bluuebunny/crossref_metadata_2025 huggingface dataset:
Context-Based Quotation Recommendation
Authors: MacLaughlin Ansel , Chen Tao , Ayan Burcu Karagol , Roth Dan
Published: November 2022

While composing a new document, anything from a news article to an email or essay, authors often utilize direct quotes from a variety of sources. Although an author may know what point they would like to make, selecting an appropriate quote for the specific context may be time-consuming and difficult. We therefore propose a novel context-aware quote recommendation system which utilizes the content an author has already written to generate a ranked list of quotable paragraphs and spans of tokens from a given source document.

We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics. Qualitative analyses show the difficulty of the paragraph and span recommendation tasks and confirm the quotability of the best BERT model's predictions, even if they are not the true selected quotes from the original news articles.
No Walk in the Park: The Viability and Fairness of Social Media Analysis for Parks and Recreational Policy Making
Authors: Mashhadi Afra , Winder Samantha G. , Lia Emilia H. , Wood Spencer A.
Published: November 2022

Recent years have seen an increase in the use of social me-dia for various decision-making purposes in the context ofurban computing and smart cities, including management ofpublic parks. However, as such decision-making tasks arebecoming more autonomous, a critical concern that arises isthe extent to which such analysis are fair and inclusive. Inthis article, we examine the biases that exist in social media analysis pipelines that focus on researching recreationalvisits to urban parks. More precisely, we demonstrate thepotential biases that exist in different data sources for esti-mating the number and demographics of visitors through acomparison of image content shared on Instagram and Flickrfrom 10 urban parks in Seattle, Washington. We draw a com-parison against a traditional intercept survey of park visitorsand a multi-modal city-wide survey of residents. We eval-uate the viability of using more complex AI facial recognition algorithms and its capabilities for removing some ofthe presented biases. We evaluate the AI algorithm throughthe lens of algorithmic fairness and its impact on sensitivedemographic groups. We show that despite the promisingresults, there are new sets of concerns regarding equity thatarise when we use AI algorithms.
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
Authors: Rekabsaz Navid , West Robert , Henderson James , Hanbury Allan
Published: November 2022

Text corpora are widely used resources for measuring societal biases and stereotypes. The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as genders). In this study, we show that, depending on what one aims to quantify as bias, this commonly-used approach can introduce non-relevant concepts into bias measurement. We propose an alternative approach to bias measurement utilizing the smoothed first-order co-occurrence relations between the word and the representative concept words, which we derive by reconstructing the co-occurrence estimates inherent in word embedding models. We compare these approaches by conducting several experiments on the scenario of measuring gender bias of occupational words, according to an English Wikipedia corpus. Our experiments show higher correlations of the measured gender bias with the actual gender bias statistics of the U.S. job market – on two collections and with a variety of word embedding models – using the first-order approach in comparison with the vector similarity-based approaches. The first-order approach also suggests a more severe bias towards female in a few specific occupations than the other approaches.
Automatic Discovery of Political Meme Genres with Diverse Appearances
Authors: Theisen William , Brogan Joel , Thomas Pamela Bilo , Moreira Daniel , Phoa Pascal , Weninger Tim , Scheirer Walter
Published: November 2022

Forms of human communication are not static --- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events.
A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.
The Effect of Moderation on Online Mental Health Conversations
Authors: Wadden David , August Tal , Li Qisheng , Althoff Tim
Published: November 2022

Many people struggling with mental health issues are unable to access adequate care due to high costs and a shortage of mental health professionals, leading to a global mental health crisis. Online mental health communities can help mitigate this crisis by offering a scalable, easily accessible alternative to in-person sessions with therapists or support groups. However, people seeking emotional or psychological support online may be especially vulnerable to the kinds of antisocial behavior that sometimes occur in online discussions. Moderation can improve online discourse quality, but we lack an understanding of its effects on online mental health conversations.In this work, we leveraged a natural experiment, occurring across 200,000 messages from 7,000 online mental health conversations, to evaluate the effects of moderation on online mental health discussions. We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations. The presence of a moderator increased user engagement, encouraged users to discuss negative emotions more candidly, and dramatically reduced bad behavior among chat participants. Moderation also encouraged stronger linguistic coordination, which is indicative of trust building. In addition, moderators who remained active in conversations were especially successful in keeping conversations on topic. Our findings suggest that moderation can serve as a valuable tool to improve the efficacy and safety of online mental health conversations. Based on these findings, we discuss implications and trade-offs involved in designing effective online spaces for mental health support.
Representation of Music Creators on Wikipedia, Differences in Gender and Genre
Authors: Wang Alice , Pappu Aasish , Cramer Henriette
Published: November 2022

