bluuebunny/crossref_metadata_2025 huggingface dataset:
Outbreak of toxoplasmosis associated with muscular lesions in finishing pigs caused by an atypical Toxoplasma gondii genotype
Authors: Piva Manoela Marchezan , Pereira Paula Reis , Schwertz Claiton Ismael , Hammerschmitt Márcia Elisa , Lorenzett Marina Paula , Henker Luan Cleber , Driemeier David , Clemes Yara Souza , Pena Hilda Fátima Jesus , Pavarini Saulo Petinatti
Published: November 2022

Abstract Toxoplasma gondii infections are usually asymptomatic in pigs, and an acute clinical disease is rare in this host. This study aimed to determine the pathological and molecular aspects of an outbreak of fatal systemic toxoplasmosis in finishing pigs in Brazil. The outbreak occurred on a commercial finishing pig farm in the state of Santa Catarina in southern Brazil. The farm had 1500 pigs and 3.8% of mortality rate during the outbreak. The pigs had fever, anorexia, apathy, and locomotor deficits. Seven pigs were necropsied. Gross findings included multifocal to coalescent pale areas in skeletal muscles, lymphadenomegaly, hepatosplenomegaly, and non-colapsed lungs. The histological findings included granulomatous lymphadenitis, hepatitis and splenitis, necrotizing myositis, and lymphoplasmacytic interstitial pneumonia. Lung and liver lesions were occasionally accompanied by T. gondii parasitic structures. Positive immunolabeling for T. gondii tachyzoites and encysted bradyzoites was detected in all examined pigs. PCR-RFLP (11 markers) and microsatellite analysis (15 markers) identified the non-archetypal genotype #278 in pigs. This is the first report of systemic toxoplasmosis in pigs with muscle lesions and additionally shows the diversity of disease-causing T. gondii genotypes circulating in animals in Brazil.
Advanced Catalytic Technologies for Compressed Natural Gas–Gasoline Fuelled Engines
Authors: Wahbi A. , Tsolakis A. , Herreros J. M. , Zeraati-Rezaei S. , Doustdar O. , Millington P. J. , Raj A.
Published: November 2022

The main challenges of compressed natural gas (CNG) engine fuelling in terms of methane abatement in the aftertreatment system are addressed in this study using differently loaded platinum group metal (pgm) catalysts. A dual-fuel injection strategy of methane-gasoline was implemented where methane gas was port-injected into the intake in stoichiometric conditions at levels corresponding to 20% and 40% energy density replacement of gasoline fuel. High, medium and low loaded palladium-rhodium catalysts were used and compared to study the effect of pgm loading on the catalyst light-off activity for methane. Results indicate that increasing the palladium loading led to significantly earlier light-off temperatures achieved at relatively lower temperatures of 340°C, 350°C and 395°C respectively. However, the benefit diminishes above palladium loading >142.5 g ft–3. The study has also demonstrated that ammonia is formed over the CNG catalyst due to steam-reforming reactions from the increased levels of methane in the exhaust with dual-fuelling. Hence aftertreatment technologies such as selective catalytic reduction (SCR) should be adopted to remove them. This further highlights the need to regulate the harmful ammonia emissions from future passenger cars fuelled with CNG. In addition, the benefits of the dual-fuel system in terms of lower engine output carbon dioxide, non-methane hydrocarbon (NMHC) and particulate matter (PM) emissions compared to the gasoline direct injection (GDI) mode alone are presented.
SentiWorld: Understanding Emotions between Countries Based on Tweets
Authors: Yea Sang-Jun , Kim Sejin , TO John-Michaël , Lee Jae-Gil
Published: November 2022

In order to understand emotions between countries, we collected around 25 million tweets, analyzed them using statistical and network analysis methods, and visualized the analytic results as both a sentiment map and a sentiment network.
Are You Charlie or Ahmed? Cultural Pluralism in Charlie Hebdo Response on Twitter
Authors: An Jisun , Kwak Haewoon , Mejova Yelena , Alonso Saenz De Oger Sonia , Gomez Fortes Braulio
Published: November 2022

We study the response to the Charlie Hebdo shootings of January 7, 2015 on Twitter across the globe. We ask whether the stances on the issue of freedom of speech can be modeled using established sociological theories, including Huntington's culturalist Clash of Civilizations, and those taking into consideration social context, including Density and Interdependence theories. We find support for Huntington's culturalist explanation, in that the established traditions and norms of one's "civilization" predetermine some of one's opinion. However, at an individual level, we also find social context to play a significant role, with non-Arabs living in Arab countries using #JeSuisAhmed ("I am Ahmed") five times more often when they are embedded in a mixed Arab/non-Arab (mention) network. Among Arabs living in the West, we find a great variety of responses, not altogether associated with the size of their expatriate community, suggesting other variables to be at play.
Lost in Propagation? Unfolding News Cycles from the Source
Authors: Tan Chenhao , Friggeri Adrien , Adamic Lada
Published: November 2022

The news media play an important role in informing the public on current events. Yet it has been difficult to understand the comprehensiveness of news media coverage on an event and how the reactions that the coverage evokes may diverge, because this requires identifying the origin of an event and tracing the information all the way to individuals who consume the news. In this work, we pinpoint the information source of an event in the form of a press release and investigate how its news cycle unfolds. We follow the news through three layers of propagation: the news articles covering the press release, shares of those articles in social media, and comments on the shares. We find that a news cycle typically lasts two days. Although news media in aggregate cover the information contained in the source, a single news article will typically only provide partial coverage. Sentiment, while dampened in news coverage relative to the source, again rises in social media shares and comments. As the information propagates through the layers, it tends to diverge from the source: while some ideas emphasized in the source fade, others emerge or gain in importance. We also discover how far the news article is from the information source in terms of sentiment or language does not help predict its popularity.
What the Language You Tweet Says About Your Occupation
Authors: Hu Tianran , Xiao Haoyuan , Luo Jiebo , Nguyen Thuy-vy Thi
Published: November 2022

Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.
''Will Check-in for Badges'': Understanding Bias and Misbehavior on Location-Based Social Networks
Authors: Wang Gang , Schoenebeck Sarita , Zheng Haitao , Zhao Ben
Published: November 2022

Social computing researchers are using data from location-based social networks (LBSN), e.g., "Check-in" traces, as approximations of human movement. Recent work has questioned the validity of this approach, showing large discrepancies between check-in data and actual user mobility. To further validate and understand such discrepancies, we perform a crowdsourced study of Foursquare users that seeks to a) quantify bias and misrepresentation in check-in datasets and the impact of self-selection in prior studies, and b) understand the motivations behind misrepresentation of check-ins, and the potential impact of any system changes designed to curtail such misbehavior. Our results confirm the presence of significant misrepresentation of location check-ins on Foursquare. They also show that while "extraneous" check-ins are motivated by external rewards provided by the system, "missing" check-ins are motivated by personal concerns such as location privacy. Finally, we discuss the broader implications of our findings to the use of check-in datasets in future research on human mobility.
Pub Crawling at Scale: Tapping Untappd to Explore Social Drinking
Authors: Chorley Martin , Rossi Luca , Tyson Gareth , Williams Matthew
Published: November 2022

There has been a recent surge of research looking at the reporting of food consumption on social media. The topic of alcohol consumption, however, remains poorly investigated. Social media has the potential to shed light on a topic that, traditionally, is difficult to collect fine-grained information on. One social app stands out in this regard: Untappd is an app that allows users to 'check-in' their consumption of beers. It operates in a similar fashion to other location-based applications, but is specifically tailored to the collection of information on beer consumption. In this paper, we explore beer consumption through the lens of social media. We crawled Untappd in real time over a period of 112 days, across 40 cities in the United States and Europe. Using this data, we shed light on the drinking habits of over 369k users. We focus on per-user and per-city characterisation, highlighting key behavioural trends.
Dynamic Data Capture from Social Media Streams: A Contextual Bandit Approach
Authors: Gisselbrecht Thibault , Lamprier Sylvain , Gallinari Patrick
Published: November 2022

Social Media usually provide streaming data access that enable dynamic capture of the social activity of their users. Leveraging such APIs for collecting social data that satisfy a given pre-defined need may constitute a complex task, that implies careful stream selections. With user-centered streams, it indeed comes down to the problem of choosing which users to follow in order to maximize the utility of the collected data w.r.t. the need. On large social media, this represents a very challenging task due to the huge number of potential targets and restricted access to the data. Because of the intrinsic non-stationarity of user's behavior, a relevant target today might be irrelevant tomorrow, which represents a major difficulty to apprehend. In this paper, we propose a new approach that anticipates which profiles are likely to publish relevant contents - given a predefined need - in the future, and dynamically selects a subset of accounts to follow at each iteration. Our method has the advantage to take into account both API restrictions and the dynamics of users' behaviors. We formalize the task as a contextual bandit problem with multiple actions selection. We finally conduct experiments on Twitter, which demonstrate the empirical effectiveness of our approach in real-world settings.
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Authors: Fast Ethan , Vachovsky Tina , Bernstein Michael
Published: November 2022

Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors' use of gender stereotypes is associated with the community's ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.
Message Impartiality in Social Media Discussions
Authors: Zafar Muhammad Bilal , Gummadi Krishna , Danescu-Niculescu-Mizil Cristian
Published: November 2022

Discourse on social media platforms is often plagued by acute polarization, with different camps promoting different perspectives on the issue at hand—compare, for example, the differences in the liberal and conservative discourse on the U.S. immigration debate. A large body of research has studied this phenomenon by focusing on the affiliation of groups and individuals. We propose a new finer-grained perspective: studying the impartiality of individual messages. While the notion of message impartiality is quite intuitive, the lack of an objective definition and of a way to measure it directly has largely obstructed scientific examination. In this work we operationalize message impartiality in terms of how discernible the affiliation of its author is, and introduce a methodology for quantifying it automatically. Unlike a supervised machine learning approach, our method can be used in the context of emerging events where impartiality labels are not immediately available. Our framework enables us to study the effects of (im)partiality on social media discussions at scale. We show that this phenomenon is highly consequential, with partial messages being twice more likely to spread than impartial ones, even after controlling for author and topic. By taking this fine-grained approach to polarization, we also provide new insights into the temporal evolution of online discussions centered around major political and sporting events.
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
Authors: Celis L. Elisa , Krafft Peter , Kobe Nathan
Published: November 2022

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.
The News Cycle's Influence on Social Media Activity
Authors: Yates Andrew , Joselow Jonah , Goharian Nazli
Published: November 2022

While much work has studied the problem of identifying real-world trends based on social media, none has attempted to explicitly model the news cycle's influence on this social media activity. In this work we attempt to model the news cycle's influence on Twitter activity in the context of "news-centric events." We present a model for estimating the number of tweets posted in response to a news event and propose a method for creating an appropriate ground truth. We find that, although our method is sensitive to variations in the amount of training data, we are able to predict future Twitter activity with reasonable accuracy.
Are Social Bots on Twitter Political Actors? Empirical Evidence from a Ukrainian Social Botnet
Authors: Hegelich Simon , Janetzko Dietmar
Published: November 2022

A considerable amount of data in social networks like Twitter is not generated by humans but by automatic programs (bots). Some of these bots are mimicking humans (socialbots) and can hardly be identified. In this article, we analyze a social botnet involved in the Ukrainian/Russian conflict. Based on text mining and unsupervised learning, we can identify three different behaviors: mimicry, window dressing, and reverberation.
#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter
Authors: An Jisun , Weber Ingmar
Published: November 2022

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.
Understanding Cognitive Styles from User-Generated Social Media Content
Authors: Wang Yi , Mahmud Jalal , Liu Taikun
Published: November 2022

The linguistic analyses on easily accessible, user-generated social media content offers great opportunities to identify individual characteristics such as their cognitive styles. In this paper, We explore the potential to use social media content to identify individuals' cognitive styles. We first employed crowdsourcing to collect Twitter users' cognitive styles using standard psychometric instruments. Then, we extracted the linguistic features of their social media postings. Leveraging these features, we build prediction models that provide estimates of cognitive styles through statistical regression and classification. We find that user generated content in social media provide useful information for characterizing people's cognitive styles. The models' performance indicates that the cognitive styles automatically inferred from social media are good proxies for the ground truth, and hence provides a promising and scalable way to automatically identify a large number of people's cognitive styles without reaching them individually.
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
Authors: Wang Yu , Li Yuncheng , Luo Jiebo
Published: November 2022

In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals' number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.
User Behaviors in Newsworthy Rumors: A Case Study of Twitter
Authors: Li Quanzhi , Liu Xiaomo , Fang Rui , Nourbakhsh Armineh , Shah Sameena
Published: November 2022

While Twitter has become an important platform for generating and disseminating breaking news stories, it is also a medium for spreading false rumors. Most research relevant to newsworthy rumors has treated its subject as a homogeneous class without due consideration to the nuances that distinguish rumors from each other. Some studies, for example, have studied rumor propagation as if all rumors follow the same type of distribution. We question this belief and argue that rumors differ in how they engage their audience, how they spread and by whom, what type of users interact with them, and how they evolve over time. In this paper, we study various semantic aspects of false rumors and analyze their spread. We study the characteristics of rumor usage, emphasize the role users play in rumors, their beliefs, and interactions. Finally, we answer several research questions with regards to each of the topics addressed.
#Unconfirmed: Classifying Rumor Stance in Crisis-Related Social Media Messages
Authors: Zeng Li , Starbird Kate , Spiro Emma
Published: November 2022

It is well-established that within crisis-related communications, rumors are likely to emerge. False rumors, i.e. misinformation, can be detrimental to crisis communication and response; it is therefore important not only to be able to identify messages that propagate rumors, but also corrections or denials of rumor content. In this work, we explore the task of automatically classifying rumor stances expressed in crisisrelated content posted on social media. Utilizing a dataset of over 4,300 manually coded tweets, we build a supervised machine learning model for this task, achieving an accuracy over 88% across a diverse set of rumors of different types.
Analyzing the Political Sentiment of Tweets in Farsi
Authors: Vaziripour Elham , Giraud-Carrier Christophe , Zappala Daniel
Published: November 2022

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.
Temporal Opinion Spam Detection by Multivariate Indicative Signals
Authors: Ye Junting , Kumar Santhosh , Akoglu Leman
Published: November 2022

Online consumer reviews reflect the testimonials of real people, unlike e.g., ads. As such, they have critical impact on potential consumers, and indirectly on businesses. Problematically, such financial incentives have created a market for spammers to fabricate reviews to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). Most existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers’ activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on two different datasets show that our approach is fast, effective, and practical to be deployed in real-world systems.
Dissemination Biases of Social Media Channels: On the Topical Coverage of Socially Shared News
Authors: Chakraborty Abhijnan , Ghosh Saptarshi , Ganguly Niloy , Gummadi Krishna P.
Published: November 2022

In a marked departure from traditional offline media, where all subscribers of a particular news media source (e.g., New York Times) used to get the same news stories through printed newspapers, online news media presents multiple options for the readers to consume news. For example, the subscribers of a media source can get news directly from the news website, or from what their peers share over social media sites like Facebook and Twitter. It is, however, unclear whether there are any differences in the news disseminated on these different online channels. In this work, we analyze data from a popular online news media site (nytimes.com), and show that each of these different channels tends to highlight some types of stories more than other stories. We believe that consumers of online news as well as media organizations need to be aware of such differences in various online news dissemination channels.
To Buy or to Read: How a Platform Shapes Reviewing Behavior
Authors: Newell Edward , Dimitrov Stefan , Piper Andrew , Ruths Derek
Published: November 2022

We explore how platforms influence user-generated content by comparing reviews made on the retail platform Amazon, with those on the (non-retail) community platform Goodreads. We find the retail setting gives rise to shorter, more declamatory, and persuasive reviews, while the non-retail community generates longer, more reflective, tentative reviews with more diverse punctuation. These differences are pronounced enough to enable automatic inference of the platform from which reviews were taken with over 90% F1. Significant differences in star-ratings appear to parallel differences in review content. Both platforms allow users to give feedback on reviews. Experiments show that a subtle difference in the review feedback features influences review-promotion behavior, which may in part explain the differences in review content. Our results show that the context and design of a platform has a strong but subtle effect on how users write and engage with content.
Fusing Audio, Textual, and Visual Features for Sentiment Analysis of News Videos
Authors: Pereira Moisés , Pádua Flávio , Pereira Adriano , Benevenuto Fabrício , Dalip Daniel
Published: November 2022

