bluuebunny/crossref_metadata_2025 huggingface dataset:
Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter
Authors: Chen Lu , Wang Wenbo , Nagarajan Meenakshi , Wang Shaojun , Sheth Amit
Published: November 2022

The problem of automatic extraction of sentiment expressions from informal text, as in microblogs such as tweets is a recent area of investigation. Compared to formal text, such as in product reviews or news articles, one of the key challenges lies in the wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns. In this work, we present an optimization-based approach to automatically extract sentiment expressions for a given target (e.g., movie, or person) from a corpus of unlabeled tweets. Specifically, we make three contributions: (i) we recognize a diverse and richer set of sentiment-bearing expressions in tweets, including formal and slang words/phrases, not limited to pre-specified syntactic patterns; (ii) instead of associating sentiment with an entire tweet, we assess the target-dependent polarity of each sentiment expression. The polarity of sentiment expression is determined by the nature of its target; (iii) we provide a novel formulation of assigning polarity to a sentiment expression as a constrained optimization problem over the tweet corpus. Experiments conducted on two domains, tweets mentioning movie and person entities, show that our approach improves accuracy in comparison with several baseline methods, and that the improvement becomes more prominent with increasing corpus sizes.
The Social World of Twitter: Topics, Geography, and Emotions
Authors: Quercia Daniele , Capra Licia , Crowcroft Jon
Published: November 2022

Debate is open as to whether social media communities resemble real-life communities, and to what extent. We contribute to this discussion by testing whether established sociological theories of real-life networks hold in Twitter. In particular, for 228,359 Twitter profiles, we compute network metrics (e.g., reciprocity, structural holes, simmelian ties) that the sociological literature has found to be related to parts of one's social world (i.e., to topics, geography and emotions), and test whether these real-life associations still hold in Twitter. We find that, much like individuals in real-life communities, social brokers (those who span structural holes) are opinion leaders who tweet about diverse topics, have geographically wide networks, and express not only positive but also negative emotions. Furthermore, Twitter users who express positive (negative) emotions cluster together, to the extent of having a correlation coefficient between one's emotions and those of friends as high as 0.45. Understanding Twitter's social dynamics does not only have theoretical implications for studies of social networks but also has practical implications, including the design of self-reflecting user interfaces that make people aware of their emotions, spam detection tools, and effective marketing campaigns.
Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency
Authors: Quercia Daniele , Las Casas Diego , Pesce Joao Paulo , Stillwell David , Kosinski Michal , Almeida Virgilio , Crowcroft Jon
Published: November 2022

Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits. We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
The Pulse of News in Social Media: Forecasting Popularity
Authors: Bandari Roja , Asur Sitaram , Huberman Bernardo
Published: November 2022

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.
So.cl: An Interest Network for Informal Learning
Authors: Farnham Shelly , Lahav Michal , Raskino David , Cheng Lili , Laird-McConnell Tom
Published: November 2022

Web search engines emerged prior to the dominance of social media. What if we imagined search as integrating with social media from the ground up? So.cl is a web application that combines web browsing, search, and social networking for the purposes of sharing and learning around topics of interest. In this paper, we present the results of a deployment study examining existing learning practices around search and social networking for students, and how these practices shifted when participants adopted So.cl. We found prior to using So.cl that students already heavily employed search tools and social media for learning. With the use of So.cl, we found that users engaged in lightweight, fun social sharing and learning for informal, personal topics, but not for more heavyweight collaboration around school or work. The public nature of So.cl encouraged users to post search results as much for self-expression as for searching, enabling serendipitous discovery around interests.
If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections
Authors: Giglietto Fabio
Published: November 2022

