Update reserach.md

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@@ -27,24 +27,28 @@ With Omer F. Yalcin, Samuel E. Bestvater, Kevin Munger, Burt L. Monroe, and Bruc
 
 **Political Communication in the Streaming-oriented Platform, Twitch** <br> 
 *Under Review* [[Paper]](https://osf.io/9hdej) <br>
-> Until now, scholars of social media and politics have focused on text-oriented social media platforms, such as Facebook and Twitter, neglecting newer platforms focused on video and real-time chat. I investigate a
-video-oriented social media platform that has seen little attention from social scientists: Twitch. I study the patterns of political communication in the platform by focusing on the streaming chat of political “streamers”, a term for live broadcasters of the platform. In this paper, I aim to answer three questions on Twitch politics: 1) Who are political Twitch streamers? 2) What contents are covered in the political streams? 3)
-How do the political streamers and their audiences interact with each other? By using the supervised machine learning methods, I have identified 574 political streamers out of 59,272 total streamers, whose information
-is retrieved via the Twitch API. I have collected 33.6 million chat posts from political streamers’ live broadcasting and found 646,073 unique Twitch users who have posted at least one chat post. Using the
-wide corpus of text data, I conduct text analyses to observe what contents are covered in the political streams and network analyses to capture the interactions among political streamers and viewers of their stream.
-
-**Comparison of Credibility of News Shared in Four Different Platforms during Midterm Election 2022:
-Twitter, Facebook, Instagram, and Reddit** <br>
+> Until now, scholars of social media and politics have focused on text-oriented social media platforms, such as Facebook and Twitter, neglecting newer platforms focused on video and real-time chat. I investigate a video-oriented social media platform that has seen little attention from social scientists: Twitch. I study the patterns of political communication in the platform by focusing on the streaming chat of political “streamers”, a term for live broadcasters of the platform. In this paper, I aim to answer three questions on Twitch politics: 1) Who are political Twitch streamers? 2) What contents are covered in the political streams? 3) How do the political streamers and their audiences interact with each other? By using the supervised machine learning methods, I have identified 574 political streamers out of 59,272 total streamers, whose information is retrieved via the Twitch API. I have collected 33.6 million chat posts from political streamers’ live broadcasting and found 646,073 unique Twitch users who have posted at least one chat post. Using the wide corpus of text data, I conduct text analyses to observe what contents are covered in the political streams and network analyses to capture the interactions among political streamers and viewers of their stream.
+
+**The Persistence of Contrarianism on Twitter: Mapping users’ sharing habits for the Ukraine war, COVID-19 vaccination, and the 2020 Midterm Elections** <br> 
+With David Axelrod and John Paolillo *Under Review*  <br>
+> Empirical studies of online disinformation emphasize matters of public concern such as the COVID-19 pandemic, foreign election interference, and the Russo-Ukraine war, largely in studies that treat the topics separately. Comparatively fewer studies attempt to relate such disparate topics and address the extent to which they share behaviors. In this study, we compare three samples of Twitter data on COVID-19 vaccination, the Ukraine war and the 2020 midterm elections, to ascertain how distinct ideological stances of users across the three samples might be related. Our results indicate the emergence of a contrast between institutionally-oriented stances and a broad contrarian stance. The contrarian position is most clearly defined by retweeting conspiratorial content targeting public health, democratic institutions and US foreign policy. We confirm the existence of ideologically coherent cross-subject stances among Twitter users, but in a manner not squarely aligned with right-left political orientations.
+
+**Comparison of Credibility of News Shared in Four Different Platforms during Midterm Election 2022: Twitter, Facebook, Instagram, and Reddit** <br>
 With Ozgur Can Seckin, Kaicheng Yang, and Fil Menzcer <br>
 > Social media platforms have become primary sources for accessing and consuming political news, aligning with the ongoing digital transformation of the media landscape. While this transformation has facilitated easier access to information, concerns regarding the over-sharing of news from low-credibility sources and partisan-driven news sharing behaviors have emerged as significant issues for both the scientific community and policymakers. Despite various studies on this topic, there remains surprisingly little understanding of how users’ political news sharing behavior differs among different social media platforms. In this article, we compare the patterns of news sharing during a major political event, the United States 2022 midterm election, across three distinct social media platforms: Twitter, Meta (encompassing Facebook and Instagram), and Reddit. We leverage large-scale data collected during the election cycle. Our findings indicate differences in the credibility of news sources shared on each platform, both in terms of source credibility and partisanship. News sources shared on Reddit have higher credibility and are relatively left-leaning compared to those on Twitter and Meta. The study also reveals consistent patterns across all three platforms, indicating that right-leaning URLs tend to be associated with lower credibility, in line with existing literature. However, notable differences among the platforms emerge even when comparing URLs with similar partisan leanings. These findings underscore the importance of conducting multi-platform research on this topic, which can enhance our understanding of the overall news-sharing environment of social media.
 
