bluuebunny/crossref_metadata_2025 huggingface dataset:
The Limited Usefulness of Social Media and Digital Trace Data for Urban Social Research
Authors: Goodspeed Robert
Published: November 2022

Drawing on the literature on social science methodology, this paper argues the potential research contributions from studies using social media data are limited. These data sources are conceptualized as unobtrusive measures, a longstanding category of information for social research. Although useful in some respects, digital unobtrusive measures are limited by their content poverty, focus on espoused theory, and positivist assumptions about social reality. To conclude, the paper describes the limitations of all forms of social research, and the need for mixed methods and action research.
Connect: Citizens, City, Environment — Sensory Research for App Development
Authors: Typhina Eli
Published: November 2022

The city scape offers a unique place to use mobile media to foster pro-environmental behavior through connecting citizens, to city, to their urban environment. The studies presented here are the start to a series of research studies that will guide the development of a mobile application aiming to foster dialog and action through the presentation of technical data, lived experiences, and community connections within urban nature. Social network theory is used in the presented studies to conceptualize the connections and influences among people and nature. The first study identifies the components of four mobile applications assisting in connecting and influencing citizen behavior toward their environment and each other. The second study identifies citizen’s lived, sensory experiences in urban nature and ways a mobile app could support the sharing of these experiences and spur pro-environmental action. As of this printing, these studies are still in progress. The author will present recommendations for app development based on duel interpretations of the studies at the When the City Meets the Citizen workshop.
Modeling Bursty Temporal Pattern of Rumors
Authors: Kwon Sejeong , Cha Meeyoung
Published: November 2022

Online social networks enable fast and wide diffusion of rumors, which could be potential threats to the society. Understanding the rumor spreading mechanism is therefore essential. This demo presents an agent-pattern based model that can explain the spiky temporal diffusion pattern of rumors. The demo platform provides an interface allowing users to check a wide set of parameters contribute to the characteristic temporal pattern of rumors.
Sponsors
Authors: Adar Eytan , Resnick Paul
Published: November 2022

List of sponsors of the Eighth International AAAI Conference on Weblogs and Social Media.
Understanding Loneliness in Social Awareness Streams: Expressions and Responses
Authors: Kivran-Swaine Funda , Ting Jeremy , Brubaker Jed , Teodoro Rannie , Naaman Mor
Published: November 2022

We studied the experience of loneliness as communicated by thousands of people on Twitter. Using a data set of public Twitter posts containing explicit expressions of loneliness, we qualitatively developed a categorization scheme for these expressions, showing how the context of loneliness expressed on Twitter relates to existing theories about loneliness. A quantitative analysis of the data exposed categories and patterns in communication practices around loneliness. For example, users expressing more severe, enduring loneliness are more likely to be female, and less likely to include requests for social interaction in their tweets. Further, we studied the responses to expressions of loneliness in Twitter’s social settings. Deriving from the same dataset, we examined factors that correlate with the existence and type of response, showing, for example, that men were more likely to receive responses to lonely tweets, and expressions of enduring loneliness are critically less likely to receive responses.
The Tweets They Are a-Changin’: Evolution of Twitter Users and Behavior
Authors: Liu Yabing , Kliman-Silver Chloe , Mislove Alan
Published: November 2022

The microblogging site Twitter is now one of the most popular Web destinations. Due to the relative ease of data access, there has been significant research based on Twitter data, ranging from measuring the spread of ideas through society to predicting the behavior of real-world phenomena such as the stock market. Unfortunately, relatively little work has studied the changes in the Twitter ecosystem itself; most research that uses Twitter data is typically based on a small time-window of data, generally ranging from a few weeks to a few months.  Twitter is known to have evolved significantly since its founding, and it remains unclear whether prior results still hold, and whether the (often implicit) assumptions of proposed systems are still valid. In this paper, we take a first step towards answering these question by focusing on the evolution of Twitter's users and their behavior.  Using a set of over 37 billion tweets spanning over seven years, we quantify how the users, their behavior, and the site as a whole have evolved.  We observe and quantify a number of trends including the spread of Twitter across the globe, the rise of spam and malicious behavior, the rapid adoption of tweeting conventions, and the shift from desktop to mobile usage.  Our results can be used to interpret and calibrate previous Twitter work, as well as to make future projections of the site as a whole.
Counting on Friends: Cues to Perceived Trustworthiness in Facebook Profiles
Authors: Toma Catalina
Published: November 2022

