Publications

The Impact of Toxic Trolling Comments on Anti-vaccine YouTube Videos

computational social science AI/ML/NLP social media

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. …

Kunihiro Miyazaki, Takayuki Uchiba, Haewoon Kwak, Jisun An, Kazutoshi Sasahara

Scientific Reports, 2024

Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage

computational social science AI/ML/NLP social media

The emergence of generative AI has sparked substantial discussions, with the potential to have profound impacts on society in all aspects. As emerging technologies continue to advance, it is imperative to facilitate their proper integration into society, managing expectations and fear. This paper investigates users’ perceptions of generative AI using 3M posts on Twitter from January 2019 to March 2023, especially focusing on their occupation and usage. We find that people across various occupations, not just IT-related ones, show a strong interest in generative AI. The sentiment toward generative AI is generally positive, …

Kunihiro Miyazaki, Taichi Murayama, Takayuki Uchiba, Jisun An, Haewoon Kwak

EPJ Data Science, 2024

Press coverage-Blockchain News

5+ papers citing this work (Google scholar)

Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

computational social science AI/ML/NLP

Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. …

Sumin Han, Youngjun Park, Minji Lee, Jisun An, Dongman Lee

ACM CIKM, 2023

Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020

computational social science AI/ML/NLP social media

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, …

Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An

ACM WebSci, 2023

Political Honeymoon Effect on Social Media: Characterizing Social Media Reaction to the Changes of Prime Minister in Japan

computational social science social media

New leaders in democratic countries typically enjoy high approval ratings immediately after taking office. This phenomenon is called the honeymoon effect and is regarded as a significant political phenomenon; however, its mechanism remains underexplored. Therefore, this study examines how social media users respond to changes in political leadership in order to better understand the honeymoon effect in politics. In particular, we constructed a 15-year Twitter dataset on eight change timings of Japanese prime ministers consisting of 6.6M tweets and analyzed them in terms of sentiments, topics, and users. …

Kunihiro Miyazaki, Taichi Murayama, Akira Matsui, Masaru Nishikawa, Takayuki Uchiba, Haewoon Kwak, Jisun An

ACM WebSci, 2023

'This is Fake News': Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information

computational social science computational journalism social media online harm

False information spreads on social media, and fact-checkingis a potential countermeasure. However, there is a severeshortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics likeCOVID-19. In this study, we focus on spontaneous debunk-ing by social media users, which has been missed in exist-ing research despite its indicated usefulness for fact-checkingand countering false information. Specifically, we character-ize the tweets with false information, or fake tweets, thattend to be debunked and Twitter users who often debunk faketweets.

Kunihiro Miyazaki, Takayuki Uchiba, Kenji Tanaka, Jisun An, Haewoon Kwak, Kazutoshi Sasahara

AAAI ICWSM, 2023

MAANG? MANGA? Characterizing Spontaneous Ideation Contest on Social Media

computational social science network science social media

Social media is not only a place for people to communicate on a daily matter but also a virtual venue to transmit and exchange various ideas. Such ideas are known as the raw voices of potential consumers, which come from a wide range of people who may not participate in consumer surveys, and therefore their opinions may contain high value to companies. However, how users share their ideas on social media is still underexplored. This study investigates a spontaneous ideation contest about a generic term for new Big Tech companies, which occurred when Facebook changed its name to Meta. We constructed a comprehensive dataset of tweets containing candidates and examined how they were suggested, spread, and exchanged by social media users. Our findings indicate that different ideas are better on different metrics. The ranking of ideas was not decided immediately after the idea contest started. The first people to post ideas have smaller followers than those who post secondarily or who only share the idea. We also confirmed that replies accumulate unique ideas, but most of them are added in the first depth in reply trees. This study would promote the use of social media as a part of open innovation and co-creation processes in the industry.

Kunihiro Miyazaki, Takayuki Uchiba, Haewoon Kwak, Jisun An

IEEE BigData, 2022 (short)

Modeling Political Activism around Gun Debate via Social Media

computational social science political science network science social media

The United States have some of the highest rates of gun violence among developed countries. Yet, there is a disagreement about the extent to which firearms should be regulated. In this study, we employ social media signals to examine the predictors of offline political activism, at both population and individual level. We show that it is possible to classify the stance of users on the gun issue, especially accurately when network information is available. Alongside socioeconomic variables, network information such as the relative size of the two sides of the debate is also predictive of state-level gun policy. On individual level, we build a statistical model using network, content, and psycho-linguistic features that predicts real-life political action, and explore the most predictive linguistic features. Thus, we argue that, alongside demographics and socioeconomic indicators, social media provides useful signals in the holistic modeling of political engagement around the gun debate.