Is it possible to forecast electoral results by analyzing social media conversations? This paper aims to contribute to the debate raised by authors claiming to have successfully predicted the outcomes of an election from Twitter or Facebook data. Our work tested the purported predictive power of social media metrics against the 2011 Italian administrative elections. During the months before the election day, we collected the amount of Likes received on the Facebook pages of 229 candidates running for the mayor offices of the 29 provincial capitals. We built two forecast models with the goal of predicting the outcomes of the elections: the aim of the first one was to predict the candidates’ vote shares, while the second was to forecast the name of the winning candidate. We found a non-significant correlation between the share of candidate popularity on Facebook and the respective share of votes. However, in 39% of the cases, the most popular candidate on Facebook actually won the contest, and in another 43%, that candidate came in second. The contribution to the ongoing debate is therefore two-sided: on the one hand, we provide a new case study from a cultural context and political system never analyzed before by this kind of study; on the other side, we propose two forecasting models that, although proven to be partially unsuccessful, can provide a foundation for improved forecasting models.
Talk of the City: Our Tweets, Our Community Happiness
Authors: Quercia Daniele , O Seaghdha Diarmuid , Crowcroft Jon
Published: November 2022

The literature of urban sociology and that of psychology have separately established two relationships: the first has linked characteristics of a community to its residents’ well-being, the second has linked well-being of individuals to their use of words. No one has hitherto explored the potential transitive relationship - that between characteristics of a community and its residents' use of words. We test this relationship by performing three steps. We consider Twitter users in a variety of London census communities; extract the subject matter of their tweets using "topic models"; and study the relationship between topics and community socio-economic well-being. We find that certain topics are correlated (positively and negatively) with community deprivation. Users in more deprived community tweet about wedding parties, matters expressed in Spanish/Portuguese, and celebrity gossips. By contrast, those in less deprived communities tweet about vacations, professional use of social media, environmental issues, sports, and health issues. We finally show that monitoring the subject matter of tweets not only offers insights into community well-being, but it is also a reasonable way of predicting community deprivation scores.
Do Linguistic Style and Readability of Scientific Abstracts Affect their Virality?
Authors: Guerini Marco , Pepe Alberto , Lepri Bruno
Published: November 2022

Reactions to textual content posted in an online social net- work show different dynamics depending on the linguistic style and readability of the submitted content. Do similar dy- namics exist for responses to scientific articles? Our intuition, supported by previous research, suggests that the success of a scientific article depends on its content, rather than on its linguistic style. In this article, we examine a corpus of sci- entific abstracts and three forms of associated reactions: ar- ticle downloads, citations, and bookmarks. Through a class- based psycholinguistic analysis and readability indices tests, we show that certain stylistic and readability features of ab- stracts clearly concur in determining the success and viral ca- pability of a scientific article.
A Supervised Approach to Predict Company Acquisition with Factual and Topic Features Using Profiles and News Articles on TechCrunch
Authors: Xiang Guang , Zheng Zeyu , Wen Miaomiao , Hong Jason , Rose Carolyn , Liu Chao
Published: November 2022

Merger and Acquisition (M&A) prediction has been an interesting and challenging research topic in the past a few decades. However, past work has only adopted numerical features in building models, and yet the valuable textual information from the great variety of social media sites has not been touched at all. To fully explore this information, we used the profiles and news articles for companies and people on TechCrunch, the leading and largest public database for the tech world, which anybody can edit. Specifically, we explored topic features via topic modeling techniques, as well as a set of other novel features of our design within a machine learning framework. We conducted experiments of the largest scale in the literature, and achieved a high true positive rate (TP) between 60% to 79.8% with a false positive rate (FP) mostly between 0% and 8.3% over company categories with a small number of missing attributes in the CrunchBase profiles.
Enhancing Event Descriptions through Twitter Mining
Authors: Tanev Hristo , Ehrmann Maud , Piskorski Jakub , Zavarella Vanni
Published: November 2022

We describe a simple IR approach for linking news about events, detected by an event extraction system, to messages from Twitter (tweets). In particular, we explore several methods for creating event-specific queries for Twitter and provide a quantitative and qualitative evaluation of the relevance and usefulness of the information obtained from the tweets. We showed that methods based on utilization of word co-occurrence clustering, domain-specific keywords and named entity recognition improve the performance with respect to a basic approach.
Stuff IBMers Say: Microblogs as an Expression of Organizational Culture
Authors: Thom Jennifer , Millen David
Published: November 2022