Wikipedia is not only the world’s largest online encyclopedia and among the most frequented websites, but provides important data leveraged by many popular services and products. Since Wikipedia data is ubiquitously encountered, it is important to evaluate its coverage of content and identify data gaps that may exist. Here, we evaluate Wikipedia’s coverage of the music domain, which is one of the most popular topics.Particularly, we compile the most prominent 50,000 music artists (by streaming popularity on a large online streaming platform) and determine whether each artist has a Wikipedia page. We first show that streaming popularity correlates with Wikipedia representation – while 90% of the top one thousand most popularly streamed artists are on Wikipedia, the chance of being on Wikipedia drops to 50% after the ten thousandth artist. Next, we examine the Wikipedia coverage of artists of different gender and genre, while controlling for popularity.We also examine, for artists that are on Wikipedia, the amount of content, frequency of edits, and Pagerank for their pages.We uncover large differences in representation for artists of different genres; for the same popularity level ,hiphop, latin, and dance/electronic artists are most lacking in representation while rock artists have approximately twice as much representation. With respect to gender, while female artists are under represented in the top of the music industry itself, male artists were less likely represented on Wikipedia relative to the female artists in this study’s top sample, suggesting inter-action with genre and visibility of select superstars.
Misinformation Adoption or Rejection in the Era of COVID-19
Authors: Weinzierl Maxwell , Hopfer Suellen , Harabagiu Sanda M.
Published: November 2022

The COVID-19 pandemic has led to a misinformation avalanche on social media, which produced confusion and insecurity in netizens. Learning how to automatically recognize adoption or rejection of misinformation about COVID-19 enables the understanding of the effects of exposure to misinformation and the threats it presents. By casting the problem of recognizing misinformation adoption or rejection as stance classification, we have designed a neural language processing system operating on micro-blogs which takes advantage of Graph Attention Networks relying on lexical, emotion, and semantic knowledge to discern the stance of each micro-blog with respect to COVID-19 misinformation. This enabled us not only to obtain promising results, but also allowed us to use a taxonomy of COVID-19 misinformation themes and concerns to characterize the misinformation adoption or rejection that can be best recognized automatically.
Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives
Authors: Wu Siqi , Resnick Paul
Published: November 2022

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.
Understanding the Invitation Acceptance in Agent-initiated Social E-commerce
Authors: Xu Fengli , Zhang Guozhen , Yuan Yuan , Huang Hongjia , Yang Diyi , Jin Depeng , Li Yong
Published: November 2022

Recent years have witnessed the phenomenal success of a new form of social e-commerce platforms, which transforms users into agents by motivating them with monetary rewards to promote products and invite new agents through their social network. Despite their rapid growth, there is still inadequate evidence on how such agent invitation works. This research examines what factors affect the agent invitation process. We first conduct a qualitative user study, where we identify four potential mechanisms related to the agent invitation: social conformity, social enrichment, refusal avoidance, and benefit-cost trade-off. Leveraging the empirical data collected from one of the largest social e-commerce platforms in China - Beidian, we operationalize a set of behavioral indicators of these mechanisms and further develop machine learning models to predict users' reactions to invitations. We found that the identified four mechanisms contribute to the high success rate of agent invitations differently. We conclude by discussing the implications of our findings and their potential benefits to real-world applications.
Discourse Parsing for Contentious, Non-Convergent Online Discussions
Authors: Zakharov Stepan , Hadar Omri , Hakak Tovit , Grossman Dina , Ben-David Kolikant Yifat , Tsur Oren
Published: November 2022

Online discourse is often perceived as polarized and unproductive. While some conversational discourse parsing frameworks are available, they do not naturally lend themselves to the analysis of contentious and polarizing discussions. Inspired by the Bakhtinian theory of Dialogism, we propose a novel theoretical and computational framework, better suited for non-convergent discussions. We redefine the measure of a successful discussion, and develop a novel discourse annotation schema which reflects a hierarchy of discursive strategies. We consider an array of classification models -- from Logistic Regression to BERT. We also consider various feature types and representations, e.g., LIWC categories, standard embeddings, conversational sequences, and non-conversational discourse markers learnt separately. Given the 31 labels in the tagset, an average F-Score of 0.61 is achieved if we allow a different model for each tag, and 0.526 with a single model. The promising results achieved in annotating discussions according to the proposed schema paves the way for a number of downstream tasks and applications such as early detection of discussion trajectories, active moderation of open discussions, and teacher-assistive bots. Finally, we share the first labeled dataset of contentious non-convergent online discussions.
Fair Representation Learning for Heterogeneous Information Networks
Authors: Zeng Ziqian , Islam Rashidul , Keya Kamrun Naher , Foulds James , Song Yangqiu , Pan Shimei
Published: November 2022

Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representation learning methods for Heterogeneous Information Networks (HINs), fundamental building blocks used in complex network mining, have socially consequential applications such as automated career counseling, but there have been few attempts to ensure that it will not encode or amplify harmful biases, e.g. sexism in the job market. To address this gap, we propose a comprehensive set of de-biasing methods for fair HINs representation learning, including sampling-based, projection-based, and graph neural networks (GNNs)-based techniques. We systematically study the behavior of these algorithms, especially their capability in balancing the trade-off between fairness and prediction accuracy. We evaluate the performance of the proposed methods in an automated career counseling application where we mitigate gender bias in career recommendation. Based on the evaluation results on two datasets, we identify the most effective fair HINs representation learning techniques under different conditions.
Shifting Our Awareness, Taking Back Tags: Temporal Changes in Computer Vision Services’ Social Behaviors
Authors: Barlas Pınar , Krahn Maximilian , Kleanthous Styliani , Kyriakou Kyriakos , Otterbacher Jahna
Published: November 2022

Much attention has been on the behaviors of computer vision services when describing images of people. Audits have revealed rampant biases that could lead to harm, when services are used by developers and researchers. We focus on temporal auditing, replicating experiments originally conducted three years ago. We document the changes observed over time, relating this to the growing awareness of structural oppression and the need to align technology with social values. While we document some positive changes in the services’ behaviors, such as increased accuracy in the use of gender-related tags overall, we also replicate findings concerning larger error rates for images of Black individuals. In addition, we find cases of increased use of inferential tags (e.g., emotions), which are often sensitive. The analysis underscores the difficulty in following changes in services’ behaviors over time, and the need for more oversight of such services.
Effect of Popularity Shocks on User Behaviour
Authors: Gurjar Omkar , Bansal Tanmay , Jangra Hitkul , Lamba Hemank , Kumaraguru Ponnurangam
Published: November 2022

Users often post on content-sharing platforms in the hope of attracting high engagement from viewers. Some posts receive unusual attention and go "viral", eliciting a significant response (likes, views, shares) to the creator in the form of popularity shocks. Past theories have suggested a sense of reputation as one of the key drivers of online activity and the tendency of users to repeat fruitful behaviors. Based on these, we theorize popularity shocks to be linked with changes in the behavior of users. In this paper, we propose a framework to study the changes in user activity in terms of frequency of posting and content posted around popularity shocks. Further, given the sudden nature of their occurrence, we look into the survival durations of effects associated with these shocks. We observe that popularity shocks lead to an increase in the posting frequency of users, and users alter their content to match with the one which resulted in the shock. Also, it is found that shocks are tough to maintain, with effects fading within a few days for most users. High response from viewers and diversification of content posted is found to be linked with longer survival durations of the shock effects. We believe our work fills the gap related to observing users' online behavior exposed to sudden popularity and has widespread implications for platforms, users, and brands involved in marketing on such platforms.
Are Proactive Interventions for Reddit Communities Feasible?
Authors: Habib Hussam , Musa Maaz Bin , Zaffar Muhammad Fareed , Nithyanand Rishab
Published: November 2022

Reddit has found its communities playing a prominent role in originating and propagating problematic sociopolitical discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.
The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter
Authors: Hasan Rakibul , Cheyre Cristobal , Ahn Yong-Yeol , Hoyle Roberto , Kapadia Apu
Published: November 2022

On social media, due to complex interactions between users' attention and recommendation algorithms, the visibility of users' posts can be unpredictable and vary wildly, sometimes creating unexpected viral events for `ordinary’ users. How do such events affect users' subsequent behaviors and long-term visibility on the platform? We investigate these questions following a matching-based framework using a dataset comprised of tweeting activities and follower graph changes of 17,157 scientists on Twitter. We identified scientists who experienced `unusual' virality for the first time in their profile lifespan (`viral' group) and quantified how viral events influence tweeting behaviors and popularity (as measured through follower statistics). After virality, the viral group increased tweeting frequency, their tweets became more objective and focused on fewer topics, and expressed more positive sentiment relative to their pre-virality tweets. Also, their post-virality tweets were more aligned with their professional expertise and similar to the viral tweet compared to past tweets. Finally, the viral group gained more followers in both the short and long terms compared to a control group.
Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?
Authors: Jiang Julie , Murrugara-Llerena Nils , Bos Maarten W. , Liu Yozen , Shah Neil , Neves Leonardo , Barbieri Francesco
Published: November 2022

