bluuebunny/crossref_metadata_2025 huggingface dataset:
Bias Misperceived:The Role of Partisanship and Misinformation in YouTube Comment Moderation
Authors: Jiang Shan , Robertson Ronald E. , Wilson Christo
Published: November 2022

Social media platforms have been the subject of controversy and scrutiny due to the spread of hateful content. To address this problem, the platforms implement content moderation using a mix of human and algorithmic processes. However, content moderation itself has lead to further accusations against the platforms of political bias. In this study, we investigate how channel partisanship and video misinformation affect the likelihood of comment moderation on YouTube. Using a dataset of 84,068 comments on 258 videos, we find that although comments on right-leaning videos are more heavily moderated from a correlational perspective, we find no evidence to support claims of political bias when using a causal model that controls for common confounders (e.g., hate speech). Additionally, we find that comments are more likely to be moderated if the video channel is ideologically extreme, if the video content is false, and if the comments were posted after a fact-check.
“Woman-Metal-White vs Man-Dress-Shorts”:Combining Social, Temporal and Image Signals to Understand Popularity of Pinterest Fashion Boards
Authors: Maity Suman Kalyan , Chaudhary Anshit , Mukherjee Animesh
Published: November 2022

Pinterest is a popular photo sharing website. Fashion is one the most popular and content generating category on this platform. Most of the popular fashion brands and designers use boards on Pinterest for showcasing their products. However, the characteristics of popular fashion boards are not well-known. These characteristics can be used for predicting popularity of a nascent board. Further, newly formed boards can organize their content in a way similar to the popular fashion boards to garner enhanced popularity. What properties on these fashion boards determine their popularity? Can these properties be systematically quantified? In this paper, we show how social, temporal and image signals can together help in characterizing the popular fashion boards. In particular, we study the sharing/borrowing behavior of pins and the image content characteristics of the fashion boards. We analyze the sharing behavior using social and temporal signals, and propose six novel yet simple metrics: originality score, retention coefficients, production coefficients, inter-copying time, duration of sharing and speed coefficients. We further study the image based content properties by extracting fashion, color and gender terms embedded in the pin images. We observe significant differences across the popular (highly followed or highly ranked by the experts) and the unpopular (less followed) boards. We then use these characteristic features to early predict the popularity of a board and achieve a high correlation of 0.874 with low RMSE value. Our key observation is that likes and repin retention coefficients are the most discriminatory factors of a board’s popularity apart from the usage of various color, gender and fashion terms.
SENPAI: Supporting Exploratory Text Analysis through Semantic & Syntactic Pattern Inspection
Authors: Samory Mattia , Mitra Tanushree
Published: November 2022

Analyzing language for social computing tasks requires looking beyond individual words. For example, the word “please” generally signals politeness, but more so together with modal verbs (“could you please...”) than without (“please do this.”). Combining semantics and syntax into rich textual patterns is essential to capturing these nuances. What are the relevant patterns for a task, and how to find them? NLP practitioners choose patterns informed by theory, and find them through computational models. However, few tools allow identifying rich patterns without NLP expertise. We introduce SENPAI, a novel tool that discovers combined semantic and syntactic patterns. SENPAI fuses neural embeddings, dependency parsing, and graph mining to surface patterns directly from data. We apply SENPAI to measure credibility, politeness, and sentiment in text. Quantitatively, models powered by SENPAI perform similarly to theoretically-motivated ones. Qualitatively, SENPAI discovers patterns that are interpretable and meaningful. SENPAI enables building computational models without NLP expertise and discovering new linguistic constructs.
Measuring the Importance of User-Generated Content to Search Engines
Authors: Vincent Nicholas , Johnson Isaac , Sheehan Patrick , Hecht Brent
Published: November 2022

Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highlyconsequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies?
Eliciting New Wikipedia Users’ Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Authors: Yazdanian Ramtin , Zia Leila , Morgan Jonathan , Mansurov Bahodir , West Robert
Published: November 2022