Personal profiles on social network sites have become an important tool for social evaluation – that is, assessing profile owners’ characteristics (e.g., personality traits, narcissism, physical and social attractiveness). The present study is one of the first to examine how users’ trustworthiness is evaluated by naïve observers based on information contained in Facebook profiles, or profile cues. Drawing on uncertainty reduction theory, warranting theory, and hardwired perceptions of facial displays, we propose a framework that identifies cues associated with trustworthiness in Facebook profiles. Results show that six profile cues (number of friends, number of tagged photographs, number of “about me” categories filled out, number of comments and “likes” received from friends, and smiling profile photographs) explained about a third of the variance in Facebook users’ perceived trustworthiness. Number of friends had a curvilinear, U-shaped effect on trustworthiness perceptions, with an inflection point at about 500 friends. That is, trustworthiness decreased as number of friends increased up to 500, and then trustworthiness increased as number of friends increased beyond 500. Theoretical and design implications of the findings are discussed.
Write Like I Write: Herding in the Language of Online Reviews
Authors: Michael Loizos , Otterbacher Jahna
Published: November 2022

Our behaviors often converge with those of others, and language within social media is no exception. We consider reviews of tourist attractions at TripAdvisor (TA), the world's largest resource for travel information. Unlike social networking sites, TA review forums do not facilitate direct interaction between participants. Nonetheless, theory suggests that language is guided by writers' conception of their audience, and that their style can shift in response. We implement a model of herding as a local transmission process, exploring the hypothesis that a reviewer is influenced by how preceding reviews manifest a given stylistic feature (e.g., pronouns, paralinguistic devices). We find that reviewers are more likely to use unusual features when such characteristics appear in their local context. The extent to which reviewers are influenced by context is correlated to attributes shared in their profiles, as well as their sentiment toward the attraction reviewed. Our results suggest that language can be influenced by others, even in an asynchronous environment with little to no interpersonal interaction. In other words, our behaviors may be susceptible to manipulation in social media; it may not always be the case that we write like ourselves.
Predicting User Replying Behavior on a Large Online Dating Site
Authors: Xia Peng , Jiang Hua , Wang Xiaodong , Chen Cindy , Liu Benyuan
Published: November 2022

Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user's preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites.
The Call of the Crowd: Event Participation in Location-Based Social Services
Authors: Georgiev Petko , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems.This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are attending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users' event preferences lead us to further hypothesize that a combination of factors better models users' event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.
On Modeling Brand Preferences in Item Adoptions
Authors: Luu Duc , Lim Ee-Peng , Chua Freddy
Published: November 2022

In marketing and advertising, developing and managingbrands value represent the core activities performedby companies. Successful brands attract buyers andadopters, which in turn increase the companies’ value.Given a set of user-item adoption data, can we inferbrand effects from users adopting items? To answerthis question, we develop the Brand Item Topic Model(BITM) that incorporates users’ brand preferences inthe process of item adoption by the users. We evaluateour model using synthetic and two real world datasetsagainst baseline models which do not consider brand effects.The results show that BITM can determine userswho demonstrate brand preferences and predict itemadoptions more accurately.
Memory-Efficient Fast Shortest Path Estimation in Large Social Networks
Authors: Floreskul Volodymyr , Tretyakov Konstantin , Dumas Marlon
Published: November 2022

As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmark-based methods have been actively studied in recent years. Landmark-based estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmark-based distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent non-pruned landmark-based methods, while requiring less memory or disk space.
Adaptive Method for Following Dynamic Topics on Twitter
Authors: Magdy Walid , Elsayed Tamer
Published: November 2022

Many research social studies of public response on social media require following (i.e., tracking) topics on Twitter for long periods of time. The current approaches rely on streaming tweets based on some hashtags or keywords, or following some Twitter accounts. Such approaches lead to limited coverage of on-topic tweets. In this paper, we introduce a novel technique for following such topics in a more effective way. A topic is defined as a set of well-prepared queries that cover the static side of the topic. We propose an automatic approach that adapts to emerging aspects of a tracked broad topic over time. We tested our tracking approach on three broad dynamic topics that are hot in different categories: Egyptian politics, Syrian conflict, and international sports. We measured the effectiveness of our approach over four full days spanning a period of four months to ensure consistency in effectiveness. Experimental results showed that, on average, our approach achieved over 100% increase in recall relative to the baseline Boolean approach, while maintaining an acceptable precision of 83%.
Network Weirdness: Exploring the Origins of Network Paradoxes
Authors: Kooti Farshad , Hodas Nathan , Lerman Kristina
Published: November 2022

Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user’s friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.
Keep Your Friends Close and Your Facebook Friends Closer: A Multiplex Network Approach to the Analysis of Offline and Online Social Ties
Authors: Hristova Desislava , Musolesi Mirco , Mascolo Cecilia
Published: November 2022

Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to associate themselves with like-minded individuals, forming homogeneous social circles both online and offline. In this work, we apply a new model that allows us to distinguish between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health, residential sector and year in college, within the online and offline social network of 74 college students. We present a multiplex network approach to social tie strength, here applied to mobile communication data — calls, text messages, and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify 75% of close friendships, 90% of weaker ties, and 90% of Facebook friendships as compared to reported ground truth. We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social circles with respect to political and health aspects, we observe strong homophily driven by music, residential sector and year in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous content can be found in the online rather than the offline social circles of students, most notably in political and music aspects.
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
Authors: Althoff Tim , Danescu-Niculescu-Mizil Cristian , Jurafsky Dan
Published: November 2022

Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More speciﬁcally, we ﬁnd that clearly communicating need through the narrative is essential and that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, signiﬁcantly improving over several baselines. We link these ﬁndings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.
Coding Together at Scale: GitHub as a Collaborative Social Network
Authors: Lima Antonio , Rossi Luca , Musolesi Mirco
Published: November 2022

GitHub is the most popular repository for open source code. It has more than 3.5 million users, as the company declared in April 2013, and more than 10 million repositories, as of December 2013. It has a publicly accessible API and, since March 2012, it also publishes a stream of all the events occurring on public projects. Interactions among GitHub users are of a complex nature and take place in different forms. Developers create and fork repositories, push code, approve code pushed by others, bookmark their favorite projects and follow other developers to keep track of their activities.In this paper we present a characterization of GitHub, as both a social network and a collaborative platform. To the best of our knowledge, this is the first quantitative study about the interactions happening on GitHub. We analyze the logs from the service over 18 months (between March 11, 2012 and September 11, 2013), describing 183.54 million events and we obtain information about 2.19 million users and 5.68 million repositories, both growing linearly in time. We show that the distributions of the number of contributors per project, watchers per project and followers per user show a power-law-like shape. We analyze social ties and repository-mediated collaboration patterns, and we observe a remarkably low level of reciprocity of the social connections. We also measure the activity of each user in terms of authored events and we observe that very active users do not necessarily have a large number of followers. Finally, we provide a geographic characterization of the centers of activity and we investigate how distance influences collaboration.
Users Joining Multiple Sites: Distributions and Patterns
Authors: Zafarani Reza , Liu Huan
Published: November 2022

The rise of social media has led to an explosion in the number of possible sites users can join. However, this same profusion of social media sites has made it nearly impossible for users to actively engage in all of them simultaneously. Accordingly, users must make choices about which sites to use or to neglect. In this paper, we study users that have joined multiple sites. We study how individuals are distributed across sites, the way they select sites to join, and behavioral patterns they exhibit while selecting sites. Our study demonstrates that while users have a tendency to join the most popular or trendiest sites, this does not fully explain users' selections. We demonstrate that peer pressure also influences the decisions users make about joining emerging sites.
WeDo: End-To-End Computer Supported Collective Action
Authors: Zhang Haoqi , Monroy-Hernández Andrés , Shaw Aaron , Munson Sean , Gerber Elizabeth , Hill Benjamin , Kinnaird Peter , Farnham Shelly , Minder Patrick
Published: November 2022

Many celebrate the Internet’s ability to connect individuals and facilitate collective action toward a common goal. While numerous systems have been designed to support particular aspects of collective action, few systems support participatory, end-to-end collective action in which a crowd or community identifies opportunities, formulates goals, brainstorms ideas and develops plans, mobilizes, and takes action. To explore the possibilities and barriers in supporting such interactions, we introduce WeDo, a system aimed at promoting simple forms of participatory, end-to-end collective action. Pilot deployments of WeDo illustrate that sociotechnical systems can support automated transitions through different phases of end-to-end collective action, but that challenges, such as the elicitation of leadership and the accommodation of existing group norms, remain.
Finding Users we Trust: Scaling up Verified Twitter Users Using their Communication Patterns
Authors: Hentschel Martin , Alonso Omar , Counts Scott , Kandylas Vasileios
Published: November 2022