The environment we are in can affect our mood and behavior. One environmental factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood. Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.
The Web We Weave: Untangling the Social Graph of the IETF
Authors: Khare Prashant , Karan Mladen , McQuistin Stephen , Perkins Colin , Tyson Gareth , Purver Matthew , Healey Patrick , Castro Ignacio
Published: November 2022

The Internet Engineering Task Force (IETF) has developed many of the technical standards that underpin the Internet. The standards development process followed by the IETF is open and consensus-driven, but is inherently both a social and political activity, and latent influential structures might exist within the community. Exploring and understanding these structures is essential to ensuring the IETF’s resilience and openness. We use network analysis to explore the social graph of IETF participants, based on public email discussions and co-author relationships, and the influence of key contributors. We show that a small core of participants dominates: the top 10% contribute almost half (43.75%) of the emails and come from a relatively small group of organisations. On the other hand, we also find that influence has become relatively more decentralised with time. IETF participants also propose and work on drafts that are either adopted by a working group for further refinement or get rejected at an early stage. Using the social graph features combined with email text features, we perform regression analysis to understand the effect of user influence on the success of new work being adopted by the IETF. Our findings shed useful insights into the behavior of participants across time, correlation between influence and success in draft adoption, and the significance of affiliated organisations in the authorship of drafts.
Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users
Authors: Poddar Soham , Mondal Mainack , Misra Janardan , Ganguly Niloy , Ghosh Saptarshi
Published: November 2022

Today, administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of pro-vax and anti-vax users strongly expressing their supports and concerns regarding the vaccines in online social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier to categorize users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. We specifically identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance.
Characterizing User Susceptibility to COVID-19 Misinformation on Twitter
Authors: Teng Xian , Lin Yu-Ru , Chung Wen-Ting , Li Ang , Kovashka Adriana
Published: November 2022

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 misinfodemic, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.
Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community
Authors: Wich Maximilian , Gorniak Adrian , Eder Tobias , Bartmann Daniel , Çakici Burak Enes , Groh Georg
Published: November 2022

Because traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate user content to the same degree. One popular platform relevant for the German community is Telegram for which limited research efforts have been made so far.

This study aimed to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of German speaking communities focused on hateful content on the Telegram platform. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.
Limits of Multilayer Diffusion Network Inference in Social Media Research
Authors: Xia Yan , Chen Ted Hsuan Yun , Kivelä Mikko
Published: November 2022

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, language, or attitude. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.
Know It to Defeat It: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis
Authors: Yang Wenjie , Wang Sitong , Peng Zhenhui , Shi Chuhan , Ma Xiaojuan , Yang Diyi
Published: November 2022

Health-related rumors being spread online during a public crisis may pose a serious threat to people's well-being. Existing crisis informatics research lacks in-depth insights into the characteristics of health rumors and the efforts to debunk them on social media in a pandemic. To fill this gap, we conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We further explore how four kinds of social media users (i.e., government, media, organization, and individual) combat health rumors, and identify their preferred way of sharing the debunking information and the key rhetoric strategies used in the process. We examine the relationship between debunking and rumor discussions using a Granger causality approach, and show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies according to rumor type and debunker. Our results can provide insights into crisis informatics and risk management on social media in pandemic settings.
FactDrill: A Data Repository of Fact-Checked Social Media Content to Study Fake News Incidents in India
Authors: Singhal Shivangi , Shah Rajiv Ratn , Kumaraguru Ponnurangam
Published: November 2022

The production and circulation of fake content in India is a rising problem. There is a dire need to investigate the false claims made in public. This paper presents a dataset containing 22,435 fact-checked social media content to study fake news incidents in India. The dataset comprises news stories from 2013 to 2020, covering 13 different languages spoken in the country. We present a detailed description of the 14 different attributes present in the dataset. We also present the detailed characterisation of three M’s (multi-lingual, multi-media, multi-domain) in the FactDrill dataset. Lastly, we present some potential use cases of the dataset. We expect that the dataset will be a valuable resource to understand the dynamics of fake content in a multi-lingual setting in India.
Who Is Missing? Characterizing the Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus
Authors: Kwak Haewoon , An Jisun , Park Kunwoo
Published: November 2022

