approach to measure public attitudes of caste through an indicator variable:
openness to intercaste marriage. Using a massive dataset of over 313K profiles
from a major Indian matrimonial site, we precisely quantify public attitudes,
along with differences between generations and between Indian residents and
diaspora. We show that younger generations are more open to intercaste
marriage, yet attitudes are based on a complex function of social status beyond
their own caste. In examining the desired qualities in a spouse, we find that
individuals open to intercaste marriage are more individualistic in the
qualities they desire, rather than favoring family-related qualities, which
mirrors larger societal trends away from collectivism. Finally, we show that
attitudes in diaspora are significantly less open, suggesting a bi-cultural
model of integration. Our research provides the first empirical evidence
identifying how various intersections of identity shape attitudes toward
intercaste marriage in India and among the Indian diaspora in the US.Comment: 12 pages; Accepted to be published at ICWSM'1
Game, Set, and Conflict: Evaluating Conflict and Game Frames in Indian Election News Coverage
News frames refer to how journalists organize and present information to
convey a particular message or perspective to their readers. When covering
elections, these frames shape how the public perceives electoral issues and
events. This study examines how news frames, especially conflict and game
frames, were employed by news organizations in India to cover the 2014 and 2019
general elections. We analyzed how the frames varied temporally, by region, and
by the party being featured in the articles. Key findings include (i) conflict
and games frames are employed more often in highly electorally consequential
states (higher legislative seats) than in other states (ii) articles featuring
challenger parties are more likely to have conflict and game frame articles
than those featuring incumbent parties (iii) the national parties (BJP,
Bharatiya Janata Party) and (INC, Indian National Congress) disproportionately
feature in articles having conflict frames. Overall, our analysis highlights
the evolving nature of election campaigns and how conflict and game frames play
a major part in them.Comment: ICWS
Designing for Safe, Fun and Informative Online Cross-Partisan Interactions
The past decade in the US has been one of the most politically polarizing in recent memory. Increasingly, ordinary Democrats and Republicans fundamentally dislike and distrust each other, even when they agree on policy issues. Most Americans report believing that the opposing party is a "serious threat to the United States and its people". This extreme partisan hostility has wide-ranging consequences, even affecting how partisans respond to COVID-19 mitigation measures. In this context, this dissertation aims to reduce hostile interactions and attitudes towards ordinary Democrats and Republicans. I argue that we can reduce hostility by leveraging nonpolitical online spaces that cut through the partisan faultlines in uniquely engaging ways. I develop approaches to transform the currently hostile, uninspiring nature of online political interactions into not only a safe experience but also a fun and informative one. I take a mixed-methods approach to studying outpartisan hostility, combining computational social science with design methods. The dissertation progresses from a large-scale exploratory analysis of online political discussions to developing potential designs to reduce online partisan hostility and, finally, to designing and evaluating a fun party game that reduces outparty hostility.
In the first study, through large-scale computational analysis of billions of Reddit comments, I find that nearly half of all political discussions on Reddit take place in nonpolitical communities and that cross-partisan political conversations in these communities are less toxic than those in explicitly political communities. These findings suggest that shared nonpolitical interests can temper online partisan hostility. In the second study, through in-depth qualitative interviews and design probes, I explore approaches to surface these nonpolitical interests and identities during online political interactions on Reddit. I demonstrate that participants are comfortable knowing and revealing shared memberships in nonpolitical communities with outpartisan discussion partners which they expect to be humanizing, potentially reducing the hostility in those interactions. Through the interviews, I find that apart from serious deliberative discussions, participants also engage in light-hearted and casual political interactions where the motivation to simply entertain themselves and have fun. In the final study, drawing on insights from the prior study and extant political science research, I develop an online party game that combines the relaxed, playful nonpartisan norms of casual games with corrective information about Democrats' and Republicans' political views that are often misperceived. Through an experiment, I find that playing the game significantly reduces hostile attitudes toward outparty supporters among Democrats.
Overall, this dissertation demonstrates the potential of using nonpolitical context to facilitate quality online cross-partisan interactions that account for and mitigate the heightened levels of partisan animosity we observe today.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174507/1/arajades_1.pd
Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)
Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly half of all political talk takes place in subreddits that host political content less than 25% of the time. In other words, cumulatively, political talk in non-political spaces is abundant. We further examine the nature of political talk and show that political conversations are less toxic in non-political subreddits. Indeed, the average toxicity of political comments replying to a out-partisan in non-political subreddits is less than even the toxicity of co-partisan replies in explicitly political subreddits
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits
Online communities about similar topics may maintain very different norms of interaction. Past research identifies many processes that contribute to maintaining stable norms, including self-selection, pre-entry learning, post-entry learning, and retention. We analyzed political subreddits that had distinctive, stable levels of toxic comments on Reddit, in order to identify the relative contribution of these four processes. Surprisingly, we find that the largest source of norm stability is pre-entry learning. That is, newcomers' first comments in these distinctive subreddits differ from those same people's prior behavior in other subreddits. Through this adjustment, they nearly match the toxicity level of the subreddit they are joining. We also show that behavior adjustments are community-specific and not broadly transformative. That is, people continue to post toxic comments at their previous rates in other political subreddits. Thus, we conclude that in political subreddits, compatible newcomers are neither born nor made– they make local adjustments on their own
Stance Classification through Proximity-based Community Detection
International audienceNumerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection