bluuebunny/crossref_metadata_2025 huggingface dataset:
Divided We Rule: Influencer Polarization on Twitter during Political Crises in India
Authors: Dash Saloni , Mishra Dibyendu , Shekhawat Gazal , Pal Joyojeet
Published: November 2022

Influencers are key to the nature and networks of information propagation on social media. Influencers are particularly important in political discourse through their engagement with issues, and may derive their legitimacy either solely or in large part through online operation, or have an offline sphere of expertise such as entertainers, journalists etc. To quantify influencers' political engagement and polarity, we use Google's Universal Sentence Encoder (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We then obtain aggregate vector representations of the influencers based on their tweet embeddings, which alongside retweet graphs help compute their stance and polarity with respect to these political issues. We find that influencers engage with the topics in a partisan manner, with polarized influencers being rewarded with increased retweeting and following. Moreover, we observe that specific groups of influencers are consistently polarized across all events. We conclude by discussing how our study provides insights into the political schisms of present-day India, but also offers a means to study the role of influencers in exacerbating political polarization in other contexts.
Time after Time: Longitudinal Trends in Nostalgic Listening
Authors: Hanson Clara , Anderton Jesse , Way Samuel F. , Anderson Ian , Wolf Scott , Wang Alice
Published: November 2022

Nostalgia was once considered a medical disease but is now understood as a beneficial, identity-affirming emotion. Yet what induces feelings of nostalgia is not fully understood. Are nostalgic feelings prompted by changes in a person’s life, and do certain events prompt nostalgia across whole populations? In this paper, we analyze when people listen to nostalgic music. We use data from a large, cross-national survey to train a classifier to detect which tracks are nostalgic for individual listeners. We then analyze a comprehensive dataset of listening histories from a 5.5 year period. Despite it being a complex concept, we were able to predict nostalgic listening with relatively high precision. We compare our results across listeners from four countries to understand how consistent behavior toward nostalgic music is. We find people listen to nostalgic music more often as they age. We also find people tend to listen to nostalgic music consistently in their day-to-day lives. We do not find evidence that listening to personally nostalgic music increases in response to particular traditions, seasons, or events. However, we do find traditions and events can affect how much “back catalog” music people listen to. These trends are consistent across the national contexts we studied. Our results advance prior findings about nostalgia and the life course, and demonstrate a novel methodological consideration for studies of nostalgic listening.
Two-Face: Adversarial Audit of Commercial Face Recognition Systems
Authors: Jaiswal Siddharth , Duggirala Karthikeya , Dash Abhisek , Mukherjee Animesh
Published: November 2022

Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance. Audits of these applications have revealed that they tend to be biased against minority groups which result in unfair and concerning societal and political outcomes. Despite multiple studies over time, these biases have not been mitigated completely and have in fact increased for certain tasks like age prediction. While such systems are audited over benchmark datasets, it becomes necessary to evaluate their robustness for adversarial inputs. In this work, we perform an extensive adversarial audit on multiple systems and datasets, making a number of concerning observations- there has been a drop in accuracy for some tasks on CelebSET dataset since a previous audit. While there still exists a bias in accuracy against individuals from minority groups for multiple datasets, a more worrying observation is that these biases tend to get exorbitantly pronounced with adversarial inputs toward the minority group. We conclude with a discussion on the broader societal impacts in light of these observations and a few suggestions on how to collectively deal with this issue.
Out of the Shadows: Analyzing Anonymous’ Twitter Resurgence during the 2020 Black Lives Matter Protests
Authors: Jones Keenan , Nurse Jason R.C. , Li Shujun
Published: November 2022

Until recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the murder of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. We first use machine learning to identify a significant network of more than 33,000 Anonymous accounts and use topic modelling of tweets collected from these accounts to find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference
Authors: Li Jiaxuan , Ning Yue
Published: November 2022