Yelena Mejova, Jisun An, Gianmarco De Francisci Morales, Haewoon Kwak

ACM Transactions on Social Computing, 2022

5+ papers citing this work (Google scholar)

Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection

computational social science AI/ML/NLP political science social media online harm

The transfer of power stemming from the 2020 presidential election occurred during an unprecedented period in United States history. Uncertainty from the COVID-19 pandemic, ongoing societal tensions, and a fragile economy increased societal polarization, exacerbated by the outgoing president’s offline rhetoric. As a result, online groups such as QAnon engaged in extra political participation beyond the traditional platforms. This research explores the link between offline political speech and online extra-representational participation by examining Twitter within the context of the January 6 insurrection. Using a mixed-methods approach of quantitative and qualitative thematic analyses, the study combines offline speech information with Twitter data during key speech addresses leading up to the date of the insurrection; exploring the link between Trump’s offline speeches and QAnon’s hashtags across a 3-day timeframe. We find that links between online extra-representational participation and offline political speech exist. This research illuminates this phenomenon and offers policy implications for the role of online messaging as a tool of political mobilization.

Claire Seungeun Lee, Juan Merizalde, John D. Colautti, Jisun An and Haewoon Kwak

Frontiers in Sociology, 2022

Press coverage-PsyPost

15+ papers citing this work (Google scholar)

Understanding Toxicity Triggers on Reddit in the Context of Singapore

computational social science AI/ML/NLP social media online harm

While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world. In this work, we demonstrate that 1) it is possible to detect toxicity triggers in an Asian online community, and 2) toxicity triggers can be strikingly different between Western and Eastern contexts.

Yun Yu Chong, Haewoon Kwak

Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM), 2022 (short)

Press coverage-AI Ethics Brief Newsletter by Montreal AI Ethics Institute

10+ papers citing this work (Google scholar)

Predicting Anti-Asian Hateful Users on Twitter during COVID-19

computational social science AI/ML/NLP social media online harm

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups – those who posted anti-Asian slurs and those who did not – with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian slurs. …

Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn

Findings of the Association for Computational Linguistics EMNLP 2021

Code repo (github)

20+ papers citing this work (Google scholar)

FrameAxis: characterizing microframe bias and intensity with word embedding

computational social science AI/ML/NLP dataset/tool bias/fairness

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes (“microframes”) that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. …

Haewoon Kwak, Jisun An, Elise Jing, Yong-Yeol Ahn

PeerJ Computer Science 7:e644, 2021

Code repo (github)

25+ papers citing this work (Google scholar)

Populist Supporters on Reddit: A Comparison of Content and Behavioral Patterns Within Publics of Supporters of Donald Trump and Hillary Clinton

computational social science political science social media

We investigate differences along these dimensions on the online forum Reddit by comparing linguistic patterns and content of comments in two subreddits focusing on a populist, Donald Trump (/r/The_Donald), and a center-left politician, Hillary Clinton (/r/hillaryclinton), during the 2016 U.S. presidential election campaign.

Andreas Jungherr, Oliver Posegga, Jisun An

Social Science Computer Review. March 2021.

15+ papers citing this work (Google scholar)

“Trust Me, I Have a Ph.D.”: A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities

computational social science AI/ML/NLP social media user engagement

We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the “transferred” social status affects the interactions among the users.

Kunwoo Park, Haewoon Kwak, Hyunho Song, Meeyoung Cha

Proceedings of the 14th International AAAI Conference on Web and Social Media (ICWSM), 2020

5+ papers citing this work (Google scholar)

Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions

computational social science AI/ML/NLP social media online harm

We define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger.

Hind Almerekhi, Haewoon Kwak, Bernard Jim Jansen, Joni Salminen (short)

Proceedings of The Web Conference (WWW), 2020

35+ papers citing this work (Google scholar)

Going beyond accuracy: estimating homophily in social networks using predictions

computational social science

We show that estimating homophily in a network can be viewed as a dyadic prediction problem, and that homophily estimates are unbiased when dyad-level residuals sum to zero in the network. Then, we propose a novel “ego-alter” modeling approach that outperforms standard node and dyad classification strategies.

George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy (preprint)

arXiv preprint arXiv:2001.11171, 2020

Gender and Racial Diversity in Commercial Brands' Advertising Images on Social Media

computational social science social media bias/fairness

Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook.

Jisun An, Haewoon Kwak

Proceedings of Social Informatics (SocInfo), 2019

Best Paper Award

20+ papers citing this work (Google scholar)

Political Discussions in Homogeneous and Cross-Cutting Communication Spaces

computational social science political science social media

We use Reddit to explore the nature of political discussionsin homogeneous and cross-cutting communication spaces. Inparticular, we develop an analytical template to studyinter-actionandlinguistic patternswithin and between politicallyhomogeneous and heterogeneous communication spaces. Ouranalyses reveal different behavioral patterns in homogeneousand cross-cutting communications spaces.

Jisun An, Haewoon Kwak, Oliver Posegga, Andreas Jungherr

Proceedings of the 13th International AAAI Conference on Web and Social Media (ICWSM), 2019

65+ papers citing this work (Google scholar)

Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media

computational social science social media online harm

We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset.

Joni Salminen, Hind Almerekhi, Milica Milenković, Soon-gyo Jung, Jisun An, Haewoon Kwak, Bernard J. Jansen

Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM), 2018

140+ papers citing this work (Google scholar)

Identifying Regional Trends in Avatar Customization

computational social science game analytics HCI

We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset.

Peter Mawhorter, Sercan Şengün, Haewoon Kwak, D. Fox Harrell

IEEE Transactions on Games, 10(2), 2018

SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment

computational social science AI/ML/NLP dataset/tool

We propose SemAxis, a simple yet powerful framework to characterize word semantics using many semantic axes in word-vector spaces beyond sentiment. We demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SemAxis outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.

Jisun An, Haewoon Kwak, Yong-Yeol Ahn

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 2018

50+ papers citing this work (Google scholar)

What We Read, What We Search: Media Attention and Public Attention among 193 Countries

computational social science computational journalism network science

We investigate the alignment of international attention of news media organizations within 193 countries with the expressed international interests of the public within those same countries from March 7, 2016 to April 14, 2017. We collect fourteen months of longitudinal data of online news from Unfiltered News and web search volume data from Google Trends and build a multiplex network of media attention and public attention in order to study its structural and dynamic properties.

Haewoon Kwak, Jisun An, Joni Salminen, Soon-Gyo Jung, Bernard J. Jansen.

Proceedings of the 2018 World Wide Web Conference (WWW), 2018

20+ papers citing this work (Google scholar)

What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?

computational social science social media online harm

We provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls

Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn

Companion Proceedings of the The Web Conference (WWW), 2018

Press coverage-New Scientist, and Vice

280+ papers citing this work (Google scholar)

I Would Not Plant Apple Trees If the World Will Be Wiped: Analyzing Hundreds of Millions of Behavioral Records of Players During an MMORPG Beta Test

computational social science game analytics

We use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation-at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players’ in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge.

Ah Reum Kang, Jeremy Blackburn, Haewoon Kwak, Huy Kang Kim

Proceedings of the 26th International Conference on World Wide Web (WWW) Companion, 2017

Press coverage-New Scientist, IFL Science, PC Gamer, Massively OK, El Confidencial, Joongang Ilbo, and so on.

10+ papers citing this work (Google scholar)

High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea

computational social science social media

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis.

Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo

Scientific Reports 6, Article number 32920 (2016)

130+ papers citing this work (Google scholar)

Are You Charlie or Ahmed? Cultural Pluralism in Charlie Hebdo Response on Twitter

computational social science social media

We study the response to the Charlie Hebdo shootings of January 7, 2015 on Twitter across the globe. We ask whether the stances on the issue of freedom of speech can be modeled using established sociological theories, including Huntington’s culturalist Clash of Civilizations, and those taking into consideration social context, including Density and Interdependence theories. We find support for Huntington’s culturalist explanation, in that the established traditions and norms of one’s “civilization” predetermine some of one’s opinion.

Jisun An, Haewoon Kwak, Yelena Mejova, Sonia Alonso Saenz De Oger, Braulio Gomez Fortes

Proceeding of the 10th International Conference on Web and Social Media (ICWSM), 2016

45+ papers citing this work (Google scholar)

Recommending investors for crowdfunding projects

computational social science

One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. We set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors’ Twitter accounts for any given project.

Jisun An, Daniele Quercia, Jon Crowcroft

Proceedings of the 23rd international conference on World wide web (WWW), 2014

Press coverage-FastCompany

100+ papers citing this work (Google scholar)

Fragile Online Relationship: a First Look at Unfollow Dynamics in Twitter

computational social science network science social media HCI

We analyze the dynamics of the behavior known as ‘unfollow’ in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees’ informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results.

Haewoon Kwak, Hyunwoo Chun, Sue Moon

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 2011.

Press coverage - Kyunghyang Shinmun

150+ papers citing this work (Google scholar)

What is Twitter, a Social Network or a News Media?

computational social science network science social media

We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one’s tweets.

Haewoon Kwak, Changhyun Lee, Hosung Park, Sue Moon

Proceedings of the 19th international conference on World wide web (WWW), 2010.

Press coverage - Mashable Op-Ed, ReadWrite, The Guardian, PC News, Chosun Ilbo, DongA Ilbo

10k+ papers citing this work (Google scholar)