In globally distributed enterprises, face-to-face communication of the norms and values underlying organizational culture may not be feasible. As a result, enterprises have turned to social media as a potential resource for acculturation, the process in which employees make sense of organizational culture. In this paper, we explore the emergence of a Twitter hashtag (#stuffibmerssay) to characterize user participation and content of the tagged tweets, and describe the motivation of active contributors. Our analysis reveals that participants tended to share often humorous observations of everyday work practices indicative of a highly technical global workforce and interviews with participants suggested that the resulting tweets help reinforce a shared sense of group identity with other members of the organization. We conclude by discussing the implications of emergent social media as expressions of organizational culture, particularly when it occurs outside of the intranet.
Docs.com: Social File Sharing in Facebook
Authors: Farnham Shelly , Turski Andrzej , Halai Sameer
Published: November 2022

Social networking sites provide a unique opportunity for users to engage in broadcast online file sharing and collaboration. In this study we examine the usage of Docs.com, which integrates Microsoft Office web documents with Facebook. In reviewing the content of Docs.com documents, we found they were equally created and shared for work, personal productivity, and play. Users actively employed relationship levels in their Facebook networks to define access to their documents. However, we also found the most viewed documents were those shared to the entire public, with Facebook notifications driving most of the traffic to these public documents. Finally, through a factor analysis of user activity four primary types of usage emerged – sharing, creating, collaborating, and consuming. Those who created and collaborated around documents had higher levels of engagement than those who just shared or consumed documents, interacting with more documents and returning on more unique days.
How You Met Me
Authors: Adamic Lada , Lento Thomas , Fiore Andrew
Published: November 2022

A popular Facebook meme asks a user's friends to recall how they met that user and then to paste the same query in their own status. We study the spread of this particular meme, which engaged millions of Facebook users, and the insights into relationship formation that the resulting compilation of answers provides. We describe the locations, relationships, and circumstances that contribute to formations of friendships that are represented on Facebook.
De-Layering Social Networks by Shared Tastes of Friendships
Authors: Dietz Laura , Gamari Ben , Guiver John , Snelson Edward , Herbrich Ralf
Published: November 2022

Traditionally, social network analyses are applied to data from a particular social domain. With the advent of online social networks such as Facebook, we observe an aggregate of various social domains resulting in a layered mix of professional contacts, family ties, and different circles. These aggregates dilute the community structure. We provide a method for de-layering social networks according to shared interests. Instead of relying on changes in the edge density, our shared taste model uses content of users to disambiguate the underlying shared interest of each friendship. We successfully de-layer real world networks from LibraryThing and Boards.ie, obtaining topics that significantly outperform LDA on unsupervised prediction of group membership.
Organizers
Authors: Spangenberg Jochen
Published: November 2022

Organizers of the Potential of Social Media Tools and Data for Journalists in the News Media Industry workshop.
Hybrid Browser / Server Collection of Streaming Social Media Data for Scalable Real-Time Analysis
Authors: Vick Lance , Soporan Titus , Lewis Daniel , Zurn Jane
Published: November 2022

We present a novel approach to collecting and distributing social media data in web service projects using both clients and servers for real-time analysis, ultimately providing an inexpensive and scalable method of a quality that has not been available to date. Current challenges to social data mining include vendor enforced API limits and infrastructure costs. Our hybrid client / server approach allows data to be collected via JavaScript in browsers as well as by servers. This allows applications to compute a wide range of data analytics. We present pure client and server based collection strategies, then demonstrate how our method has substantial advantages over both. Specific advantages include lower infrastructure requirements and greater efficiency in API utilization. Our approach distributes the majority of data collection tasks to client web browsers while using servers to supply more complex analysis techniques. In addition, we provide details on two open source tools we have released to facilitate implementation by researchers in their own projects. We close by detailing a use case scenario describing a large scale public web service project followed by a solution accomplished using our approach and open source tools.
Preface
Authors: Zubiaga Arkaitz
Published: November 2022

The Real-Time Analysis and Mining of Social Streams workshop aims to bring together experts in the real-time analysis and mining of social streams, as well as to further develop and exchange knowledge around these tasks.
Visualizing Information Diffusion and Polarization with Key Statements
Authors: Salway Andrew , Diakopoulos Nicholas , Elgesem Dag
Published: November 2022

This paper reports ongoing work in the “Networks of Texts and People” project, which is developing methods to visualize the social and epistemological contexts of information contained in blogs. Here, we propose an approach to visualize information diffusion and polarization in the blogosphere, with two novel characteristics. Firstly, we demonstrate how text content can be analyzed and visualized as key statements, rather than as keywords. Secondly, we sketch and discuss ideas for a visual analytic tool that integrates data about blog networks with data about the occurrence of related key statements in blog posts.
A Temporal Analysis of Posting Behavior in Social Media Streams
Authors: Lee Bumsuk
Published: November 2022

In this work, we investigated the social media streams to understand their characteristics and their temporal aspects. We assumed that each blogger has different temporal preference for posting. To investigate this hypothesis, we analyzed a massive dataset, nearly 700,000 blog articles, with the consideration of two factors which are day of the week and time of the day. The comparison was done in manifold ways: Blogosphere vs. Twitter, commercial blogs vs. non-commercial blogs, and their individuals. We hope that this work provides a hint to develop a personalized system which can be used for the reduction of the system resources for pull/fetch technology.
Organizers
Authors: Daly Elizabeth
Published: November 2022

List of organizers of the When the City Meets the Citizen workshop.
Conference Committee
Authors: Kiciman Emre
Published: November 2022

Committee members for the 2013 AAAI  International Conference on Weblogs and Social Media.
Combating Threats to Collective Attention in Social Media: An Evaluation
Authors: Lee Kyumin , Kamath Krishna , Caverlee James
Published: November 2022

Breaking news, viral videos, and popular memes are all examples of the collective attention of huge numbers of users focusing in large-scale social systems. But this self-organization, leading to user attention quickly coalescing and then collectively focusing around a phenomenon, opens these systems to new threats like collective attention spam. Compared to many traditional spam threats, collective attention spam relies on the insidious property that users themselves will intentionally seek out the content where the spam will be encountered, potentially magnifying its effectiveness. Our goal in this paper is to initiate a study of this phenomenon. How susceptible are social systems to collective attention threats? What strategies by malicious users are most effective? Can a system automatically inoculate itself from emerging threats? Towards beginning our study of these questions, we take a two fold approach. First, we develop data-driven models to simulate large-scale social systems based on parameters derived from a real system. In this way, we can vary parameters — like the fraction of malicious users in the system, their strategies, and the countermeasures available to system operators — to explore the resilience of these systems to threats to collective attention. Second, we pair the data-driven model with a comprehensive evaluation over a Twitter system trace, in which we evaluate the effectiveness of countermeasures deployed  based on the first moments of a bursting phenomenon in a real system. Our experimental study shows the promise of these countermeasures to identifying threats to collective attention early in the lifecycle, providing a shield for unsuspecting social media users.
Competition and Success in the Meme Pool: A Case Study on Quickmeme.com
Authors: Coscia Michele
Published: November 2022

The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a ``meme''. Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful.
Detecting Comments on News Articles in Microblogs
Authors: Kothari Alok , Magdy Walid , Darwish Kareem , Mourad Ahmed , Taei Ahmed
Published: November 2022

A reader of a news article would often be interested in the comments of other readers on an article, because comments give insight into popular opinions or feelings toward a given piece of news. In recent years, social media platforms, such as Twitter, have become a social hub for users to communicate and express their thoughts. This includes sharing news articles and commenting on them. In this paper, we propose an approach for identifying “comment-tweets” that comment on news articles. We discuss the nature of comment-tweets and compare them to subjective tweets. We utilize a machine learning-based classification approach for distinguishing between comment-tweets and others that only report the news. Our approach is evaluated on the TREC-2011 Microblog track data after applying additional annotations to tweets containing comments. Results show the effectiveness of our classification approach. Furthermore, we demonstrate the effectiveness of our approach on live news articles.
Exploiting Burstiness in Reviews for Review Spammer Detection
Authors: Fei Geli , Mukherjee Arjun , Liu Bing , Hsu Meichun , Castellanos Malu , Ghosh Riddhiman
Published: November 2022

Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are often related in the sense that spammers tend to work with other spammers and genuine reviewers tend to appear together with other genuine reviewers. This paves the way for us to build a network of reviewers appearing in different bursts. We then model reviewers and their co-occurrence in bursts as a Markov Random Field (MRF), and employ the Loopy Belief Propagation (LBP) method to infer whether a reviewer is a spammer or not in the graph. We also propose several features and employ feature induced message passing in the LBP framework for network inference. We further propose a novel evaluation method to evaluate the detected spammers automatically using supervised classification of their reviews. Additionally, we employ domain experts to perform a human evaluation of the identified spammers and non-spammers. Both the classification result and human evaluation result show that the proposed method outperforms strong baselines, which demonstrate the effectiveness of the method.
#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags
Authors: Lin Yu-Ru , Margolin Drew , Keegan Brian , Baronchelli Andrea , Lazer David
Published: November 2022

We examine the growth, survival, and context of 256 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. We propose a "conversational vibrancy" framework to capture dynamics of hashtags based on their topicality, interactivity, diversity, and prominence. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between features of this framework and the relative success of hashtags. Specifically, retweets always contribute to faster hashtag adoption, replies extend the life of "winners" while having no effect on "also-rans." This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks. We draw on theories of uses and gratification, organizational ecology, and language evolution to discuss these findings and their implications for understanding social influence and collective action in social media more generally.
What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media
Authors: Lakkaraju Himabindu , McAuley Julian , Leskovec Jure
Published: November 2022

Creating, placing, and presenting social media content is a difficult problem. In addition to the quality of the content itself, several factors such as the way the content is presented (the title), the community it is posted to, whether it has been seen before, and the time it is posted determine its success. There are also interesting \emph{interactions} between these factors. For example, the language of the title should be targeted to the community where the content is submitted, yet it should also highlight the distinctive nature of the content. In this paper, we examine how these factors interact to determine the popularity of social media content. We do so by studying resubmissions, i.e., content that has been submitted multiple times, with multiple titles, to multiple different communities. Such data allows us to `tease apart' the extent to which each factor influences the success of that content. The models we develop help us understand how to better target social media content: by using the right title, for the right community, at the right time.
On the Interplay between Social and Topical Structure
Authors: Romero Daniel , Tan Chenhao , Ugander Johan
Published: November 2022

People’s interests and people’s social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks — who connects with whom — and the set structure of topical affiliations — who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, “hashtags” are used to label conversation topics, and we examine hashtag usage alongside these social structures. We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.
New Insights and Methods For Predicting Face-To-Face Contacts
Authors: Scholz Christoph , Atzmueller Martin , Barrat Alain , Cattuto Ciro , Stumme Gerd
Published: November 2022

The prediction of new links in social networks is a challeng- ing task. In this paper, we focus on predicting links in net- works of face-to-face spatial proximity by using information from online social networks, such as co-authorship networks in DBLP, and a number of node level attributes. First, we analyze influence factors for the link prediction task. Then, we propose a novel method that combines information from different networks and node level attributes for the pre- diction task: We introduce an unsupervised link prediction method based on rooted random walks, and show that it out- performs state-of-the-art unsupervised link prediction meth- ods. We present an evaluation using three real-world datasets. Furthermore, we discuss the impact of our results and of the insights we glean in the field of link prediction and human contact behavior.
Emoticon Style: Interpreting Differences in Emoticons Across Cultures
Authors: Park Jaram , Barash Vladimir , Fink Clay , Cha Meeyoung
Published: November 2022

