bluuebunny/crossref_metadata_2025 huggingface dataset:
A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework
Authors: Lokala Usha , Srivastava Aseem , Dastidar Triyasha Ghosh , Chakraborty Tanmoy , Akhtar Md Shad , Panahiazar Maryam , Sheth Amit
Published: November 2022

Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adopt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.
The Dynamics of Exploration on Spotify
Authors: Mok Lillio , Way Samuel F. , Maystre Lucas , Anderson Ashton
Published: November 2022

Digital media platforms give users access to enormous amounts of content that they must explore to avoid boredom and satisfy their needs for heterogeneity. Existing strands of work across psychology, marketing, computer science, and music underscore the importance of the lifecycle to understanding exploratory behavior, but they are also often inconsistent with each other. In this study, we examine how users explore online content on Spotify over time, whether by discovering entirely novel music or by refreshing their listening habits from one time frame to the next. We find clear differences between users at different points of their off-platform lifecycles, with younger listeners consistently exploring unknown content less and exploiting known content more. Across their on-platform histories, users also explore in bursts by following seasonal cycles and exploratory phases. We also find that these patterns of exploration do not translate to other notions of heterogeneity like diversity; notably, younger listeners are more diverse in their consumption despite exploring less. Exploration and diversity thus capture different ways in which people find variety, potentially accounting for the inconsistencies in existing work. Together, these nuanced dynamics of exploration suggest that online platforms may be better poised to support users by incorporating different measures of heterogeneous consumption.
The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood
Authors: Naseri Shahrzad , Reddy Sravana , Correia Joana , Karlgren Jussi , Jones Rosie
Published: November 2022

In this work, we study the association between song lyrics and mood through a data-driven analysis. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. We take advantage of state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. We find that a pretrained transformer-based language model in a zero-shot setting -- i.e., out of the box with no further training on our data -- is powerful for capturing song-mood associations. Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. Furthermore, by comparing the prediction of a model using lyrics with one using acoustic features, we observe that the relative importance of lyrics for mood prediction in comparison with acoustics depends on the specific mood. Finally, we verify if the models are capturing the same information about lyrics and acoustics as humans through an annotation task where we obtain human judgments of mood-song relevance based on lyrics and acoustics.
“It Is Just a Flu”: Assessing the Effect of Watch History on YouTube’s Pseudoscientific Video Recommendations
Authors: Papadamou Kostantinos , Zannettou Savvas , Blackburn Jeremy , Cristofaro Emiliano De , Stringhini Gianluca , Sirivianos Michael
Published: November 2022

The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79.

We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos.
A Unified Graph-Based Approach to Disinformation Detection Using Contextual and Semantic Relations
Authors: Paraschiv Marius , Salamanos Nikos , Iordanou Costas , Laoutaris Nikolaos , Sirivianos Michael
Published: November 2022

As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which we denote as a meta-graph, that combines underlying users' relational event information, as well as semantic and topical modeling. We detail the construction of an example meta-graph using Twitter data covering the 2016 US election campaign and then compare the detection of disinformation at cascade level, using well-known graph neural network algorithms, to the same algorithms applied on the meta-graph nodes. The comparison shows a consistent 3-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered. We carry out the same experiment on two other datasets, HealthRelease and HealthStory, part of the FakeHealth dataset repository, with consistent results. Finally, we discuss further advantages of our approach, such as the ability to augment the graph structure using external data sources, the ease with which multiple meta-graphs can be combined as well as a comparison of our method to other graph-based disinformation detection frameworks.
Community-Based Fact-Checking on Twitter’s Birdwatch Platform
Authors: Pröllochs Nicolas
Published: November 2022

Misinformation undermines the credibility of social media and poses significant threats to modern societies. As a countermeasure, Twitter has recently introduced "Birdwatch," a community-driven approach to address misinformation on Twitter. On Birdwatch, users can identify tweets they believe are misleading, write notes that provide context to the tweet and rate the quality of other users' notes. In this work, we empirically analyze how users interact with this new feature. For this purpose, we collect all Birdwatch notes and ratings between the introduction of the feature in early 2021 and end of July 2021. We then map each Birdwatch note to the fact-checked tweet using Twitter's historical API. In addition, we use text mining methods to extract content characteristics from the text explanations in the Birdwatch notes (e.g., sentiment). Our empirical analysis yields the following main findings: (i) users more frequently file Birdwatch notes for misleading than not misleading tweets. These misleading tweets are primarily reported because of factual errors, lack of important context, or because they treat unverified claims as facts. (ii) Birdwatch notes are more helpful to other users if they link to trustworthy sources and if they embed a more positive sentiment. (iii) The social influence of the author of the source tweet is associated with differences in the level of user consensus. For influential users with many followers, Birdwatch notes yield a lower level of consensus among users and community-created fact checks are more likely to be seen as being incorrect and argumentative. Altogether, our findings can help social media platforms to formulate guidelines for users on how to write more helpful fact checks. At the same time, our analysis suggests that community-based fact-checking faces challenges regarding opinion speculation and polarization among the user base.
COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives
Authors: Sharma Karishma , Zhang Yizhou , Liu Yan
Published: November 2022

COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the information shared on social media. In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy. During the pandemic, anti-science and political misinformation/conspiracies have been rampant on social media. Therefore, we investigate misinformation and conspiracy groups and their characteristic behaviours in Twitter data collected on COVID-19 vaccines. We identify if any suspicious coordinated efforts are present in promoting vaccine misinformation, and find two suspicious groups - one promoting a ‘Great Reset’ conspiracy which suggests that the pandemic is orchestrated by world leaders to take control of the economy, with vaccine related misinformation and strong anti-vaccine and anti-social messages such as no lock-downs; and another promoting the Bioweapon theory. Misinformation promoted is largely from the anti-vaccine and far-right communities in the 3-core of the retweet graph, with its tweets proportion of conspiracy and questionable sources to reliable sources being much higher. In comparison with the mainstream and health news, the right-leaning community is more influenced by the anti-vaccine and far-right communities, which is also reflected in the disparate vaccination rates in left and right U.S. states. The misinformation communities are also more vocal, either in vaccine or other discussions, relative to remaining communities, besides other behavioral differences. Furthermore, we investigate the COVID-19 vaccine narratives spread on social media. Besides misinformation narratives about vaccine safety, effectiveness and conspiracies, we find that rarer vaccine side-effects, reported less frequently in CDC VAERS reports, were more frequently discussed on social media, and in misinformation narratives, which also use other known tactics of science narratives distortion.
On Xing Tian and the Perseverance of Anti-China Sentiment Online
Authors: Shen Xinyue , He Xinlei , Backes Michael , Blackburn Jeremy , Zannettou Savvas , Zhang Yang
Published: November 2022

Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time. The outbreak of COVID-19 and the extended quarantine has further amplified it. However, we lack a quantitative understanding of the cause of Sinophobia as well as how it evolves over time. In this paper, we conduct a largescale longitudinal measurement of Sinophobia, between 2016 and 2021, on two mainstream and fringe Web communities. By analyzing 8B posts from Reddit and 206M posts from 4chan’s /pol/, we investigate the origins, evolution, and content of Sinophobia. We find that, anti-Chinese content may be evoked by political events not directly related to China, e.g., the U.S. withdrawal from the Paris Agreement. And during the COVID-19 pandemic, daily usage of Sinophobic slurs has significantly increased even with the hate-speech ban policy. We also show that the semantic meaning of the words “China” and “Chinese” are shifting towards Sinophobic slurs with the rise of COVID-19 and remain the same in the pandemic period. We further use topic modeling to show the topics of Sinophobic discussion are pretty diverse and broad. We find that both Web communities share some common Sinophobic topics like ethnics, economics and commerce, weapons and military, foreign relations, etc. However, compared to 4chan’s /pol/, more daily life-related topics including food, game, and stock are found in Reddit. Our finding also reveals that the topics related to COVID-19 and blaming the Chinese government are more prevalent in the pandemic period. To the best of our knowledge, this paper is the longest quantitative measurement of Sinophobia.
Identifying Competition and Mutualism between Online Groups
Authors: TeBlunthuis Nathan , Hill Benjamin Mako
Published: November 2022

Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutual-ism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this frame-work to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.
The Manufacture of Partisan Echo Chambers by Follow Train Abuse on Twitter
Authors: Torres-Lugo Christopher , Yang Kai-Cheng , Menczer Filippo
Published: November 2022

A growing body of evidence points to critical vulnerabilities of social media, such as the emergence of partisan echo chambers and the viral spread of misinformation. We show that these vulnerabilities are amplified by abusive behaviors associated with so-called "follow trains'' on Twitter, in which long lists of like-minded accounts are mentioned for others to follow. We present the first systematic analysis of a large U.S. hyper-partisan train network. We observe an artificial inflation of influence: accounts heavily promoted by follow trains profit from a median six-fold increase in daily follower growth. This catalyzes the formation of highly clustered echo chambers, hierarchically organized around a dense core of active accounts. Train accounts also engage in other behaviors that violate platform policies: we find evidence of activity by inauthentic automated accounts and abnormal content deletion, as well as amplification of toxic content from low-credibility and conspiratorial sources. Some train accounts have been active for years, suggesting that platforms need to pay greater attention to this kind of abuse.
An Automated Approach to Identifying Corporate Editing
Authors: Veselovsky Veniamin , Sarkar Dipto , Anderson Jennings , Soden Robert
Published: November 2022

OpenStreetMap (OSM) is the world’s largest peer-produced geospatial project. As a freely-editable open map of the world to which anyone may contribute or make use of, the dynamics and motivations of its contributors have been the object of significant scholarship. A growing phenomena in the OSM community is the increasing contributions of paid editing teams hired by tech corporations, such as, Microsoft, Apple, and Facebook. Though corporations have long supported OSM in various ways, the recent growth of teams of paid editors raises challenges to the community’s norms and policies, which are historically oriented around contributions by individual volunteer, making it hard to track the contribution of paid editors. This research addresses a fundamental problem in approaching these concerns: understanding the scale and character of corporate editing in OSM. We use machine-learning to improve upon prior approaches to estimating this phenomena, contributing both a novel methodology as well a more robust understanding of the latest corporate editing behavior in OSM.
From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations
Authors: Weinzierl Maxwell A. , Harabagiu Sanda M.
Published: November 2022

While billions of COVID-19 vaccines have been administered, too many people remain hesitant. Twitter, with its substantial reach and daily exposure, is an excellent resource for examining how people frame their vaccine hesitancy and to uncover vaccine hesitancy profiles. In this paper we expose our processing journey from identifying Vaccine Hesitancy Framings in a collection of 9,133,471 original tweets discussing the COVID-19 vaccines, establishing their ontological commitments, annotating the Moral Foundations they imply to the automatic recognition of the stance of the tweet authors toward any of the CoVaxFrames that we have identified. When we found that 805,336 Twitter users had a stance towards some CoVaxFrames in either the 9,133,471 original tweets or their 17,346,664 retweets, we were able to derive nine different Vaccine Hesitancy Profiles of these users and to interpret these profiles based on the ontological commitments of the frames they evoked in their tweets and on value of their stance towards the evoked frames.
Twitter-STMHD: An Extensive User-Level Database of Multiple Mental Health Disorders
Authors: Suhavi  , Singh Asmit Kumar , Arora Udit , Shrivastava Somyadeep , Singh Aryaveer , Shah Rajiv Ratn , Kumaraguru Ponnurangam
Published: November 2022

Social Media is equipped with the ability to track and quantify user behavior, establishing it as an appropriate resource for mental health studies. However, previous efforts in the area have been limited by the lack of data and contextually relevant information. There is a need for large-scale, well-labeled mental health datasets with fast reproducible methods to facilitate their heuristic growth. In this paper, we cater to this need by building the Twitter - Self-Reported Temporally-Contextual Mental Health Diagnosis Dataset (Twitter-STMHD), a large scale, user-level dataset grouped into 8 disorder categories and a companion class of control users. The dataset is 60% hand-annotated, which lead to the creation of high-precision self-reported diagnosis report patterns, used for the construction of the rest of the dataset. The dataset, instead of being a corpus of tweets, is a collection of user-profiles of those suffering from mental health disorders to provide a holistic view of the problem statement. By leveraging temporal information, the data for a given profile in the dataset has been collected for disease prevalence periods: onset of disorder, diagnosis and progression, along with a fourth period: COVID-19. This is the only and the largest dataset that captures the tweeting activity of users suffering from mental health disorders during the COVID-19 period.
Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street
Authors: Alvarez Richard , Bhatt Paras , Zhao Xingmeng , Rios Anthony
Published: November 2022

Who actually expresses an intent to buy shares of GameStop Corporation (GME) on Reddit? What convinces people to buy stocks? Are people convinced to support a coordinated plan to adversely impact Wall Street investors? Existing literature on understanding intent has mainly relied on surveys and self-reporting; however there are limitations to these methodologies. Hence, in this paper, we develop an annotated dataset of communications centered on the GameStop phenomenon to analyze the subscriber intention behaviors within the r/WallStreetBets community to buy (or not buy) stocks. Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop. Overall, our dataset can provide insight to social scientists on the persuasive power of social movements online by adopting common language and narrative. WARNING: This paper contains offensive language that commonly appears on Reddit's r/WallStreetBets subreddit.
DISMISS: Database of Indian Social Media Influencers on Twitter
Authors: Arya Arshia , De Soham , Mishra Dibyendu , Shekhawat Gazal , Sharma Ankur , Panda Anmol , Lalani Faisal , Singh Parantak , Mothilal Ramaravind Kommiya , Grover Rynaa , Nishal Sachita , Dash Saloni , Shora Shehla , Akbar Syeda Zainab , Pal Joyojeet
Published: November 2022

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed ``friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category of ``digital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarization, misinformation, extreme speech, political discourse etc.
Fauci-Email: A JSON Digest of Anthony Fauci’s Released Emails
Authors: Benson Austin R. , Veldt Nate , Gleich David F.
Published: November 2022

A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemic. In this paper, we describe how the original PDF document of emails was translated into a resource consisting of json files that make many future studies easy. We include examples for how to convert this email information into a network, a hypergraph, a temporal sequence, and a tensor for subsequent analysis, and discuss use cases and benefits in analyzing the data in these different derived formats. These resources are broadly useful for future research and pedagogical uses in terms of human and system behavioral interactions.
Drink Bleach or Do What Now? COVID-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation
Authors: Dharawat Arkin , Lourentzou Ismini , Morales Alex , Zhai ChengXiang
Published: November 2022

Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceive this severity, i.e., how harmful a message believed by the audience can be and what type of signals can be used to recognize potentially malicious fake news and detect refuted claims. To address our research questions, we introduce a new benchmark dataset, accompanied with detailed data analysis. We evaluate several traditional and state-of-the-art models, and show there is a significant gap in performance when applying traditional misinformation classification models to this task. We conclude with open challenges and future directions.
A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram
Authors: Hamlett Mara , Powell Grace , Silva Yasin N. , Hall Deborah
Published: November 2022

As online communication continues to become more prevalent, instances of cyberbullying have also become more common, particularly on social media sites. Previous research in this area has studied cyberbullying outcomes, predictors of cyberbullying victimization/perpetration, and computational detection models that rely on labeled datasets to identify the underlying patterns. However, there is a dearth of work examining the content of what is said when cyberbullying occurs and most of the available datasets include only basic labels (cyberbullying or not). This paper presents an annotated Instagram dataset with detailed labels about key cyberbullying properties, such as the content type, purpose, directionality, and co-occurrence with other phenomena, as well as demographic information about the individuals who performed the annotations. Additionally, results of an exploratory logistic regression analysis are reported to illustrate how new insights about cyberbullying and its automatic detection can be gained from this labeled dataset.
BelElect: A New Dataset for Bias Research from a “Dark” Platform
Authors: Höhn Sviatlana , Mauw Sjouke , Asher Nicholas
Published: November 2022

New social networks and platforms such as Telegram, Gab and Parler offer a stage for extremist, racist and aggressive content, but also provide a safe space for freedom fighters in authoritarian regimes. Data from such platforms offer excellent opportunities for research on issues such as linguistic bias and toxic language detection. However, only a few, mostly unannotated, English-only corpora from such platforms exist. This article presents a new Telegram corpus in Russian and Belorussian languages tailored for research on linguistic bias in political news. In addition, we created a repository to make all currently available corpora from so-called "dark" platforms accessible in one place.
Homepage2Vec: Language-Agnostic Website Embedding and Classification
Authors: Lugeon Sylvain , Piccardi Tiziano , West Robert
Published: November 2022

Currently, publicly available models for website classification do not offer an embedding method and have limited support for languages beyond English. We release a dataset of more than two million category-labeled websites in 92 languages collected from Curlie, the largest multilingual human-edited Web directory. The dataset contains 14 website categories aligned across languages. Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model for classifying and embedding websites based on their homepage in a language-agnostic way. Homepage2Vec, thanks to its feature set (textual content, metadata tags, and visual attributes) and recent progress in natural language representation, is language-independent by design and generates embedding-based representations. We show that Homepage2Vec correctly classifies websites with a macro-averaged F1-score of 0.90, with stable performance across low- as well as high-resource languages. Feature analysis shows that a small subset of efficiently computable features suffices to achieve high performance even with limited computational resources.
We make publicly available the curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec model, and libraries: https://github.com/epfl-dlab/homepage2vec.
On the Context-Free Ambiguity of Emoji
Authors: Częstochowska Justyna , Gligorić Kristina , Peyrard Maxime , Mentha Yann , Bień Michał , Grütter Andrea , Auer Anita , Xanthos Aris , West Robert
Published: November 2022

Due to their pictographic nature, emojis come with baked-in, grounded semantics. Although this makes emojis promising candidates for new forms of more accessible communication, it is still unknown to what degree humans agree on the inherent meaning of emojis when encountering them outside of concrete textual contexts. To bridge this gap, we collected a crowdsourced dataset (made publicly available) of one-word descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that, with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that the variation in their descriptions is as high as that in randomly chosen descriptions. Most emojis lie between these two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that emojis representing symbols from established, yet not cross-culturally familiar code books (e.g., zodiac signs, Chinese characters) are most ambiguous. We conclude by discussing design implications.
Evaluation of Fake News Detection with Knowledge-Enhanced Language Models
Authors: Whitehouse Chenxi , Weyde Tillman , Madhyastha Pranava , Komninos Nikos
Published: November 2022

Recent advances in fake news detection have exploited the success of large-scale pre-trained language models (PLMs). The predominant state-of-the-art approaches are based on fine-tuning PLMs on labelled fake news datasets. However, large-scale PLMs are generally not trained on structured factual data and hence may not possess priors that are grounded in factually accurate knowledge. The use of existing knowledge bases (KBs) with rich human-curated factual information has thus the potential to make fake news detection more effective and robust. In this paper, we investigate the impact of knowledge integration into PLMs for fake news detection. We study several state-of-the-art approaches for knowledge integration, mostly using Wikidata as KB, on two popular fake news datasets - LIAR, a politics-based dataset, and COVID-19, a dataset of messages posted on social media relating to the COVID-19 pandemic. Our experiments show that knowledge-enhanced models can significantly improve fake news detection on LIAR where the KB is relevant and up-to-date. The mixed results on COVID-19 highlight the reliance on stylistic features and the importance of domain specific and current KBs. The code is available at https://github.com/chenxwh/fake-news-detection.
Exploring Social Media Scenarios for the Television
Authors: Ali-Hasan Noor
Published: November 2022

The use of social technologies is becoming ubiquitous in the lives of average computer users. However, social media has yet to infiltrate users' television experiences. This paper presents the findings of an exploratory study examining social scenarios for TV. Eleven participants took part in the three-part study that included in-home field visits, a diary study of participants' daily usage of TV and social media, and participatory design sessions. During the participatory design sessions, participants evaluated and discussed several paper wireframes of potential social TV applications. Overall, participants responded to most social TV concepts with excitement and enthusiasm, but were leery of scenarios that they felt violated their privacy.
Document Representation and Query Expansion Models for Blog Recommendation
Authors: Arguello Jaime , Elsas Jonathan L. , Callan Jamie , Carbonell Jaime
Published: November 2022

We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia1 to expand a user's initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.
International Sentiment Analysis for News and Blogs
Authors: Bautin Mikhail , Vijayarenu Lohit , Skiena Steven
Published: November 2022

There is a growing interest in mining opinions using sentiment analysis methods from sources such as news, blogs and product reviews. Most of these methods have been developed for English and are difficult to generalize to other languages. We explore an approach utilizing state-of-the-art machine translation technology and perform sentiment analysis on the English translation of a foreign language text. Our experiments indicate that (a) entity sentiment scores obtained by our method are statistically significantly correlated across nine languages of news sources and five languages of a parallel corpus; (b) the quality of our sentiment analysis method is largely translator independent; (c) after applying certain normalization techniques, our entity sentiment scores can be used to perform meaningful cross-cultural comparisons.
Finding Influencers and Consumer Insights in the Blogosphere
Authors: Matsumura Naohiro , Yamamoto Hikaru , Tomozawa Daisuke
Published: November 2022

In this paper, we attempt to specify the influencer and the influential terms in consumer package goods by using the influence diffusion model (IDM). IDM calculates the spread of a term in the blogosphere recursively, and evaluates the influence of terms, blog entries, and bloggers. The model was applied to the official data from a blog hosting service. The results of the model were compared with the term frequency (TF). Then, the influencers of the IDM were examined and compared with the results of other network indices such as number of links and trackbacks. The results indicate that IDM performs better in finding blogs posted by bloggers with product usage experience or with an interest in the product. A propagation network which visualizes the consumer-oriented key marketing elements is also presented. An empirical analysis suggests the validity of IDM for marketing decision marking.
On the TREC Blog Track
Authors: Ounis Iadh , MacDonald Craig , Soboroff Ian
Published: November 2022

The rise of blogging as a new grassroots publishing medium and the many interesting peculiarities that characterise blogs compared to other genres of documents opened up several new interesting research areas in the information retrieval field. The Blog track was introduced in 2006 as part of the renowned Text REtrieval Conference (TREC) evaluation forum, to drive research on the blogosphere and to facilitate experimentation and evaluation of blog search techniques. This paper reports on two years of the Blog track at TREC. We describe the blog search tasks investigated at TREC, and discuss the main lessons we learnt from the track. We conclude the paper with discussions of the broader implications of the Blog track lessons and possible directions for the future, with the aim to uncover and explore the richness of information available in the blogosphere.
The Psychology of Word Use in Depression Forums in English and in Spanish: Testing Two Text Analytic Approaches
Authors: Ramirez-Esparza Nairan , Chung Cindy , Kacewic Ewa , Pennebaker James
Published: November 2022

The present studies demonstrate two computerized approaches to examining the expression of depression on the Internet. Study 1 observed linguistic markers of depression in English and Spanish forums. English and Spanish posts by depressed (N=160) and non-depressed individuals (N=160) were collected from Internet forums using bulletin board systems (bbs). A computer program (LIWC2001) was used to compare linguistic categories across groups within the English and Spanish language. Study 2 analyzed the themes people use when talking about their depression in forums in English and Spanish. Posts by English speaking (N=404) and Spanish speaking depressed people (N=400) were collected from bbs. An automated inductive approach to content analysis was used to compare how the expression of depression varies across cultures. The results for Study 1 showed that linguistic cues previously associated with depression were found to be higher in depressed than in non-depressed posts in English and Spanish. Study 2 showed that depressed people who wrote in Spanish were more likely to mention relational concerns than depressed people who wrote in English, who were more likely to mention medicinal concerns. Implications for informational and support needs and concerns in psychotherapy vs. online support across cultures are addressed and discussed.
Spontaneous Inference of Personality Traits and Effects on Memory for Online Profiles
Authors: Stecher Kristin , Counts Scott
Published: November 2022

As users navigate online social spaces, they encounter numerous personal profiles, each displaying a unique constellation of attributes. How do users make sense of this information? In our first study, we provide evidence that users spontaneously make personality trait inferences about people from profiles they encounter online, and for certain profiles, preferentially remember this inferred trait content over actual profile content. Study 2 uses several measures of profile coherence to assess how the coherence of user profiles interacts with trait inferences to influence memory for profiles. Findings provide a better understanding of specific profile content that makes profiles memorable and the social-cognitive process utilized when extracting information from profiles.
Understanding the Efficiency of Social Tagging Systems Using Information Theory
Authors: Chi Ed , Mytkowicz Todd
Published: November 2022

Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing any underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional categorization problem with ontologies? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources.
Communicative Informatics: A Social Media Perspective for Online Communities
Authors: Gallant Linda , Boone Gloria
Published: November 2022

