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

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Evaluating stance-annotated sentences from political blogs regarding the Brexit:a quantitative analysis

This paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to identify features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features, such as punctuation, words and grammatical categories that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine stance similarities in the data set. We show that among the six stance categories in the corpus, contrariety and necessity are the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances. necessity has longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters

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

Searching for Truth in a Database of Statistics

International audienceThe proliferation of falsehood and misinformation, in particular through the Web, has lead to increasing energy being invested into journalistic fact-checking. Fact-checking journalists typically check the accuracy of a claim against some trusted data source. Statistic databases such as those compiled by state agencies are often used as trusted data sources, as they contain valuable, high-quality information. However, their usability is limited when they are shared in a format such as HTML or spreadsheets: this makes it hard to find the most relevant dataset for checking a specific claim, or to quickly extract from a dataset the best answer to a given query. We present a novel algorithm enabling the exploitation of such statistic tables, by (i) identifying the statistic datasets most relevant for a given fact-checking query, and (ii) extracting from each dataset the best specific (precise) query answer it may contain. We have implemented our approach and experimented on the complete corpus of statistics obtained from INSEE, the French national statistic institute. Our experiments and comparisons demonstrate the effectiveness of our proposed method