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sentiment analysis from the closed caption associated to the videos of interest. More specifically, we compute features, such as, visual intensities of recognized emotions, field sizes of participants, voicing probability, sound loudness, speech fundamental frequencies and the sentiment scores (polarities) from text sentences in the closed caption. Experimental results with a dataset containing 520 annotated news videos from three Brazilian and one American popular TV newscasts show that our approach achieves an accuracy of up to 84% in the sentiments (tension levels) classification task, thus demonstrating its high potential to be used by media analysts in several applications, especially, in the journalistic domain.
If You've Got the Money, I've Got the Time: Spatio-Temporal Footprints of Spending at Sports Events on Foursquare
Authors: Hristova Desislava , Liben-Nowell David , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

Notable events such as sports games have a highly visible impact both in the media and at the event location itself, but the pre- and post-game activities of attendees - and their spending around such events - are much less evident. In this work, we use social media footprints to analyze and interpret these broader ripples of activity: specifically, we examine the temporal, spatial, and microeconomic patterns of baseball and basketball game attendees' activity around game events for 21 North American cities and 32 teams during 2010--2014, using mobility and price data from the location-based social network Foursquare. We identify some universal spatio temporal patterns that hold across both sports and cities: exploiting additional information about venues' price levels, we find that, in aggregate, users who check in later at the game come from pricier restaurants and bars. We use these insights to predict the costliness of a user's choice of restaurant/bar, based only on a single transition to or from the stadium/arena, using the temporal properties of the transition such as the time of check-in at the game, distance traveled, and speed of transition, with the last being the most important factor. Our analysis carries broad implications for human mobility studies, mobile applications, and sports economics.
Wiki Editors' Acceptance of Additional Guidance on Talk Pages
Authors: Heimbuch Sven , Bodemer Daniel
Published: November 2022

One purpose of Wikis like Wikipedia is to collaboratively generate content. During the creation processes controversies between multiple authors might emerge that are further discussed on a Wiki article's so-called talk page. Research has shown that controversies grounded on opposing points of view and contradictory evidence can be fruitful to trigger individual elaboration processes. But it has also been shown that many Wikis are not necessarily suited to identify relevant discussion contents and thus need additional support to guide users. In an experimental study (N = 181) we implemented on a Wiki talk page (1) visual controversy status highlights as implicit guidance and (2) a collaboration script directing users towards discussions as explicit guidance. We analyzed their user adoption and influence on Wiki activities. Our results show that both experimental additions affect user behaviors and that in particular the visual controversy status highlights are positively adopted by users.
Hidden Gems in the Wikipedia Discussions: The Wikipedians’ Rationales
Authors: Xiao Lu
Published: November 2022

This paper presents a series of completed and ongoing studies that are aimed at understanding the role of the Wikipedians’ rationales in the Wikipedia discussions. We define a rationale as one’s justification of her viewpoint and suggestion. Our studies demonstrate the potential of leveraging the Wikipedians’ rationales in the discussions as resources for future decision-makings and as resources for eliciting knowledge about the community’s norms, practices and policies. Viewed as the rich digital traces in these environments, we consider them to be beneficial for the community members, such as helping newcomers familiarize themselves on the commonly accepted justificatory reasoning styles. We call for more research attention to the discussion content from this rationale study perspective.
Topical Interest and Degree of Involvement of Bilingual Editors in Wikipedia
Authors: Kim Soo Young , Oh Alice
Published: November 2022

Language reveals a lot of information about its speakers. Speakers of one language usually share common cultural habits or regional characteristics, and their similarities become more obvious within the context where there are multiple languages in use. We focus on studying bilingual users of Wikipedia, one of the largest multilingual user-generated content platforms. In Wikipedia, we can observe the patterns in the English edition, where users of multiple languages come together to express their thoughts and interests in the common language of English. To understand the specific topics edited by bilingual users, we analyze them in terms of revision counts, topics, and country names. We find that bilingual users are generally interested in more local topics, and their language is highly related with their topics. Also, we observe that the topical diversity decreases with the proportion of English edits, and more concentrates on topics related with countries and cultures.
"Just the Facts" with PALOMAR: Detecting Protest Events in Media Outlets and Twitter
Authors: Papanikolaou Konstantina , Papageorgiou Haris , Papasarantopoulos Nikos , Stathopoulou Theoni , Papastefanatos George
Published: November 2022