Every day, thousands of users sign up as new Wikipedia contributors. Once joined, these users have to decide which articles to contribute to, which users to seek out and learn from or collaborate with, etc. Any such task is a hard and potentially frustrating one given the sheer size of Wikipedia. Supporting newcomers in their first steps by recommending articles they would enjoy editing or editors they would enjoy collaborating with is thus a promising route toward converting them into long-term contributors. Standard recommender systems, however, rely on users’ histories of previous interactions with the platform. As such, these systems cannot make high-quality recommendations to newcomers without any previous interactions—the so-called cold-start problem. The present paper addresses the cold-start problem on Wikipedia by developing a method for automatically building short questionnaires that, when completed by a newly registered Wikipedia user, can be used for a variety of purposes, including article recommendations that can help new editors get started. Our questionnaires are constructed based on the text of Wikipedia articles as well as the history of contributions by the already onboarded Wikipedia editors. We assess the quality of our questionnaire-based recommendations in an offline evaluation using historical data, as well as an online evaluation with hundreds of real Wikipedia newcomers, concluding that our method provides cohesive, human-readable questions that perform well against several baselines. By addressing the cold-start problem, this work can help with the sustainable growth and maintenance of Wikipedia’s diverse editor community.
Participation of New Editors after Times of Shock on Wikipedia
Authors: Zhang Ark Fangzhou , Wang Ruihan , Blohm Eric , Budak Ceren , Robert Jr. Lionel P. , Romero Daniel M.
Published: November 2022

User participation is vital to the success of collaborative crowdsourcing platforms such as Wikipedia. Previously user participation has been studied during “normal times”. However, less is known about participation following shocks that draw attention to an article. Such events can be recruiting opportunities due to increased attention; but can also pose a threat to the quality and control of the article and drive away newcomers. We study the collaborative dynamics of Wikipedia articles after times corresponding to shocks generated by drastic increases in attention as indicated by data from Google trends. We find that participation following such events is indeed different from participation during normal times–both newcomers and incumbents participate at higher rates during shocks. We also identify collaboration dynamics that mediate the effects of shocks on continued participation after the shock. The impact of shocks on participation is mediated by the amount of negative feedback given to newcomers in the form of reverted edits and the amount of coordination editors engage in through edits of the article’s talk page.
FA-KES: A Fake News Dataset around the Syrian War
Authors: Abu Salem Fatima K. , Al Feel Roaa , Elbassuoni Shady , Jaber Mohamad , Farah May
Published: November 2022

Most currently available fake news datasets revolve around US politics, entrainment news or satire. They are typically scraped from fact-checking websites, where the articles are labeled by human experts. In this paper, we present FA-KES, a fake news dataset around the Syrian war. Given the specific nature of news reporting on incidents of wars and the lack of available sources from which manually-labeled news articles can be scraped, we believe a fake news dataset specifically constructed for this domain is crucial. To ensure a balanced dataset that covers the many facets of the Syrian war, our dataset consists of news articles from several media outlets representing mobilisation press, loyalist press, and diverse print media. To avoid the difficult and often-subjective task of manually labeling news articles as true or fake, we employ a semi-supervised fact-checking approach to label the news articles in our dataset. With the help of crowdsourcing, human contributors are prompted to extract specific and easy-to-extract information that helps match a given article to information representing “ground truth” obtained from the Syrian Violations Documentation Center. The information extracted is then used to cluster the articles into two separate sets using unsupervised machine learning. The result is a carefully annotated dataset consisting of 804 articles labeled as true or fake and that is ideal for training machine learning models to predict the credibility of news articles. Our dataset is publicly available at https://doi.org/10.5281/zenodo.2607278. Although our dataset is focused on the Syrian crisis, it can be used to train machine learning models to detect fake news in other related domains. Moreover, the framework we used to obtain the dataset is general enough to be used to build other fake news datasets around military conflicts, provided there is some corresponding ground-truth available.
To Interpret or Not to Interpret PCA? This Is Our Question
Authors: Vilenchik Dan , Yichye Barak , Abutbul Maor
Published: November 2022