With the spreading of hate speech on social media in recent years, automatic detection of hate speech is becoming a crucial task and has attracted attention from various communities. This task aims to recognize online posts (e.g., tweets) that contain hateful information. The peculiarities of languages in social media, such as short and poorly written content, lead to the difficulty of learning semantics and capturing discriminative features of hate speech. Previous studies have utilized additional useful resources, such as sentiment hashtags, to improve the performance of hate speech detection. Hashtags are added as input features serving either as sentiment-lexicons or extra context information. However, our close investigation shows that directly leveraging these features without considering their context may introduce noise to classifiers. In this paper, we propose a novel approach to leverage sentiment hashtags to enhance hate speech detection in a natural language inference framework. We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts. The semantic relation inference aims to encourage the model to encode sentiment-indicative information into representations of online posts. We conduct extensive experiments on two real-world datasets and demonstrate the effectiveness of our proposed framework compared with state-of-the-art representation learning models.
Openness to Migrate Internationally for a Job: Evidence from LinkedIn Data in Europe
Authors: Perrotta Daniela , Johnson Sarah C. , Theile Tom , Grow André , Valk Helga de , Zagheni Emilio
Published: November 2022

Understanding the factors that explain why people move – or stay – and where they go, is a central goal of migration research. This article improves our understanding of migration aspirations of professionals in Europe by leveraging a previously untapped data source: aggregate-level information on LinkedIn users open to work-related international relocation, accessed through the LinkedIn Recruiter platform. We collected data at regular intervals from Oct. 2020 to Sept. 2021. First, we offer descriptive statistics of proxies for migration aspirations (or lack thereof) for millions of Linkedin users in Europe. Then we approach our questions using a standardization technique, based on gravity models of migration, in order to account for a number of biases in the data, including uneven use of LinkedIn across countries. We found that, in absolute terms, countries in Northern and Western Europe are the most attractive ones when considering LinkedIn users open to work-related relocation (about 60%), followed by Southern Europe (about 40%) and Eastern Europe (30%). We also observed substantial heterogeneity in directionality of aspirations: for example, roughly 20% of LinkedIn users would relocate from Western to Northern Europe, while less than 10% would relocate from Northern to Western Europe. After accounting for differences in population density, geographic and linguistic distances, as well as internet and LinkedIn penetration, we observed that, in relative terms, Southern Europe appears to be a highly desired destination for professionals, indicating that there is potential for changing patterns in actual flows, should more opportunities for professionals arise in Southern Europe.
Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions
Authors: Phadke Shruti , Samory Mattia , Mitra Tanushree
Published: November 2022

The disruptive offline mobilization of participants in online conspiracy theory (CT) discussions has highlighted the importance of understanding how online users may form radicalized conspiracy beliefs. While prior work researched the factors leading up to joining online CT discussions and provided theories of how conspiracy beliefs form, we have little understanding of how conspiracy radicalization evolves after users join CT discussion communities.
In this paper, we provide the empirical modeling of various radicalization phases in online CT discussion participants.
To unpack how conspiracy engagement is related to radicalization, we first characterize the users' journey through CT discussions via conspiracy engagement pathways.
Specifically, by studying 36K Reddit users through their 169M contributions, we uncover four distinct pathways of conspiracy engagement: steady high, increasing, decreasing, and steady low.
We further model three successive stages of radicalization guided by prior theoretical works.
Specific sub-populations of users, namely those on steady high and increasing conspiracy engagement pathways, progress successively through various radicalization stages. In contrast, users on the decreasing engagement pathway show distinct behavior: they limit their CT discussions to specialized topics, participate in diverse discussion groups, and show reduced conformity with conspiracy subreddits.
By examining users who disengage from online CT discussions, this paper provides promising insights about conspiracy recovery process.
Identifying and Characterizing New Expressions of Community Framing during Polarization
Authors: Sarmiento Hernan , Bravo-Marquez Felipe , Graells-Garrido Eduardo , Poblete Barbara
Published: November 2022

Chile experienced a series of important protests between October and December 2019. This social unrest, as it was called, was fueled by social inequity and radically affected the nation's status quo. A large portion of the population demanded a new Constitution and changes to the current government, whereas another part of the population rejected these social demands. This created a highly polarized scenario evidenced through online social media interactions. Analyzing controversial issues that emerge naturally from conversations in online communities can offer a more wide-scale understanding of today's political and societal discussions. Here, we analyze group polarization in social networks by studying the 2019 Chilean social unrest. Specifically, we propose an unsupervised approach for identifying and characterizing community framing (i.e., discovering and understanding polarized concepts). Our approach is based on the sequential application of community detection, topic modeling, and word embedding methods. The novelty of having an unsupervised approach is that it facilitates the performance of scalable and objective framing analyses with minimal human intervention, as it does not require prior domain or network knowledge. Using this methodology, we observe that an apparently similar conversation topic across communities can actually have completely different meanings to each group. We noted, for instance, that while an online community linked the term gente (people) with communism and terrorism, the other associated it with police and military oppression. In this direction, our work can help to contextualize real-world social issues in online platforms, describing how users discuss similar concepts with opposing views.
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks
Authors: Silva Samuel Henrique , Das Arun , Aladdini Adel , Najafirad Peyman
Published: November 2022

Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes.
One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations.
Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.
Overcoming Language Disparity in Online Content Classification with Multimodal Learning
Authors: Verma Gaurav , Mujumdar Rohit , Wang Zijie J. , Choudhury Munmun De , Kumar Srijan
Published: November 2022

Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. However, the development of advanced computational techniques and resources is disproportionately focused on the English language, sidelining a majority of the languages spoken globally. While existing research has developed better multilingual and monolingual language models to bridge this language disparity between English and non-English languages, we explore the promise of incorporating the information contained in images via multimodal machine learning. Our comparative analyses on three detection tasks focusing on crisis information, fake news, and emotion recognition, as well as five high-resource non-English languages, demonstrate that: (a) detection frameworks based on pre-trained large language models like BERT and multilingual-BERT systematically perform better on the English language compared against non-English languages, and (b) including images via multimodal learning bridges this performance gap. We situate our findings with respect to existing work on the pitfalls of large language models, and discuss their theoretical and practical implications.
TWikiL – the Twitter Wikipedia Link Dataset
Authors: Meier Florian
Published: November 2022

Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.
FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset
Authors: Sharma Shakshi , Agrawal Ekanshi , Sharma Rajesh , Datta Anwitaman
Published: November 2022

COVID-19, which was first detected in late 2019 in Wuhan, China, has spread to the rest of the world and is currently deemed a global pandemic. A flux of events triggered by a wide ranging set of factors such as virus mutations and waves of infections, imperfect medical and policy interventions, and vested interest driven political posturing all have created a continuous state of uncertainty and strife. In this verbile environment, misinformation and fake news thrive and propagate easily through the modern efficient all-pervading media and social media tools, resulting in an infodemic running its course in conjunction with the pandemic. In this work, we present a COVID-19 related dataset – FaCov – a compilation of fact-checking articles that examine and evaluate some of the most widely circulated rumors and claims concerning the coronavirus. We have collected articles from 13 very popular fact-checking sources, along with information about the articles and the vetted verity assigned to the claims being evaluated. We also share insights into the dataset to highlight and understand the major conversations and conflicts in narratives encompassing the pandemic.
PrivacyAlert: A Dataset for Image Privacy Prediction
Authors: Zhao Chenye , Mangat Jasmine , Koujalgi Sujay , Squicciarini Anna , Caragea Cornelia
Published: November 2022

Image privacy issues have become an important challenge as millions of images are being shared on social networking sites every day. Often due to users' lack of privacy awareness and social pressure, users' posted images reveal sensitive information and may be easily used to their detriment. To address these issues, several recent studies have proposed machine learning models to automatically identify whether an image contains private information. However, progress on this important task has been hampered by the absence of reliable, publicly available, up-to-date datasets. To this end, we introduce PrivacyAlert, a dataset developed from recent images extracted from Flickr and annotated with privacy labels (private or public). Our data collection process is based on state-of-the-art privacy taxonomy and captures a comprehensive set of image types of various sensitivity. We perform a comprehensive analysis of our dataset and report image privacy prediction results using classic and deep learning models to set the ground for future studies. Our dataset is publicly available at: https://doi.org/10.5281/zenodo.6406870.
Understanding Toxicity Triggers on Reddit in the Context of Singapore
Authors: Chong Yun Yu , Kwak Haewoon
Published: November 2022

While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world. In this work, we demonstrate that 1) it is possible to detect toxicity triggers in an Asian online community, and 2) toxicity triggers can be strikingly different between Western and Eastern contexts.
Competing to Share Expertise: the Taskcn Knowledge Sharing Community
Authors: Yang Jiang , Adamic Lada , Ackerman Mark
Published: November 2022

"Witkeys" are websites in China that form a rapidly growing web-based knowledge market. A user who posts a task also offers a small fee, and many other users submit their answers to compete. The Witkey sites fall in-between aspects of the now-defunct Google Answers (vetted experts answer questions for a fee) and Yahoo Answers (anyone can answer or ask a question). As such, these sites promise new possibilities for knowledge-sharing online communities, perhaps fostering the freelance marketplace of the future. In this paper, we investigate one of the biggest Witkey websites in China, Taskcn.com. In particular, we apply social network prestige measures to a novel construction of user and task networks based on competitive outcomes to discover the underlying properties of both users and tasks. Our results demonstrate the power of this approach: Our analysis allows us to infer relative expertise of the users and provides an understanding of the participation structure in Taskcn. The results suggest challenges and opportunities for this kind of knowledge sharing medium.
A Social Network Based Approach to Personalized Recommendation of Participatory Media Content
Authors: Seth Aaditeshwar , Zhang Jie
Published: November 2022

Given the rapid growth of participatory media content such as blogs, there is a need to design personalized recommender systems to recommend only useful content to users. We believe that in addition to producing useful recommendations, certain insights from media research such as simplification and opinion diversity in recommendations should form the foundations of such recommender systems, so that the behavior of the systems can be understood more closely, and modified if necessary. We propose and evaluate such a system based on a Bayesian user-model. We use the underlying social network of blog authors and readers to model the preference features for individual users. The initial results of our proposed solution are encouraging, and set the agenda for future research.
Wikipedia as an Ontology for Describing Documents
Authors: Syed Zareen , Finin Tim , Joshi Anupam
Published: November 2022

Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to predict concepts common to a set of documents. We describe several algorithms to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments demonstrate the feasibility of extending the category system with new concepts identified as a union of pages from the page link graph.
GroupMe! – Combining Ideas of Wikis, Social Bookmarking, and Blogging
Authors: Abel Fabian , Frank Mischa , Henze Nicola , Krause Daniel , Siehndel Patrick
Published: November 2022

The GroupMe! system provides a novel approach to social bookmarking. It enables users to create groups of arbitrary multimedia web resources and visualizes resources according to their media type. Therewith, content of a resource can be grasped immediately. Hence, GroupMe! groups form web sites -- comparable to Wikipedia pages -- that users visit in order to gather information about corresponding topics. Technically, the grouping of resources carries valuable information about web resources and their relations, and can be exploited to improve the mining of web content, e.g. for search and retrieval. GroupMe! is available via http://groupme.org.
Visualization of Social Interactions in Facebook
Authors: Okamoto Takashi , Buza Kyle
Published: November 2022

With the popularity of social networks such as Facebook, we have greatly extended our social interactions from the physical into the digital. With social networks we have an increase in interaction opportunities; however, how we experience the interaction is rather bland. Accompanying social interactions with interesting visual feedback is one way to enrich this new space. In our demo, we present E15:FB -- a visualization application which shows a graphical representation of social interactions with individual Facebook users. The application also provides alternative methods to navigate Facebook content beyond what is provided through the Facebook website, and creates new opportunities to interact with related content outside of Facebook.
ASL-STEM Forum: A Bottom-Up Approach to Enabling American Sign Language to Grow in STEM Fields
Authors: Bigham Jeffrey , Otero Daniel , DeWitt Jessica , Cavender Anna , Ladner Richard
Published: November 2022

Deaf and hard of hearing students studying advanced topics in Science, Technology, Engineering, and Mathematics (STEM) lack standard terminology to enable them to learn, discuss and contribute to their chosen fields. The ASL-STEM Forum enables the diverse, thinly-spread groups that are independently creating and us1ing terminology to come together using a community-based, video-enabled web resource. A common vocabulary would provide interpreters with consistent terminology and enable deaf scientists to more easily converse from a common basis. This paper discusses the implementation of the ASL-STEM Forum, describes our approach to building a community using the site, and overviews the unique opportunities it offers for observing a language developing from the bottom-up.
An Automatic Classification of Book Texts to User-Defined Tags
Authors: Givon Sharon , Wilson Theresa
Published: November 2022

We describe work on automatically assigning labels to books using user-defined tags as the label set. Using supervised learning and exploring both binary and multiclass classification, we train and test classifiers on several sets of features, focusing on the size of the sets, part-of-speech classes and named entities. Results indicate that a binary classifier, trained and tested on a feature space that consists of a limited selection of parts of speech as well as all frequent named entities, achieves a classification precision of 81%, significantly outperforming a baseline which assigns the top-10 most popular tags to each book.
Logsonomy — A Search Engine Folksonomy
Authors: Robert Jäschke Robert , Krause Beate , Hotho Andreas , Stumme Gerd
Published: November 2022

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.
Some Users Pack a Wallop: Measuring the Impact of Core Users on the Participation of Others in Online Social Systems
Authors: Lento Thomas , Gleave Eric , Smith Marc , Welser Howard
Published: November 2022

This study investigates how heavily active contributors affect recruitment and retention in online social systems. We find that core enthusiasts are more successful recruiters, their recruits are more likely to become enthusiasts, and interacting with enthusiasts makes users less likely to exit the system. We also find evidence that strong dyadic ties between non-enthusiasts help extend active careers in online social systems. Implications include considerations for community growth and retention based on the influence of these core users.
Extraction of Topical Consumer Products from Weblogs
Authors: Nagano Shinichi , Inaba Masumi , Mizoguchi Yumiko , Kawamura Takahiro
Published: November 2022

This paper proposes a new algorithm of associated topic extraction, which detects related topics in a collection of blog entries commenting on a specified topic. The main feature of the algorithm is to evaluate how important a topic is to the collection, according to the popularity of blog entries through Trackbacks and comments. Another feature is to utilize product ontology for excluding unrelated topics. Evaluation results show that the proposed algorithm can capture users' impressions of associated topics more accurately than TF-IDF.
StYLiD: Structure Your Own Linked Data
Authors: Shakya Aman , Takeda Hideaki , Wuwongse Vilas
Published: November 2022

Information sharing can be effective with structured data. The Semantic Web is mainly aimed at creating standard ontologies. However, users have different preferences and evolving requirements. It is not practical to attempt perfect schema definitions with strict constraints. Creating structured formats should be a collaborative and evolutionary process. Social software motivates wide participation by providing easy interface. We propose a system called StYLiD for sharing wide variety of structured information. Users freely define their own structured concepts. The system consolidates different versions defined by different users. Combined search and browsing can be done. Popular concepts gradually emerge from the concept cloud and stabilize. Concept definitions are flexible. The suggestive range for attribute values does not constrain the contributors. StYLiD generates unique dereferenceable URIs so that data items can form a linked data web. Structured data is embedded using RDFa.
From Generating to Mining: Automatically Scripting Conversations Using Existing Online Sources
Authors: Nichols Nathan , Gandy Lisa , Hammond Kristian
Published: November 2022