Emoticons are a key aspect of text-based communication, and are the equivalent of nonverbal cues to the medium of online chat, forums, and social media like Twitter. As emoticons become more widespread in computer mediated communication, a vocabulary of different symbols with subtle emotional distinctions emerges especially across different cultures. In this paper, we investigate the semantic, cultural, and social aspects of emoticon usage on Twitter and show that emoticons are not limited to conveying a specific emotion or used as jokes, but rather are socio-cultural norms, whose meaning can vary depending on the identity of the speaker. We also demonstrate how these norms propagate through the Twitter @-reply network. We confirm our results on a large-scale dataset of over one billion Tweets from different time periods and countries.
When Will You Answer This? Estimating Response Time in Twitter
Authors: Mahmud Jalal , Chen Jilin , Nichols Jeffrey
Published: November 2022

We present a study analyzing the response times of users to questions on Twitter. We investigate estimating these response times using an exponential distribution-based wait time model learned from users’ previous responses. Our analysis considers several different model building approaches, including personalized models for each user, general models built for all users, and time-sensitive models specific to a day of the week or hour of the day. Our evaluation using a real world question-answer dataset shows the effectiveness of our approach.
Where Would You Go this Weekend? Time-Dependent Prediction of User Activity Using Social Network Data
Authors: Bart Evgeniy , Zhang Rui , Hussain Muzammil
Published: November 2022

Predicting user activities and interests has many applications, for example, in contextual recommendations. Although the problem of predicting interests in general has been studied extensively, the problem of predicting when the users are likely to act on those interests has received considerably less attention. Such predictions of timing are extremely important when the application itself is time-sensitive (e.g., travel recommendations are irrelevant too far in advance and after reservations have already been made). Particularly important is the ability to predict likely future activities long in advance (as opposed to short-term prediction of imminent activities). In this paper we describe a comprehensive study that addresses this problem of making long-term time-dependent predictions of user interest. We have conducted this study on a large collection of visits to various venues of interest performed by users of Foursquare. We have built models that, given a user's history, can predict whether or not the user will visit a venue of a particular type on a given day. These models provide useful prediction accuracy of up to 75% for up to several weeks into the future. Our study explores and compares various feature sets and prediction methods. Of particular interest is the fact that venues interact with each other: to predict visits to one type of venue, it helps to use the history of visits to all venue types.
Ensemble Methods for Personality Recognition
Authors: Verhoeven Ben , Daelemans Walter , De Smedt Tom
Published: November 2022

An important bottleneck in the development of accurate and robust personality recognition systems based on supervised machine learning, is the limited availability of training data, and the high cost involved in collecting it. In this paper, we report on a proof of concept of using ensemble learning as a way to alleviate the data acquisition problem. The approach allows the use of information from datasets from different genres, personality classification systems and even different languages in the construction of a classifier, thereby improving its performance. In the exploratory research described here, we indeed observe the expected positive effects.
Mining Facebook Data for Predictive Personality Modeling
Authors: Markovikj Dejan , Gievska Sonja , Kosinski Michal , Stillwell David
Published: November 2022

Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented.
A Salesforce Control Theory Analysis of Enterprise Microblog Posts
Authors: Varshney Kush , Shami N.
Published: November 2022

Social computing and enterprise 2.0 trends are reshaping business. Selling is no different. In this work, we examine the theory of behavior-based and outcome-based salesforce control put forth several decades ago in the context of social business. We collect enterprise microblog posts of salespeople and conduct a preliminary empirical study. Our preliminary results indicate that microblog posts written by sellers not on commission are greater in number and more cooperative on average, confirming the propositions of salesforce control theory. However, a different view of the empirical results indicates that there may not be a difference between commission and noncommission sellers in the content of their posts, warranting further study.
Visualizing Community Resilience Metrics from Twitter Data
Authors: Patton Robert , Steed Chad , Stahl Chris
Published: November 2022