Principal Component Analysis (PCA) is a central tool for analyzing data and social media data in particular. Typically, the data is projected on the first two PCs to obtain a twodimensional view, and trends and patterns are being examined. A key to making sense of the projected data is the semantic interpretation of the new axes (the PCs). To label the PCs, one usually looks at the top k vector entries in absolute value and assigns meaning according to them. The choice of k is done by “eyeballing” the vector. In this work we provide a computational framework to support this process and suggest an interpretability score, which measures how sensitive the interpretation step could be to the choice of k. Furthermore we give a visual method to choose the optimal k. We study our methodology in four social media platforms and discover that in two of them, Twitter and Instagram, interpretation can be done in a carefree manner, but in Steam and LinkedIn there is no natural labeling of the axes. This separation is clearly reflected in the interpretability score that each dataset received.
A Co-Training Model with Label Propagation on a Bipartite Graph to Identify Online Users with Disabilities
Authors: Yu Xing , Chakraborty Sunandan , Brady Erin
Published: November 2022

Collecting data from representative users with disabilities for accessibility research is time and resource consuming. With the proliferation of social media websites, many online spaces have emerged for people with disabilities. The information accumulated in such places is of great value for data collection and participant recruiting. However, there are also many active non-representative users in such online spaces such as medical practitioners, caretakers, or family members. In this work, we introduce a novel co-training model based on the homophily phenomenon observed among online users with the same disability. The model combines a variational label propagation algorithm and a naive Bayes classifier to identify online users who have the same disability. We evaluated this model on a dataset collected from Reddit and the results show improvements over traditional models.
Sharing Emotions at Scale: The Vent Dataset
Authors: Lykousas Nikolaos , Patsakis Constantinos , Kaltenbrunner Andreas , Gómez Vicenç
Published: November 2022

The continuous and increasing use of social media has enabled the expression of human thoughts, opinions, and everyday actions publicly at an unprecedented scale. We present the Vent dataset, the largest annotated dataset of text, emotions, and social connections to date. It comprises more than 33 millions of posts by nearly a million users together with their social connections. Each post has an associated emotion. There are 705 different emotions, organized in 63 “emotion categories”, forming a two-level taxonomy of affects. Our initial statistical analysis describes the global patterns of activity in the Vent platform, revealing large heterogeneities and certainly remarkable regularities regarding the use of the different emotions. We focus on the aggregated use of emotions, the temporal activity, and the social network of users, and outline possible methods to infer emotion networks based on the user activity. We also analyze the text and describe the affective landscape of Vent, finding agreements with existing (small scale) annotated corpus in terms of emotion categories and positive/negative valences. Finally, we discuss possible research questions that can be addressed from this unique dataset.
Characterising User Content on a Multi-Lingual Social Network
Authors: Agarwal Pushkal , Garimella Kiran , Joglekar Sagar , Sastry Nishanth , Tyson Gareth
Published: November 2022

Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multicultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.
The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems
Authors: Fabbri Francesco , Bonchi Francesco , Boratto Ludovico , Castillo Carlos
Published: November 2022

Evaluating (and mitigating) the potential negative effects of algorithms has become a central issue in computer science. While research on algorithmic bias in ranking systems has dealt with disparate exposure of products or individuals, less attention has been devoted to the analysis of the disparate exposure of subgroups of online users.In this paper, we investigate the visibility of minorities in people recommender systems in social networks. Specifically, we consider a bi-populated social network, i.e., a graph where the nodes belong to two different groups (majority and minority) and, by applying state-of-the-art people recommenders, we analyze how disparate visibility can be amplified or mitigated by different levels of homophily within each subgroup.We start our analysis on real-world social graphs, where the two subgroups are defined by sensitive demographic attributes such as gender or age. Our findings suggest that the way and the extent to which people recommenders can produce disparate visibility on the two subgroups, might depend in large part on the level of homophily within the subgroups. % To verify these findings, we move our analysis to synthetic datasets, where we can control characteristics of the input social graph, such as the size of the minority and the level of homophily. Our results show that homophily plays a key role in promoting or reducing visibility for different subgroups under various combinations of dataset characteristics and recommendation algorithms.
Gossip and Attend: Context-Sensitive Graph Representation Learning
Authors: Kefato Zekarias , Girdzijauskas Sarunas
Published: November 2022

Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-free, resulting in only a single representation per node. Recently studies have argued on the adequacy of a single representation and proposed context-sensitive approaches, which are capable of extracting multiple node representations for different contexts. This proved to be highly effective in applications such as link prediction and ranking.However, most of these methods rely on additional textual features that require complex and expensive RNNs or CNNs to capture high-level features or rely on a community detection algorithm to identify multiple contexts of a node.In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models. We propose Goat, a context-sensitive algorithm inspired by gossip communication and a mutual attention mechanism simply over the structure of the graph. We show the efficacy of Goat using 6 real-world datasets on link prediction and node clustering tasks and compare it against 12 popular and state-of-the-art (SOTA) baselines. Goat consistently outperforms them and achieves up to 12% and 19% gain over the best performing methods on link prediction and clustering tasks, respectively.
Influence Maximization Using Influence and Susceptibility Embeddings
Authors: Panagopoulos George , Malliaros Fragkiskos D. , Vazirgianis Michalis
Published: November 2022

Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis — being a critical part of several real-world applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose Celfie, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on Inf2vec, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed Inf2vec modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.
Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children
Authors: Papadamou Kostantinos , Papasavva Antonis , Zannettou Savvas , Blackburn Jeremy , Kourtellis Nicolas , Leontiadis Ilias , Stringhini Gianluca , Sirivianos Michael
Published: November 2022

A large number of the most-subscribed YouTube channels target children of very young age. Hundreds of toddler-oriented channels on YouTube feature inoffensive, well produced, and educational videos. Unfortunately, inappropriate content that targets this demographic is also common. YouTube's algorithmic recommendation system regrettably suggests inappropriate content because some of it mimics or is derived from otherwise appropriate content. Considering the risk for early childhood development, and an increasing trend in toddler's consumption of YouTube media, this is a worrisome problem. In this work, we build a classifier able to discern inappropriate content that targets toddlers on YouTube with 84.3% accuracy, and leverage it to perform a large-scale, quantitative characterization that reveals some of the risks of YouTube media consumption by young children. Our analysis reveals that YouTube is still plagued by such disturbing videos and its currently deployed counter-measures are ineffective in terms of detecting them in a timely manner. Alarmingly, using our classifier we show that young children are not only able, but likely to encounter disturbing videos when they randomly browse the platform starting from benign videos.
Purchase Intentions on Social Media as Predictors of Consumer Spending
Authors: Pekar Viktor
Published: November 2022

The paper addresses the problem of forecasting consumer expenditure from social media data. Previous research of the topic exploited the intuition that search engine traffic reflects purchase intentions and constructed predictive models of consumer behaviour from search query volumes. In contrast, we derive predictors from explicit expressions of purchase intentions found in social media posts. Two types of predictors created from these expressions are explored: those based on word embeddings and those based on topical word clusters. We introduce a new clustering method, which takes into account temporal co-occurrence of words, in addition to their semantic similarity, in order to create predictors relevant to the forecasting problem. The predictors are evaluated against baselines that use only macroeconomic variables, and against models trained on search traffic data. Conducting experiments with three different regression methods on Facebook and Twitter data, we find that both word embeddings and word clusters help to reduce forecasting errors in comparison to purely macroeconomic models. In most experimental settings, the error reduction is statistically significant, and is comparable to error reduction achieved with search traffic variables.
MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis
Authors: Yan Muheng , Lin Yu-Ru , Hwa Rebecca , Mert Ertugrul Ali , Guo Meiqi , Chung Wen-Ting
Published: November 2022

Lexicon-based methods and word embeddings are the two widely used approaches for analyzing texts in social media. The choice of an approach can have a significant impact on the reliability of the text analysis. For example, lexicons provide manually curated, domain-specific attributes about a limited set of words, while word embeddings learn to encode some loose semantic interpretations for a much broader set of words. Text analysis can benefit from a representation that offers both the broad coverage of word embeddings and the domain knowledge of lexicons. This paper presents MimicProp, a new graph-mode method that learns a lexicon-aligned word embedding. Our approach improves over prior graph-based methods in terms of its interpretability (i.e., lexicon attributes can be recovered) and generalizability (i.e., new words can be learned to incorporate lexicon knowledge). It also effectively improves the performance of downstream analysis applications, such as text classification.
Analysing the Extent of Misinformation in Cancer Related Tweets
Authors: Bal Rakesh , Sinha Sayan , Dutta Swastika , Joshi Rishabh , Ghosh Sayan , Dutt Ritam
Published: November 2022