The volume and velocity of available online sources have changed journalistic research in terms of cost and effort required for discovering stories. However, the heterogeneity and veracity of data sources pose further obstacles in knowledge extraction making it a hard task to handle. The purpose of this study is threefold. Firstly, we present a platform for automated data processing in the context of Computational Journalism. We then propose a general methodology for event extraction from different data sources. Finally, we conducted a pilot implementation of our methodology for protest events extraction from news and Twitter data. Evaluation showed promising results, indicating the feasibility of our approach.
Please Sign to Save... : How Online Environmental Petitions Succeed
Authors: Proskurnia Julia , Aberer Karl , Cudré-Mauroux Philippe
Published: November 2022

Social media have become one of the key platforms to support the debate on climate change. In particular, Twitter allows easy information dissemination when running environmental campaigns. Yet, the dynamics of these campaigns on social platforms still remain largely unexplored. In this paper, we study the success factors enabling online petitions to attain their required number of signatures. We present an analysis of e-petitions and identify how their number of users, tweets and retweets correlate with their success. In addition, we show that environmental petitions are actively promoted by popular public campaigns on Twitter. Finally, we present an annotated corpus of petitions posted by environmental campaigns together with their corresponding tweets to enable further exploration.
CitizenHelper: A Streaming Analytics System to Mine Citizen and Web Data for Humanitarian Organizations
Authors: Karuna Prakruthi , Rana Mohammad , Purohit Hemant
Published: November 2022

Vast information available on social media through citizen sensing, complemented by diverse Web sources such as blogs, and news present an unprecedented opportunity for organizations to improve their work practices using such data. We will demo a novel, scalable and interactive streaming analytics system with a collaborative visual dashboard. The proposed system mines heterogeneous information streams from social and web platforms for analyzing real world events in humanitarian domain, along dimensions of information source demographics, time, location, and content summaries. Specific use-cases of gender-based violence and global displacements will be presented to demonstrate contrasting analyses of diverse data sources, unlike existing social computing systems with single data source, in humanitarian domain.
#NotOkay: Understanding Gender-Based Violence in Social Media
Authors: ElSherief Mai , Belding Elizabeth , Nguyen Dana
Published: November 2022

Gender-based violence (GBV) is a global epidemic that is powered, in part, by a culture of silence and denial of the seriousness of its repercussions. In this paper, we present one of the first investigations of GBV in social media. Considering Twitter as an open pervasive platform that provides means for open discourse and community engagement, we study user engagement with GBV related posts, and age and gender dynamics of users who post GBV content. We also study the specific language nuances of GBV-related posts. We find evidence for increased engagement with GBV-related tweets in comparison to other non-GBV tweets. Our hashtag-based topical analysis shows that users engage online in commentary and discussion about political, social movement-based, and commonplace GBV incidents. Finally, with the rise of public figures encouraging women to speak up, we observe a unique blended experience of non-anonymous self-reported assault stories and an online community of support around victims of GBV. We discuss the role of social media and online anti-GBV campaigns in enabling an open conversation about GBV topics and how these conversations provide a lens into a socially complex and vulnerable issue like GBV.
Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter
Authors: Starbird Kate
Published: November 2022

This research explores the alternative media ecosystem through a Twitter lens. Over a ten-month period, we collected tweets related to alternative narratives — for example, conspiracy theories — of mass shooting events. We utilized tweeted URLs to generate a domain network, connecting domains shared by the same user, then conducted qualitative analysis to understand the nature of different domains and how they connect to each other. Our findings demonstrate how alternative news sites propagate and shape alternative narratives, while mainstream media deny them. We explain how political leanings of alternative news sites do not align well with a U.S. left-right spectrum, but instead feature an anti-globalist (versus globalist) orientation where U.S. Alt-Right sites look similar to U.S. Alt-Left sites. Our findings describe a subsection of the emerging alternative media ecosystem and provide insight in how websites that promote conspiracy theories and pseudo-science may function to conduct underlying political agendas.
Predicting Popular and Viral Image Cascades in Pinterest
Authors: Han Jinyoung , Choi Daejin , Joo Jungseock , Chuah Chen-Nee
Published: November 2022