The recent explosive growth of smart phones and social media creates a unique opportunity to view events from various unique perspectives. Unfortunately, this relatively new form of communication lacks the structural integrity, accuracy, and reduced noise of other forms of communication. Nevertheless, social media increasingly plays a vita role in the observation of societal actions before, during, and after significant events. In October 2012, Hurricane Sandy making landfall on the northeastern coasts of the United States demonstrated this role. This work provides a preliminary view into how social media could be used to monitor and gauge community resilience to such natural disasters. We observe, evaluate, and visualize how Twitter data evolves over time before, during, and after a natural disaster such as Hurricane Sandy and what opportunities there may be to leverage social media for situational awareness and emergency response.
Preface
Authors: Archambault Daniel , Kandogan Eser , Harrigan Martin
Published: November 2022

Social media study and analysis brings researchers from many fields into a single setting. Even though the tasks of these researchers are varied, data visualization and analytics plays an important role. For industry and academics alike, visualization of social media data helps with hypothesis formation and supports the explanation of phenomena. This second workshop on social media visualization is a venue for the presentation of research and applications of visualization to social media data. Its goal is to bring together researchers and industry practitioners interested in visual and interactive techniques for social media analysis, particularly in social sciences and humanities as well as in industry.
Observation of Human Dynamics in Cote d’Ivoire Through D4D Call Detail Records
Authors: Wakita Ken , Kawasaki Ryo , Takami Masanori
Published: November 2022

The article outlines how we have identified the cities, city population, strength of social ties among cities, urban mobility in the largest city of Abidjan, and locations of residential and work areas. To deal with otherwise inadequate information contained in the D4D call detail records we introduced some daring assumptions and invented approximation techniques. Although further investigation and assessment are required, these techniques let the dataset illustrate the societies found in the nation.
Aggregating Community Information to Explore Social Connections
Authors: Han Kyungsik , Shih Patrick , Carroll John
Published: November 2022

We introduce our ongoing work in community networks and present a system that aggregates geographical community news and events information from local news and social media. Our goal is to present community information according to its content and to make it more visible to local residents, thereby increasing community awareness. We also present a data analysis of how local residents are connected by analyzing the usage of their tweets and discuss future directions for our system.
Organizers
Authors: Daly Elizabeth
Published: November 2022

List of organizers of the AAAI ICWSM Workshop on When the City Meets the Citizen 2.
Traveling Path Recommendation Using Temporal Transit Patterns
Authors: Hsieh Hsun-Ping , Li Cheng-Te
Published: November 2022

In this work, we aim to collectively recommend traveling paths by leveraging the check-in data through mining the moving behaviors of users. We call such traveling paths as Temporal Transit Patterns (TTP), which capture the representative traveling behaviors over consecutive locations, from the big check-in data. To achieve such goal, we propose a novel Temporal Transit Pattern Mining method (TTPM-method), which devises an unsupervised mechanism that automatically summarizes the representative travel patterns us guarantee better time efficiency lower and memory usage. Based on the mined TTP, a novel system, TTP-Rec, is then developed. TTP-Rec not only allows users to specify starting/end locations, but also provides the flexibility of the time constraint requirement (i.e., the expected duration of the trip). Considering a sequence of check-in points as a traveling path, we mine the frequent sequences with a ranking mechanism to achieve the goal. Our TTP-Rec targets at travelers who are unfamiliar to the objective area/city and have time limitation in the trip.
Using Co-Following for Personalized Out-of-Context Twitter Friend Recommendation
Authors: Weber Ingmar , Garimella Venkata Rama Kiran
Published: November 2022