Twitter has become one of the most sought after places to discuss a wide variety of topics, including medically relevant issues such as cancer. This helps spread awareness regarding the various causes, cures and prevention methods of cancer. However, no proper analysis has been performed, which discusses the validity of such claims. In this work, we aim to tackle the misinformation spread in such platforms. We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread. We then do a comparative analysis of the linguistic variation in the text corresponding to misinformation and truth. This analysis helps us gather relevant insights on various social aspects related to misinformed tweets.
GeoSiteSearch: A Tool to Map Vietnamese Diaspora by Deducing Geographical Information of Web Pages about Our Lady of LaVang
Authors: Masten Madison G. , Ninh Thien-Huong , Tran Nicholas
Published: November 2022

We construct a web tool to extract geographical locations from web pages returned by the Google search engine for an arbitrary query and display those locations on an interactive map. The tool was used to track the worldwide Vietnamese diaspora using Our Lady of LaVang as proxy for presence of a Vietnamese community, but it could potentially have other applications.
Market Forces: Quantifying the Role of Top Credible Ad Servers in the Fake News Ecosystem
Authors: Bozarth Lia , Budak Ceren
Published: November 2022

Larry Lessig argues that four modes regulate behavior in cyberspace: laws, markets, norms, and architecture. How can these four modes regulate the production and spread of fake news? In this paper, we focus on markets and empirically evaluate one particular market-based solution: top ad firms blacklisting fake news producers to eliminate their revenue sources. Our study reveals that fake and low-quality publishers demonstrate a higher tendency to serve more ads and to partner with risky ad servers than traditional news media with similar popularity and age. However, fake news publishers are still strongly reliant on credible ad servers. In fact, the top-10 credible ad servers alone account for 66.7% and 55.6% of fake and low-quality ad traffic respectively. Furthermore, our back-of-the-envelope calculation shows that, at the time of our data collection, the top-10 ad firms were receiving $985.7K to $1.15M monthly from web traffic on fake news sites, a negligible fraction of these firms' annual revenue. Overall, our findings suggest that having top ad firms blacklist known fake and low-quality publishers is a low-cost way to combat fake news.
Experience-Driven Peer Effects: Evidence from a Large Natural Experiment
Authors: Cai William , Ugander Johan
Published: November 2022

Social interactions between people are a central mechanism by which behavior spreads. Several field studies have shown how observing peer behavior affects ones own behavior in a wide range of domains including health, information diffusion, advertisement, and education. However, the role of observing peer experience or outcome---the reward for the observed peer behavior---has largely gone uninvestigated. Here we examine evidence from a large-scale online setting, game play records from League of Legends match-ups, where we are able to disentangle the effects of observing peer behavior from observing peer experience. We find that in addition to positive peer effects from observing behavior, the effect is accentuated by observed experience, with a large positive effect when observing a good outcome and a small (but still positive) effect when observing a bad outcome. We further find that this experience driven effect is moderated by skill, becoming more pronounced among highly skilled subjects. Our findings demonstrate the importance of the role of observed peer experience beyond peer behavior. We anticipate this result to be of use to both practitioners and theoreticians in the space of social influence. For example, online platforms may wish to broadcast the positive outcomes of peers, more than mere behaviors, when a user performs a behavior desirable to the platform. Furthermore, efforts aimed at maximizing the social spread of a product may benefit from modeling experience-driven peer effects as part of the spreading process.
Global Gender Differences in Wikipedia Readership
Authors: Johnson Isaac , Lemmerich Florian , Sáez-Trumper Diego , West Robert , Strohmaier Markus , Zia Leila
Published: November 2022