A conversation corpus is essential to build interactive AI applications.
However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora.
In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.
Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency
Authors: Loon Austin van , Giorgi Salvatore , Willer Robb , Eichstaedt Johannes
Published: November 2022

The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., ‘pleasant’ and ‘unpleasant’ words). We show that anti-Black WEAT estimates from geo-tagged social media data at the level of metropolitan statistical areas strongly correlate with several measures of racial animus—even when controlling for sociodemographic covariates. However, we also show that every one of these correlations is explained by a third variable: the frequency of Black names in the underlying corpora relative to White names. This occurs because word embeddings tend to group positive (negative) words and frequent (rare) words together in the estimated semantic space. As the frequency of Black names on social media is strongly correlated with Black Americans’ prevalence in the population, this results in spuriously high anti-Black WEAT estimates wherever few Black Americans live. This suggests that research using the WEAT to measure bias should consider term frequency, and also demonstrates the potential consequences of using black-box models like word embeddings to study human cognition and behavior.
Wikipedian Self-Governance in Action: Motivating the Policy Lens
Authors: Beschastnikh Ivan , Kriplean Travis , McDonald David
Published: November 2022

While previous studies have used the Wikipedia dataset to provide an understanding of its growth, there have been few attempts to quantitatively analyze the establishment and evolution of the rich social practices that support this editing community. One such social practice is the enactment and creation of Wikipedian policies. We focus on the enactment of policies in discussions on the talk pages that accompany each article. These policy citations are a valuable micro-to-macro connection between everyday action, communal norms and the governance structure of Wikipedia. We find that policies are widely used by registered users and administrators, that their use is converging and stabilizing in and across these groups, and that their use illustrates the growing importance of certain classes of work, in particular source attribution. We also find that participation in Wikipedia's governance structure is inclusionary in practice.
Space Planning for Online Community
Authors: Fisher Danyel , Turner Tammara , Smith Marc
Published: November 2022

Several years of consulting with online community hosts and managers have highlighted a variety of issues that recur across many online community development efforts. We summarize those issues in eight points that have functioned as useful guidelines to working with online communities, particularly within a corporate context. These recommendations focus on the location and purpose of the community, the monitoring of social activity within the space, the provision of feedback to participants and the organization and maintenance of the space. While this collection is particularly focused on issues relevant to community organizers closely involved in starting, maintaining or growing online communities, its principles are generally applicable for analyzing and understanding the dynamics within a variety of communities.
Computational Metaphor Identification in Communities of Blogs
Authors: Eric Baumer Eric Baumer , Tomlinson Bill
Published: November 2022

This poster presents a computational analysis of conceptual metaphors in a community of political blogs. Like sentiment analysis or opinion extraction, computational metaphor identification can provide a means of understanding the particular framings or conceptualizations used in a community. This poster includes an overview of the implementation and a summary of results.
iBlogVis: An Interactive Blog Visualization Tool
Authors: Indratmo J. , Vassileva Julita
Published: November 2022

We developed and evaluated a visualization tool for browsing individual blog archives. In our study, we solicited qualitative feedback from the participants to explore the reasons behind their usability ratings. This feedback reveals factors that are deemed important in selecting entries to read, and adds to the understanding of blogging practices. Based on our analysis, we propose two design principles to complement the current blog interfaces: (1) A blog should provide a rich overview of its content to ease information exploration; and (2) A blog should utilize social interaction history to help users find potentially useful entries.
Preliminary Semantic Analysis of Political Blogs
Authors: Jiang Maojin , Argamon Shlomo
Published: November 2022

In this paper, we present a series of semantic analyses of words in political blogs in the setting of categorization of two opposite political orientations: liberal vs. conservative. We classify nouns, verbs, adjectives and adverbs into semantic categories by using the General Inquirer dictionary. Then distributions of these categories and correlations among them are examined both within and between blogs of the two opposite political leanings. Results show that although words of certain categories tend to appear together while others do not within blogs of a political leaning, the semantic category distribution of words used by left-wing bloggers is very similar to those by right-wing bloggers, suggesting single words alone do not account for major difference between these two major categories of blogs. Lastly, by examining preliminary results of association rule mining of nouns, verbs, adjectives and adverbs in sentences, we posit that the similarity and/or difference between blogs of opposite political orientations can be detected by extracting opinion expressions around collocation of nouns and verbs (together with modifiers).
The MultiRank Bootstrap Algorithm: Semi-Supervised Political Blog Classification and Ranking Using Semi-Supervised Link Classification
Authors: Lin Frank , Cohen William
Published: November 2022