Hearing people argue opposing sides of an issue can be a useful way to understand the topic; however, these debates or conversations often don’t exist.  Unfortunately, generating interesting natural language conversations is a difficult problem and typically requires a deep model of both a domain and its language.  Fortunately, there is a huge amount of interesting text, written both by professional writers and amateurs, already available on the web.   In this paper, we describe a system that builds compelling conversations between two characters—not by generating wholly new natural language, but by gathering, assembling, and processing existing online textual content.  Our initial system authors conversations between two simulated movie reviewers, in a style similar to “Siskel and Ebert.”  Using various online repositories, the system searches for a variety of facts and opinions about a given film.  The system then uses this mined data to choose between various conversational templates and construct the dialogue.  Using this information, the system is able to generate natural-sounding, colorful conversations and arguments without a deep representation of the subject being discussed.
Using Transactional Information to Predict Link Strength in Online Social Networks
Authors: Kahanda Indika , Neville Jennifer
Published: November 2022

Many scientific fields analyzing and modeling social networks have focused on manually-collected datasets where the friendship links are sparse (due to the costs of collection) but relatively noise-free (i.e. they indicate strong relationships). In online social networks, where the notion of ``friendship'' is broader than what would generally be considered in sociological studies, the friendship links are denser but the links contain noisier information (i.e., some weaker relationships). However, the networks also contain additional transactional events among entities (e.g., communication, file transfers) that can be used to infer the true underlying social network. With this aim in mind, we develop a supervised learning approach to predict link strength from transactional information. We formulate this as a link prediction task and compare the utility of attribute-based, topological, and transactional features. We evaluate our approach on public data from the Purdue Facebook network and show that we can accurately predict strong relationships. Moreover, we show that transactional-network features are the most influential features for this task.
A Utility for Estimating the Relative Contributions of Wiki Authors
Authors: Arazy Ofer , Stroulia Eleni
Published: November 2022

Wikis were originally designed to hide the association between a wiki page and the authors who have produced it. However, there is evidence suggesting that corporate wiki users require an attribution mechanism that would automatically record (and present) the relative contribution of each author. In this paper we introduce an algorithm for assessing the contributions of wiki authors that is based on the notion of sentence ownership. The results of an empirical evaluation comparing the algorithm’s output to manual evaluations reveal the type of contributions captured by our algorithm. Implications for research and practice are discussed.
Collecting and Sharing Observations with Semantic Support
Authors: Vainikainen Sari , Nummiaho Antti , Bäck Asta , Laakko Timo
Published: November 2022

We present two applications that can be used to store and share ideas, bookmarks and observations from the web and on the move. These applications utilize semantic web tech­nologies both to support users in tagging and to store and integrate data. The core of the system is a social book­mark­ing application, Tilkut, complemented with a mobile application TagIt, which can be used to send photo and text entries from a mobile device. Tag suggestions are given from external ontologies, and from earlier tags.  TagIt stores its data in an RDF database, which is also used in integrating these applications. The ontology for the RDF database combines existing social media ontologies, and its key structures are presented. The paper shares our experiences from linking and using external ontologies, and its special challenges on mobile applications.
Salsa: Leveraging Email to Create a Social Network for the Enterprise
Authors: Stecher Kristin , Counts Scott , Cheng Lili , Williams Shane , Turski Andrzej
Published: November 2022