Wikipedia represents the largest and most popular source of encyclopedic knowledge in the world, aiming to provide equal access to information worldwide. From a global online survey of 65,031 readers of Wikipedia and their corresponding reading logs, we present first evidence of gender differences in Wikipedia readership and how they manifest in records of user behavior. More specifically we report that (1) women are underrepresented among readers of Wikipedia, (2) women view fewer pages per reading session than men do, (3) men and women visit Wikipedia for similar reasons, and (4) men and women exhibit specific topical preferences. Our findings lay the foundation for identifying pathways toward knowledge equity in the usage of online encyclopedic knowledge.
Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data
Authors: Kulshrestha Juhi , Oliveira Marcos , Karaçalık Orkut , Bonnay Denis , Wagner Claudia
Published: November 2022

Understanding human activities and movements on the Web is not only important for computational social scientists but can also offer valuable guidance for the design of online systems for recommendations, caching, advertising, and personalization. In this work, we demonstrate that people tend to follow routines on the Web, and these repetitive patterns of web visits increase their browsing behavior's achievable predictability. We present an information-theoretic framework for measuring the uncertainty and theoretical limits of predictability of human mobility on the Web. We systematically assess the impact of different design decisions on the measurement. We apply the framework to a web tracking dataset of German internet users. Our empirical results highlight that individual's routines on the Web make their browsing behavior predictable to 85% on average, though the value varies across individuals. We observe that these differences in the users' predictabilities can be explained to some extent by their demographic and behavioral attributes.
PopFactor: Live-Streamer Behavior and Popularity
Authors: Netzorg Robert , Arnett Lauren , Chaintreau Augustin , Wu Eugene
Published: November 2022

Live video-streaming platforms such as Twitch enable top content creators to reap significant profits and influence.
To that effect, various behavioral norms are recommended to new entrants and those seeking to increase their popularity and success.
Chiefly among them are to simply put in the effort and promote on social media outlets such as Twitter, Instagram, and the like.
But does following these behaviors indeed have a relationship with eventual popularity?

In this paper, we collect a corpus of Twitch streamer popularity measures---spanning social and financial measures---and their behavior data on Twitch and third party platforms. We also compile a set of community-defined behavioral norms. We then perform temporal analysis to identify the increased predictive value that a streamer's future behavior contributes to predicting future popularity. At the population level, we find that behavioral information improves the prediction of relative growth that exceeds that of the median streamer. At the individual level, we find that although it is difficult to quickly become successful in absolute terms, streamers that put in considerable effort are more successful than the rest. Ultimately, we find that studying the popularity and success of content creators in the long term is a promising and rich research area.
How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection
Authors: Prabhakaran Vinodkumar , Rei Marek , Shutova Ekaterina
Published: November 2022

Metaphors are widely used in political rhetoric as an effective framing device. While the efficacy of specific metaphors such as the war metaphor in political discourse has been documented before, those studies often rely on small number of hand-coded instances of metaphor use. Larger-scale topic-agnostic studies are required to establish the general persuasiveness of metaphors as a device, and to shed light on the broader patterns that guide their persuasiveness. In this paper, we present a large-scale data-driven study of metaphors used in political discourse. We conduct this study on a publicly available dataset of over 85K posts made by 412 US politicians in their Facebook public pages, up until Feb 2017. Our contributions are threefold: we show evidence that metaphor use correlates with ideological leanings in complex ways that depend on concurrent political events such as winning or losing elections; we show that posts with metaphors elicit more engagement from their audience overall even after controlling for various socio-political factors such as gender and political party affiliation; and finally, we demonstrate that metaphoricity is indeed the reason for increased engagement of posts, through a fine-grained linguistic analysis of metaphorical vs. literal usages of 513 words across 70K posts.
“Call me sexist, but...” : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
Authors: Samory Mattia , Sen Indira , Kohne Julian , Flöck Fabian , Wagner Claudia
Published: November 2022

Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets, surfacing their limitations in breadth and validity with respect to the construct of sexism. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples. Yet, including diverse data and adversarial examples at training time results in models that generalize better and that are more robust to artifacts of data collection. By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
An Embedding-based Joint Sentiment-Topic Model for Short Texts
Authors: Sengupta Ayan , Paka William Scott , Roy Suman , Ranjan Gaurav , Chakraborty Tanmoy
Published: November 2022