We present a new semi-supervised learning algorithm for classifying political blogs in a blog network and ranking them within predicted classes. We test our algorithm on two datasets and achieve classification accuracy of 81.9% and 84.6% using only 2 seed blogs.
Blog Link Classification
Authors: Martineau Justin , Hurst Matthew
Published: November 2022

Blog links raise three key questions: Why did the author make the link, what exactly is he pointing at, and what does he feel about it? In response to these questions we introduce a link model with three fundamental descriptive dimensions where each dimension is designed to answer one question. We believe the answers to these questions can be utilized to improve search engine results for blogs. While proving this is outside the scope of this paper, we do prove that knowing the rhetorical role of a link helps determine what the author was pointing at and how he feels about it.
Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering
Authors: Wong Davin , Bingham Ella , Hyvönen Saara
Published: November 2022

We investigate two recommendation approaches suitable for online multimedia sharing services. Our first approach, UserRank, recommends items by global interestingness irrespective of user preferences and is based on the analysis of ownership and evaluation link structure. We also present a personalized interestingness algorithm that combines UserRank with collaborative filtering which enables a single parameter to control the degree of personalization in the recommendations. Our initial results from an informal user study are encouraging.
Gephi: An Open Source Software for Exploring and Manipulating Networks
Authors: Bastian Mathieu , Heymann Sebastien , Jacomy Mathieu
Published: November 2022

Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results.  We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, filtering, navigating, manipulating and clustering. Finally, by presenting dynamic features of Gephi, we highlight key aspects of dynamic network visualization.
Diversity of User Activity and Content Quality in Online Communities
Authors: Hogg Tad , Szabo Gabor
Published: November 2022

Web sites where users create and rate content display long-tailed distributions in many aspects of behavior. Using one such community site, Essembly, we propose and evaluate mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available to each user. For Essembly, we find the long-tails arise from large differences among user activity rates, the time users devote to the site, and qualities of the rated content. The models not only explain overall behavior but also allow estimating the properties of users and content from their early behaviors.
A Social Identity Approach to Identify Familiar Strangers in a Social Network
Authors: Agarwal Nitin , Liu Huan , Murthy Sudheendra , Sen Arunabha , Wang Xufei
Published: November 2022

We present a novel problem of searching for ‘familiar strangers’ in a social network. Familiar strangers are individuals who are not directly connected but exhibit some similarity. The power-law nature of social networks determines that majority of individuals are directly connected with a small number of fellow individuals, and similar individuals can be largely unknown to each other. Moreover, the individuals of a social network have only a local view of the network, which makes the problem of aggregating these familiar strangers a challenge. In this work, we formulate the problem, show why it is significant to address the challenge, and present an approach that innovatively employs the social identities of the individuals with competitive approaches. A blogger and citation network are used to showcase technical details and empirical results with related issues and future work.
What Are They Blogging About? Personality, Topic and Motivation in Blogs
Authors: Gill Alastair , Nowson Scott , Oberlander Jon
Published: November 2022

Personal weblogs (blogs), provide individuals with the opportunity to write freely and express themselves online in the presence of others. In such situations, what do bloggers write about, and what are their motivations for blogging? Using a large blog corpus annotated with the LIWC text analysis program, we examine the content of blogs to provide insight into the role of personality in motivation for blogging. As predicted, we find that highly Neurotic authors use blogs to serve a cathartic or auto-therapeutic function, and reflect mainly upon themselves and negative emotions. Highly Extraverted blog authors, as expected, use blogs to document their lives at a high level, and uniquely interact directly with the reader. Additionally Extraverts use blogs to vent both positive and negative emotions. Bloggers who are high scorers on the Openness trait are more concerned with leisure activities, although they are more evaluative than intellectual, whereas high Conscientiousness bloggers tend to report daily life – and work – around them. As in other contexts, expressing positive rather than negative emotions is associated with high Agreeableness, but that trait is associated with self reference to a greater degree in blogs than elsewhere. In general, findings are consistent with other contexts indicating that bloggers tend to adapt to the possibilities of the medium, rather than try to present themselves differently.