The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). This paper studies the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume — how large the cascade is, that is, total number of users reached, and (ii) structural virality — how many users in the cascade are responsible for attracting other users. Our model predicts whether an image will be (a) popular in terms of the volume of its cascade, or (b) viral in terms of the structural virality. Our analysis reveals that a popular image is not necessarily viral, and vice versa. This motivates us to investigate whether there are distinctive features for accurately predicting popular or viral image cascades. To predict the popular or viral image cascades, we consider the following feature sets: (i) deep image features, (ii) image meta and poster's information, and (iii) initial propagation pattern. We find that using deep image features alone is not as effective in predicting popular or viral image cascades. We show that image meta and poster's information are strong predictors for predicting popular image cascades while image meta and initial propagation patterns are useful to predict viral image cascades. We believe our exploration can give an important insight for content providers, OSN operators, and marketers in predicting popular or viral image diffusion.
To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion
Authors: Aragón Pablo , Gómez Vicenç , Kaltenbrunner Andreaks
Published: November 2022

Online discussion is essential for the communication and collaboration of online communities. The reciprocal exchange of messages between users that characterizes online discussion can be represented in many different ways. While some platforms display messages chronologically using a simple linear interface, others use a hierarchical (threaded) interface to represent more explicitly the structure of the discussion. Although the type of representation has been shown to affect communication, to the best of our knowledge, the impact of using either one or the other has not yet been investigated in a large and mature online community. In this work we analyze Meneame, a popular Spanish social news platform which recently transitioned from a linear to a hierarchical interface, becoming an ideal research opportunity for this purpose. Using interrupted time series analysis and regression discontinuity design, we observe an abrupt and significant increase in social reciprocity after the adoption of a threaded interface. We furthermore extend state-of-the-art generative models of discussion threads by including reciprocity, a fundamental feature to explain better the structure of the discussions, both before and after the change in the interface.
Community Identity and User Engagement in a Multi-Community Landscape
Authors: Zhang Justine , Hamilton William , Danescu-Niculescu-Mizil Cristian , Jurafsky Dan , Leskovec Jure
Published: November 2022

A community’s identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community’s identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that organizing the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.
Fertility and Its Meaning: Evidence from Search Behavior
Authors: Ojala Jussi , Zagheni Emilio , Billari Francesco , Weber Ingmar
Published: November 2022

Fertility choices are linked to the different preferences and constraints of individuals and couples, and vary importantly by socio-economic status, as well by cultural and institutional context. The meaning of childbearing and childrearing, therefore, differs between individuals and across groups. In this paper, we combine data from Google Correlate and Google Trends for the U.S. with ground truth data from the American Community Survey to derive new insights into fertility and its meaning. First, we show that Google Correlate can be used to illustrate socio-economic differences on the circumstances around pregnancy and birth: for example, searches for “flying while pregnant” are linked to high income fertility, and “paternity test” are linked to non-marital fertility. Second, we combine several search queries to build predictive models of regional variation in fertility, explaining about 75% of the variance. Third, we explore if aggregated web search data can also be used to model fertility trends.
A Long-Term Analysis of Polarization on Twitter
Authors: Garimella Venkata Rama Kiran , Weber Ingmar
Published: November 2022

Social media has played an important role in shaping political discourse over the last decade. At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network — how people follow political and media accounts, (ii) tweeting behavior — whether they retweet content from both sides, and (iii) content — how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10% - 20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.
"Voters of the Year": 19 Voters Who Were Unintentional Election Poll Sensors on Twitter
Authors: Hobbs William , Friedland Lisa , Joseph Kenneth , Tsur Oren , Wojcik Stefan , Lazer David
Published: November 2022

Public opinion and election prediction models based on social media typically aggregate, weight, and average signals from a massive number of users. Here, we analyze political attention and poll movements to identify a small number of social "sensors" — individuals whose levels of social media discussion of the major parties' candidates characterized the candidates' ups and downs over the 2016 U.S. presidential election campaign. Starting with a sample of approximately 22,000 accounts on Twitter that we linked to voter registration records, we used penalized regressions to identify a set of 19 accounts (sensors) that were predictive for the candidates’ poll numbers (5 for Hillary Clinton, 13 for Donald Trump, and 1 for both). The predictions based on the activity of these handfuls of sensors accurately tracked later movements in poll margins. Despite the regressions allowing both supportive and opposition sensors, our separate models for Trump and Clinton poll support identified sensors for Hillary Clinton who were disproportionately women and for Donald Trump who were disproportionately white. The method did not predict changes in levels of undecideds and underestimated support for Donald Trump in September 2016, where the errors were correlated with discussions of protests of police shootings.