Short text is a popular avenue of sharing feedback, opinions and reviews on social media, e-commerce platforms, etc. Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users’ behaviour. However, obtaining high quality sentiment-associated and human interpretable themes still remains a challenge for short texts. In this paper we develop ELJST, an embedding enhanced generative joint sentiment-topic model that can discover more coherent and diverse topics from short texts. It uses Markov Random Field Regularizer that can be seen as generalisation of skip-gram based models. Further, it can leverage higher order semantic information appearing in word embedding, such as self-attention weights in graphical models. Our results show an average improvement of 10% in topic coherence and 5% in topic diversification over baselines. Finally, ELJST helps understand users' behaviour at more granular levels which can be explained. All these can bring significant values to service and healthcare industries often dealing with customers.
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Authors: Alam Firoj , Sajjad Hassan , Imran Muhammad , Ofli Ferda
Published: November 2022

The time-critical analysis of social media streams is important for humanitarian organizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis-related data posted on social media. However, due to the dispersed nature of the datasets used in the literature, it is not possible to compare the results and measure the progress made towards better models for crisis informatics. In this work, we attempt to bridge this gap by combining various existing crisis-related datasets. We consolidate eight annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks, respectively. The consolidation results in a larger dataset that affords the ability to train more sophisticated models. To that end, we provide binary and multiclass classification results using CNN, FastText, and transformer based models to address informativeness and humanitarian tasks, respectively. We make the dataset and scripts available at https://crisisnlp.qcri.org/crisis_datasets_benchmarks.html.
Memes, Radicalisation, and the Promotion of Violence on Chan Sites
Authors: Crawford Blyth , Keen Florence , Suarez-Tangil Guillermo
Published: November 2022

A number of violent far-right attacks in recent years have revealed an apparent connection with `chan culture', not just in the tangible examples of attackers uploading manifestos, final messages and livestreams to chan sites themselves, but in the widespread community support exhibited in some corners of this online subculture where violence is both trivialised and glorified. Commonly, this is manifested in the visual culture present on chan sites, particularly memes, which may be used to promote extreme or even violent narratives under the guise of humour and irony.

This paper seeks to understand how the visual culture of chan sites contribute to, and/or encourage violent discourse. In particular, we combine quantitative data scraping, ethnography and visual analysis across tens of chan sites ranging in popularity like 4chan, 8kun, or neinchan between March -- June 2020. Over all we collect a dataset of 135K images across different chans and provide first qualitivative characterization of the most popular images shared across different chans.
YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube
Authors: Horta Ribeiro Manoel , West Robert
Published: November 2022

YouTube plays a key role in entertaining and informing people around the globe. However, studying the platform is difficult due to the lack of randomly sampled data and of systematic ways to query the platform's colossal catalog. In this paper, we present YouNiverse, a large collection of channel and video metadata from English-language YouTube. YouNiverse comprises metadata from over 136k channels and 72.9M videos published between May 2005 and October 2019, as well as channel-level time-series data with weekly subscriber and view counts. Leveraging channel ranks from socialblade.com, an online service that provides information about YouTube, we are able to assess and enhance the representativeness of the sample of channels. Additionally, the dataset also contains a table specifying which videos a set of 449M anonymous users commented on. YouNiverse, publicly available at https://zenodo.org/record/4650046, will empower the community to do research with and about YouTube.
Evolution of Retweet Rates in Twitter User Careers: Analysis and Model
Authors: Garimella Kiran , West Robert
Published: November 2022

We study the evolution of the number of retweets received by Twitter users over the course of their 'careers' on the platform.
We find that on average the number of retweets received by users tends to increase over time.
This is partly expected because users tend to gradually accumulate followers.
Normalizing by the number of followers, however, reveals that the relative, per-follower retweet rate tends to be non-monotonic, maximized at a 'peak age' after which it does not increase, or even decreases.
We develop a simple model of the process behind this phenomenon, which assumes a constantly growing number of followers, each of whom loses interest over time.
We show that this model is sufficient to explain the non-monotonic nature of per-follower retweet rates, without any assumptions about the quality of content posted at different times.
Well-Being Depends on Social Comparison: Hierarchical Models of Twitter Language Suggest That Richer Neighbors Make You Less Happy
Authors: Giorgi Salvatore , Guntuku Sharath Chandra , Eichstaedt Johannes C. , Pajot Claire , Schwartz H. Andrew , Ungar Lyle H.
Published: November 2022