We present two demos that give personalized `"out-of-context" recommendations of Twitter users to follow. By out-of-context we mean that a user wants to receive recommendation on, say, musicians to follow even though the user's tweets' contents and social links have no connection to the "context" of music. In this setting, where a user has never expressed interest in the context of music, many existing methods fail. Our approach exploits co-following information and hidden correlations where, say, a user's political preference might actually provide clues about their likely music preference. We implement this framework in two very distinct settings: one for recommending musicians and one for recommending political parties in Tunisia. Our framework is simple and similar to Amazon's "users who bought X also bought Y" and can be used not only for explainable out-of-context recommendations but also for social studies on, say, which music is "closest" to users of a particular political affiliation. It also helps to introduce and to "link" a user to an unknown domain, say, politics in Tunisia.
Conference Committee
Authors: Adar Eytan , Resnick Paul
Published: November 2022

List of conference committee members of the Eighth International AAAI Conference on Weblogs and Social Media.
User-Created Groups in Health Forums: What Makes Them Special?
Authors: Vydiswaran V. G. , Liu Yang , Zheng Kai , Hanauer David , Mei Qiaozhu
Published: November 2022

Patients and caregivers often seek help and support through online forums for their ailments. In many health forums, users can not only participate in existing, site-defined user groups but also initiate their own groups. To better understand the mechanism and usefulness of such user-created groups, we comparatively studied their characteristics against those of site-defined groups, based on an empirical dataset of over three years of user activities in health forums collected from MedHelp.org. In the study, we first derived a categorization of user-created groups according to the underlying reasons leading to their creation, and then investigated if there are significant differences between user-created and site-defined groups with respect to group memberships, activity levels, social network metrics, and interaction patterns. The results show that users initiate more homophily driven communities and social interactions, and that the members of user-created groups are more vocal and more socially active than those who have only participated in site-defined groups. The findings suggest that allowing users to create their own groups could improve user engagement in health forums, foster rapport among users, and ultimately lead to a cohesive social environment that supports more effective information sharing and community building.
Time-Aware Latent Concept Expansion for Microblog Search
Authors: Miyanishi Taiki , Seki Kazuhiro , Uehara Kuniaki
Published: November 2022

Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog search.In contrast to such word-based query expansion methods, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms and phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time.Moreover, the proposed model produces important concepts that are frequently used within a particular time periodassociated with a given topic, which better discriminate between relevant and non-relevant microblog documents than words.Our experiments using a corpus of microblog data (Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially for highly relevant documents.
When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter
Authors: Lietz Haiko , Wagner Claudia , Bleier Arnim , Strohmaier Markus
Published: November 2022

Assessing political conversations in social media requires a deeper understanding of the underlying practices and styles that drive these conversations. In this paper, we present a computational approach for assessing online conversational practices of political parties. Following a deductive approach, we devise a number of quantitative measures from a discussion of theoretical constructs in sociological theory. The resulting measures make different – mostly qualitative – aspects of online conversational practices amenable to computation. We evaluate our computational approach by applying it in a case study. In particular, we study online conversational practices of German politicians on Twitter during the German federal election 2013. We find that political parties share some interesting patterns of behavior, but also exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i) how complex cultural phenomena such as online conversational practices are amenable to quantification and (ii) the way social media such as Twitter are utilized by political parties.
Working with Friends: Unveiling Working Affinity Features from Facebook Data
Authors: Castilho Douglas , Melo Pedro , Quercia Daniele , Benevenuto Fabrício
Published: November 2022

College students often have to team up for classprojects, and they select each other based not only onpast performance (e.g., grades) but also on whetherthey get along (e.g., whether they trust each other).There has not been any study on the relationshipbetween team formation for class projects and socialmedia. To fix that, we ask a group of university studentsto tell us with whom they wish to work, gather theironline Facebook data, and test the predictors of teamformation. We find that self-organized selection ofteam members does not strongly depend on pastgrades but rather on Facebook-derived proxies fortie strength, popularity, and homophily. These resultshave important theoretical implications for the teamformation literature and practical implications foronline educational platforms.