arXiv subjects

Chhandak Bagchi

Publications and source records attributed to Chhandak Bagchi.

5 recordsLinked to original sources

arXiv2025

Political Biases on X before the 2025 German Federal Election

This study examines whether German X users would see politically balanced news feeds if they followed comparable leading politicians from each federal parliamentary party of Germany. We address this question using an algorithmic audit tool [1] and all publicly available posts published by 436 German politicians on X. We find that the default feed of X showed more content from far-right AfD than from other political parties. We analyze potential factors influencing feed content and the resulting political non-representativeness of X. Our findings suggest that engagement measures and unknown factors related to party affiliation contribute to the overrepresentation of extremes of the German political party spectrum in the default algorithmic feed of X.

cs.SI↗

arXiv2024

Social media algorithms can curb misinformation, but do they?

A recent article in $\textit{Science}$ by Guess et al. estimated the effect of Facebook's news feed algorithm on exposure to misinformation and political information among Facebook users. However, its reporting and conclusions did not account for a series of temporary emergency changes to Facebook's news feed algorithm in the wake of the 2020 U.S. presidential election that were designed to diminish the spread of voter-fraud misinformation. Here, we demonstrate that these emergency measures systematically reduced the amount of misinformation in the control group of the study, which was using the news feed algorithm. This issue may have led readers to misinterpret the results of the study and to conclude that the Facebook news feed algorithm used outside of the study period mitigates political misinformation as compared to reverse chronological feed.

cs.SI↗

arXiv2024

Effects of Research Paper Promotion via ArXiv and X

In the evolving landscape of scientific publishing, it is important to understand the drivers of high-impact research, to equip scientists with actionable strategies to enhance the reach of their work, and to understand trends in the use of modern scientific publishing tools to inform their further development. Here, we study trends in the use of early preprint publications and revisions on ArXiv and the use of X (formerly Twitter) for promotion of such papers in computer science and physics. We find that early submissions to ArXiv and promotion on X have soared in recent years. Estimating the effect that the use of each of these modern affordances has on the number of citations of scientific publications, we find that peer-reviewed conference papers in computer science that are submitted early to ArXiv gain on average $21.1 \pm 17.4$ more citations, revised on ArXiv gain $18.4 \pm 17.6$ more citations, and promoted on X gain $44.4 \pm 8$ more citations in the first 5 years from an initial publication. In contrast, journal articles in physics experience comparatively lower boosts in citation counts, with increases of $3.9 \pm 1.1$, $4.3 \pm 0.9$, and $6.9 \pm 3.5$ citations respectively for the same interventions. Our results show that promoting one's work on ArXiv or X has a large impact on the number of citations, as well as the number of influential citations computed by Semantic Scholar, and thereby on the career of researchers. These effects are present also for publications in physics, but they are relatively smaller. The larger relative effect sizes, effects of promotion accumulating over time, and elevated unpredictability of the number of citations in computer science than in physics suggest a greater role of world-of-mouth spreading in computer science than in physics.

cs.DL↗

arXiv2024

Analyzing and Estimating Support for U.S. Presidential Candidates in Twitter Polls

Polls posted on social media have emerged in recent years as an important tool for estimating public opinion, e.g., to gauge public support for business decisions and political candidates in national elections. Here, we examine nearly two thousand Twitter polls gauging support for U.S. presidential candidates during the 2016 and 2020 election campaigns. First, we describe the rapidly emerging prevalence of social polls. Second, we characterize social polls in terms of their heterogeneity and response options. Third, leveraging machine learning models for user attribute inference, we describe the demographics, political leanings, and other characteristics of the users who author and interact with social polls. Finally, we study the relationship between social poll results, their attributes, and the characteristics of users interacting with them. Our findings reveal that Twitter polls are biased in various ways, starting from the position of the presidential candidates among the poll options to biases in demographic attributes and poll results. The 2016 and 2020 polls were predominantly crafted by older males and manifested a pronounced bias favoring candidate Donald Trump, in contrast to traditional surveys, which favored Democratic candidates. We further identify and explore the potential reasons for such biases in social polling and discuss their potential repercussions. Finally, we show that biases in social media polls can be corrected via regression and poststratification. The errors of the resulting election estimates can be as low as 1%-2%, suggesting that social media polls can become a promising source of information about public opinion.

cs.SI↗

arXiv2022

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors' stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.

cs.CL↗