Psychological research has shown that subjective well-being is sensitive to social comparison effects; individuals report decreased happiness when their neighbors earn more than they do.
In this work, we use Twitter language to estimate the well-being of users, and model both individual and neighborhood income using hierarchical modeling across counties in the United States (US).
We show that language-based estimates from a sample of 5.8 million Twitter users replicate results obtained from large-scale well-being surveys --- relatively richer neighbors leads to lower well-being, even when controlling for absolute income.
Furthermore, predicting individual-level happiness using hierarchical models (i.e., individuals within their communities) out-predicts standard baselines.
We also explore language associated with relative income differences and find that individuals with lower income than their community tend to swear (f*ck, sh*t, b*tch), express anger (pissed, bullsh*t, wtf), hesitation (don't, anymore, idk, confused) and acts of social deviance (weed, blunt, drunk).
These results suggest that social comparison robustly affects reported well-being, and that Twitter language analyses can be used to both measure these effects and shed light on their underlying psychological dynamics.
Studying Moral-based Differences in the Framing of Political Tweets
Authors: Reiter-Haas Markus , Kopeinik Simone , Lex Elisabeth
Published: November 2022

In this paper, we study the moral framing of political content on Twitter. Specifically, we examine differences in moral framing in two datasets: (i) tweets from US-based politicians annotated with political affiliation and (ii) COVID-19 related tweets in German from followers of the leaders of the five major Austrian political parties. Our research is based on recent work that introduces an unsupervised approach to extract framing bias and intensity in news using a dictionary of moral virtues and vices. In this paper, we use a more extensive dictionary and adapt it to German-language tweets. Overall, in both datasets, we observe a moral framing that is congruent with the public perception of the political parties. In the US dataset, democrats have a tendency to frame tweets in terms of care, while loyalty is a characteristic frame for republicans. In the Austrian dataset, we find that the followers of the governing conservative party emphasize care, which is a key message and moral frame in the party's COVID-19 campaign slogan. Our work complements existing studies on moral framing in social media. Also, our empirical findings provide novel insights into moral-based framing on COVID-19 in Austria.
ESG Tracker: Unbiased and Explainable ESG Profile from Real-time Data
Authors: Momeni Elaheh , Fraenkel Constantin , Kiss Patrick , Burgmann Andreas
Published: November 2022

Environmental, social, and governance (ESG) criteria are a set of standards for a company's operations that investors who are conscious about sustainability use to screen potential investments. Many sustainable investors use ESG criteria ratings provided by ESG rating providers, which are mainly based on corporate disclosures. However, the source of data and the diversity of their assessment methodologies have posed several challenges that lead to biased and non-transparent results. We propose a system that provides an unbiased ESG profile for companies from real-time data collected from the dynamic Web (online News and Social media platforms) that is explainable.
A Decentralized Approach towards Responsible AI in Social Ecosystems
Authors: Chu Wenjing
Published: November 2022

For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of biases and discrimination in automated decision making. Recent literature in the field has identified serious shortcomings of narrow technology focused and formalism-oriented research and has proposed an interdisciplinary approach that brings the social context into the scope of study.
In this paper, we take a sociotechnical approach to propose a more expansive framework of thinking about the Responsible AI challenges in both technical and social context. Effective solutions need to bridge the gap between a technical system with the social system that it will be deployed to. To this end, we propose computational human agency and regulation as main mechanisms of intervention and propose a decentralized computational infrastructure, or a set of public utilities, as the computational means to bridge this gap. A decentralized infrastructure is uniquely suited for meeting this challenge and enable technical solutions and social institutions in a mutually reinforcing dynamic to achieve Responsible AI goals. Our approach is novel in its sociotechnical co-design and its aim in tackling the structural issues that cannot be solved within the narrow confines of AI technical research. We then explore possible features of the proposed infrastructure and discuss how they may help solve example problems recently studied in the field.