In this paper we describe Salsa, a plug-in that aggregates social and other content into an email client. Information in Salsa is collected from a variety of sources both inside and outside of email to create a profile for each person in our corporation that contains a photo, organizational details, friends, and an aggregated feed of shared content. Thus, Salsa uses a social networking approach to add social awareness to email. Salsa was deployed internally within our corporation and we trace its growth for six months and describe the results of a targeted user study. Findings demonstrate the extent to which and what type of social awareness is useful in our organization. Use of the aggregated feed is also discussed. In addition to identifying features that are useful for social networks in the enterprise, we explore the benefits of situating this type of application in email.
Connecting Users with Similar Interests across Multiple Web Services
Authors: Kwak Haewoon , Shin Hwa-Yong , Yoon Jong-Il , Moon Sue
Published: November 2022

Most online social networking services provide a feature for users to build groups.  The web service has both user profiles describing user interests and behavior data such as browsing contents.   It can assist users to join groups by recommending relevant groups.  In this paper, we have proposed a novel method to connect users across multiple services based on user-labeled tags.  Tags represent interests of a user and have advantages in terms of the privacy, up-to-dateness, and service coverage.  We have collected tags from six popular web services.  We have analyzed tag usage patterns and observed that the popularity of tags is highly skewed and dependent on the web services.  We have also found that frequently used tags of a single user change over time.  Through user study, we show that the vector space model combined with intra-personomy normalization is the promising to find other users with similar interests.
Characterization of FriendFeed — A Web-based Social Aggregation Service
Authors: Gupta Trinabh , Garg Sanchit , Mahanti Anirban , Carlsson Niklas , Arlitt Martin
Published: November 2022

Many Web users have accounts with multiple different social networking services. This scenario has prompted development of "social aggregation" services such as FriendFeed that aggregate the information available through various services. Using five weeks of activity of more than 100,000 FriendFeed users, we consider questions such as what types of services users aggregate content from, the relative popularity of services, who follows the aggregated content feeds, and why.
An Examination of Language Use in Online Dating Profiles
Authors: Nagarajan Meenakshi , Hearst Marti
Published: November 2022

This paper contributes to the study of self-presentation in online dating systems by performing a factor analysis on the text portions of online profiles. Findings include a similarity in the overall factor structures between male and female profiles, including use of tentative words by men. Contrasts between sexes were also found in a cluster analysis of the profiles using their factor scores. Finally, we also found similarities in frequent words used by the gender groups.
JAM: Joint Action Matrix Factorization for Summarizing a Temporal Heterogeneous Social Network
Authors: Lin Yu-Ru , Sundaram Hari , Kelliher Aisling
Published: November 2022

This paper presents JAM (Joint Action Matrix Factorization), a novel framework to summarize social activity from rich media social networks. Summarizing social network activities requires an understanding of the relationships among concepts, users, and the context in which the concepts are used. Our work has three contributions: First, we propose a novel summarization method which extracts the co-evolution on multiple facets of social activity – who (users), what (concepts), how (actions) and when (time), and constructs a context rich summary called "activity theme". Second, we provide an efficient algorithm for mining activity themes over time. The algorithm extracts representative elements in each facet based on their co-occurrences with other facets through specific actions. Third, we propose new metrics for evaluating the summarization results based on the temporal and topological relationship among activity themes. Extensive experiments on real-world Flickr datasets demonstrate that our technique significantly outperforms several baseline algorithms. The results explore nontrivial evolution in Flickr photo-sharing communities.
Emotionality in Text as Predictor of Behavior
Authors: Onn Elliot , Morewedge Carey
Published: November 2022