Individuals are active audience members that use Internet-based social media technologies to create and negotiate social action in online spaces. Communicative informatics is the key to constructing, describing or critiquing social media. Communicative informatics is the discovery of the audience, text/image, technology, negotiated place relationships that create symbolic meaning. Four propositions focus on the communication of the audience: 1) the audience is active; 2) the audience is creative; 3) the audience interacts with technology and 4) place is negotiated in online communication.
Cross-Lingual Blog Analysis based on Multilingual Blog Distillation from Multilingual Wikipedia Entries
Authors: Kawaba Mariko , Nakasaki Hiroyuki , Utsuro Takehito , Fukuhara Tomohiro
Published: November 2022

The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog distillation (feed search) procedure. Mulitlingual queries for retrieving blog feeds are created from Wikipedia entries. Finally, we cross-lingually and cross-culturally compare less well known facts and opinions that are closely related to a given topic. Preliminary evaluation results support the effectiveness of the proposed framework.
A Categorical Model for Discovering Latent Structure in Social Annotations
Authors: Kashoob Said , Caverlee James , Ding Ying
Published: November 2022

The advent of social tagging systems has enabled a new community-based view of the Web in which objects like images, videos, and Web pages are annotated by thousands of users. Understanding the emergent semantics inherent in the socially-generated collection of annotations has important research implications for information discovery and knowledge sharing. To this end, we propose a novel probabilistic generative model for discovering latent structure in large-scale social annotations. The generative model identifies latent community-based ``categories'' of interest that can be used to group semantically-related tags and to augment traditional content-based information search and discovery. We illustrate the proposed approach over large collections of Web objects annotated by the Flickr and Delicious communities. Additionally, we show how to integrate the annotation-based categorical model with traditional content-based approaches for the effective focused discovery and exploration of Web objects.
Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations
Authors: Kessler Jason , Nicolov Nicolas
Published: November 2022

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query.  In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions.  The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity.  An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions --- this is the focus of our paper.  We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention.  We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets.  The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the top-level entity.  We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art.
Community Structure and Information Flow in Usenet: Improving Analysis with a Thread Ownership Model
Authors: McGlohon Mary , Hurst Matthew
Published: November 2022

Studying Usenet provides unique insight into online communities, since there is often a pre-defined space for community interaction. Here, we examine a large set of posts in nearly 200 politically-oriented newsgroups over a period of 4 years, using a unique and novel approach to the analysis. Our study is multi-scale: not only do we examine the newsgroups individually and compare measurements of different groups, but we also examine the relationships between the groups. Since users often post to multiple groups, shared participating authors or cross posts may be a way to assess how closely related, content-wise, two groups may be. However, this also causes some confusion of relevance. To combat this effect, we develop an “ownership” measure of an article to the set of cross-posted groups, based on the posting activity of the author. We show that this ownership measure can greatly improve assessment of information diffusion within and between groups, and suggest using it to improve analysis in other problems.
Personal Information Management vs. Resource Sharing: Towards a Model of Information Behavior in Social Tagging Systems
Authors: Heckner Markus , Heilemann Michael , Wolff Christian
Published: November 2022

Social tagging systems allow users to upload and assign keywords to digital resources. Thus a body of user annotated resources gradually evolves: Users can share resources, re-find their own resources or use the systems as search engines for items added by the whole user population. In this paper we want to contribute towards a better understanding of usage patterns within social tagging systems by presenting results from a survey of 142 users of the systems. Flickr, Youtube, Delicious and Connotea. Data was gathered partly by using the Mechanical Turk service, and partly via an announcement on the Connotea blog. Our study reveals differences of user motivation and tag usage between systems. While (resource) sharing emerges as an all-embracing intra-system motivation, users differ with respect to social spheres of sharing. Based on our results which we integrated with earlier research from Cool and Belkin (2002), we propose a model of information behaviour in social tagging systems.
Visualizing Cross-Lingual/Cross-Cultural Differences in Concerns in Multilingual Blogs
Authors: Nakasaki Hiroyuki , Kawaba Mariko , Yamazaki Sayuri , Utsuro Takehito , Fukuhara Tomohiro
Published: November 2022

The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword.  The framework of collecting multilingual blogs with a topic keyword is designed as the blog feed retrieval procedure.  Multilingual queries for retrieving blog feeds are created from Wikipedia entries.  Finally, we present an interface for visualizing cross-lingual/cross-cultural differences in concerns and opinions that are closely related to a given topic.  Preliminary evaluation results support the effectiveness of the proposed framework.