-**The Political Influence of Non-Politicized Friends: How do social networks affect the spread of protest information in social media?** <br>
-Dissertation Chapter <br>
+**The Political Influence of Non-Politicized Friends: How do social networks affect the spread of protest information in social media?** 
 
 ## Work in progress
 
 **Twitching, Fast and Slow: Field Experiment in Political Stream** <br>
 With Chloe Ahn, Drew Dimmery, and Kevin Munger <br>
+> Online livestreaming has become a prominent hub for online conversations with a variety of content, including politics. Unlike asynchronous political expressions in online spaces, livestreaming chats are more vulnerable to becoming environments where extreme speech is pervasive. To mitigate the spread of toxic speech online, we conducted a field experiment where we intervened in the streaming chat by entering the livestream, leaving comments, and measuring subsequent changes in the discussion. A confederate joined each stream and began commenting in a style that was randomized based on two factors: whether the comments were polite or less polite, and whether the comments were ideologically congruent or incongruent with the leaning of the stream. Our results indicate that sharing a polite or ideologically incongruent comment during a political livestream can reduce the toxicity of speech from other commenters and increase the substantiveness of comments shared. However, these benefits came with the trade-off of decreasing users’ engagement within the chats. While like-minded, less-restrictive expressions online promote engagement with other users, they may also incentivize extreme speech norms that lack substantive reflections on the discussed topics. Our findings have broader implications for studying the relatively new media affordance of livestreaming and important trade-offs in online platform design
 
 **Quantifying the effects of time delay in illegal content takedown** <br>
-With Bao Tran Truong, Natascha Just, Florian Saurwein, and Fil Menzcer <br>
+With Bao Tran Truong, Samuel Groesch, Enrico Verdolotti, Silvia Giordano, Natascha Just, Florian Saurwein, and Fil Menzcer <br>
+> Social media platforms implement content moderation to manage illegal content, which includes copyright violations and unlawful material dissemination. Various regulations mandate different “takedown deadlines” for such content, resulting in inconsistent enforcement and effectiveness across platforms. This study examines the impact of time delays in illegal content moderation, highlighting how different deadlines imposed by regulations influence the social media ecosystem. Building on research that explores the effectiveness of moderation measures for misinformation and harmful content, we utilize an agent-based model to simulate illegal content removal. The findings reveal that the adverse impact of time delay in illegal content removal increases proportionally with longer delays, but beyond a certain threshold, this impact plateaus, rendering prolonged delays ineffective. The study offers insights into optimal content moderation strategies and their implications for policymakers aiming to mitigate the risks associated with illegal content on social media platforms.
+
+**Who you are can predict how you behave politically in online spaces: LLM-based approach to extract personality traits from political comments in news portal** <br>
+With Byunghwee Lee, Haewoon Kwak, and Jisun An <br>
+> In this article, we use GPT-3.5, a type of LLM, to identify personality traits based on the Big 5 model (also known as the OCEAN test) from political comments on the Korean news portal Naver. We then evaluate how well these traits predict users’ political leanings and other behaviors like posting frequency and comment length. Our aim isn’t to precisely determine Big 5 traits, but rather to uncover underlying personality features from news portal comments and see how they relate to various user behaviors. Through analyzing feature importance, we aim to pinpoint which of the 44 questions from the Big 5 test can distinguish between liberal and conservative groups and predict their online behavioral patterns. Our study makes two key contributions: firstly, it demonstrates that features derived from non-political questions can predict political behaviors online, and secondly, it proposes a research methodology using LLM that doesn’t require expert coding to uncover latent personality features from social media data, showing the potential for constructing predictive models based on these traits.