The present study demonstrates how the emotional content of search terms and their eventual results affects the breadth of a users’ search for information. We observed the quantity of results selected by users. In a random sample of queries from the Microsoft LiveSearch search engine, 7,021 queries were evaluated using a dictionary with valence and arousal ratings. The number of search results selected was regressed on the valence and arousal of the search terms. We additionally observed users’ selection of results based on the position in the search results. Using the same sample, result placement was regressed on the valence and arousal level of the search terms. Results from quantity of search results selected shows that negative search terms result in an overall larger number of selections made than positive search terms. For position-based selections, we found that the selection of the first result is affected by an interaction of valence and arousal. Specifically, users were unaffected by the arousal level of negative search terms, but appeared to be more likely to search deeper on the page when they searched for less arousing positive information. These results suggest that the emotional content associated with a search query may lead users to be more or less discriminating in their acceptance of information and may influence the impact of placement on result selection.
I Think You Know Who I Am: Bloggers' Perceived Identifiability
Authors: Jang Chyng-Yang , Stefanone Michael
Published: November 2022

Bloggers who conduct relational communication via their public posts on the Web have to make decisions regarding the degree to which they would like to reveal their real-world identity. On the one hand, disclosing identity related information online can be risky. On the other hand, being identifiable is essential for positive relational communication. This study explored factors influencing bloggers’ decision of identity disclosure. Data were collected via an online survey of 148 randomly selected bloggers. Results showed that self disclosure tendency and targeting blogs at family and close friends were positive predictors to bloggers’ perceived identifiability. In addition, extraversion was found to moderate the relationship between bloggers’ perceived vulnerability and identifiability.
Star Quality: Aggregating Reviews to Rank Products and Merchants
Authors: McGlohon Mary , Glance Natalie , Reiter Zach
Published: November 2022

Given a set of reviews of products or merchants from a wide range of authors and several reviews websites, how can we measure the true quality of the product or merchant?  How do we remove the bias of individual authors or sources?  How do we compare reviews obtained from different websites, where ratings may be on different scales (1-5 stars, A/B/C, etc.)?  How do we filter out unreliable reviews to use only the ones with ``star quality''?  Taking into account these considerations, we analyze data sets from a variety of different reviews sites (the first paper, to our knowledge, to do this). These data sets include 8 million product reviews and 1.5 million merchant reviews. We explore statistic- and heuristic- based models for estimating the true quality of a product or merchant, and compare the performance of these estimators on the task of ranking pairs of objects.  We also apply the same models to the task of using Netflix ratings data to rank pairs of movies, and discover that the performance of the different models is surprisingly similar on this data set.
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?
Authors: De Choudhury Munmun , Lin Yu-Ru , Sundaram Hari , Candan Kasim Selcuk , Xie Lexing , Kelliher Aisling
Published: November 2022

Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion.We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%.
Toward Social Causality: An Analysis of Interpersonal Relationships in Online Blogs and Forums
Authors: Girju Roxana
Published: November 2022

In this paper we present encouraging preliminary results into the problem of social causality (causal reasoning used by intelligent agents in a social environment) in online social interactions based on a model of reciprocity. At every level, social relationships are guided by the shared understanding that most actions call for appropriate reactions, and that inappropriate reactions require management. Thus, we present an analysis of interpersonal relationships in English reciprocal contexts. Specifically, we rely here on a large and recently built database of 10,882 reciprocal relation instances in online media. The resource is analyzed along a set of novel and important dimensions: symmetry, affective value, gender}, and {\em intentionality of action which are highly interconnected. At a larger level, we automatically generate {\em chains of causal relations} between verbs indicating interpersonal relationships. Statistics along these dimensions give insights into people's behavior, judgments, and thus their social interactions.
Connecting the Dots: When Personal Information Becomes Personally Identifying on the Internet
Authors: Yates Dave , Shute Mark , Rotman Dana
Published: November 2022

With online social media such as weblogs (blogs), authors seemingly control how much self-identifying information they disclose. However we find that that even authors who wish to remain anonymous will share expressive and access-enabling information which, when combined, can be used to positively identify the person. In a case study of three anonymous blogs we demonstrate how to combine investigative analysis with statistical techniques to identify anonymous authors with a high degree of accuracy. Paradoxically, anonymous authors feel as if they can be honest and open with their thoughts and opinions, and thus may be more likely to share more information than they might if their identities were known.