We present a technique to identify Twitter users we trust to be regular Twitter users and not spam or fake accounts. The technique starts with an initial set of trusted users based on Twitter's verified users and recursively includes other users the trusted users communicate with. Conversations must be initiated by trusted users. We show that this technique produces a set of users that is over 200 times larger than Twitter's verified users. Our evaluation shows that the share of non-spam users within the resulting set of trusted users is more than 92% while the share of non-spam users within all tweeting users is 74%.
Identifying and Analyzing Moral Evaluation Frames in Climate Change Blog Discourse
Authors: Diakopoulos Nicholas , Zhang Amy , Elgesem Dag , Salway Andrew
Published: November 2022

Media frames define distinctive perspectives or ways of communicating about issues and can be manifested through patterns of language use, such as preferences for various key terms and phrases. In this work we develop a novel operationalization of moral evaluation frames and study their use within a corpus of blogs discussing climate change. We compare moral evaluation frames between blogs marked as climate change skeptics and climate change acceptors. We develop a text visualization tool called Lingoscope that allows the user to observe and filter the contextual terms that convey moral framing across large volumes of text, as well as to drill down to specific examples. By focusing on climate-related topics and how they are discussed by climate change skeptics versus climate change acceptors, our approach uncovers and explores how numerous topics are framed in a different moral light in skeptical and acceptor blogs.
On Analyzing Hashtags in Twitter
Authors: Ferragina Paolo , Piccinno Francesco , Santoro Roberto
Published: November 2022

Hashtags, originally introduced in Twitter, are now becoming the most used way to tag short messages in social networks since this facilitates subsequent search, classification and clustering over those messages. However, extracting information from hashtags is difficult because their composition is not constrained by any (linguistic) rule and they usually appear in short and poorly written messages which are difficult to analyze with classic IR techniques. In this paper we address two challenging problems regarding the meaning of hashtags — namely, hashtag relatedness and hashtag classification - and we provide two main contributions. First we build a novel graph upon hashtags and (Wikipedia) entities drawn from the tweets by means of topic annotators (such as TagME); this graph will allow us to model in an efficacious way not only classic co-occurrences but also semantic relatedness among hashtags and entities, or between entities themselves. Based on this graph, we design algorithms that significantly improve state-of-the-art results upon known publicly available datasets. The second contribution is the construction and the public release to the research community of two new datasets: the former is a new dataset for hashtag relatedness, the latter is a dataset for hashtag classification that is up to two orders of magnitude larger than the existing ones. These datasets will be used to show the robustness and efficacy of our approaches, showing improvements in F1 up to two-digits in percentage (absolute).
Degeneracy-Based Real-Time Sub-Event Detection in Twitter Stream
Authors: Meladianos Polykarpos , Nikolentzos Giannis , Rousseau Francois , Stavrakas Yannis , Vazirgiannis Michalis
Published: November 2022

In this paper, we deal with the task of sub-event detection in evolving events using posts collected from the Twitter stream. By representing a sequence of successive tweets in a short time interval as a weighted graph-of-words, we are able to identify the key moments (sub-events) that compose an event using the concept of graph degeneracy. We then select a tweet to best describe each sub-event using a simple yet effective heuristic. We evaluated our approach using humangenerated summaries containing the actual important sub-events within each event and compare it to two baseline approaches using several performance metrics such as DET curves and precision/recall performance. Extensive experiments on recent sporting event streams indicate that our approach outperforms the dominant sub-event detection methods and constructs a humanreadable event summary by aggregating the most representative tweets of each sub-event.
SEEFT: Planned Social Event Discovery and Attribute Extraction by Fusing Twitter and Web Content
Authors: Wang Yu , Fink David , Agichtein Eugene
Published: November 2022

Social events comprise some of the most popular topics in social media. Automatically identifying planned social events and extracting structured information, such as event title, date, and location, would enable more effective index, display and search for social events. However, the informal and noisy nature of language used in social media can degrade the quality of event extraction, resulting in broken titles, incorrect or absent attributes - making the resulting event databases not suitable for realistic applications. Previous work mostly focused on event identification and categorization in Twitter. Yet, event title extraction, arguably one of the most useful and difficult tasks in this domain, has never been investigated. In this paper, we address the task of identifying and extracting structured information (titles, dates, locations) for planned social events, and introduce SEEFT, a social event extraction system, which uses social media content to discover events. To extract the event title and other attributes, SEEFT fuses the original social media content and the content of other Tweets and webpages. Experiments over multiple popular event types and more than a thousand of event instances show that SEEFT significantly outperforms the previous state-of-the-art system in event identification. Moreover, by fusing information from multiple sources, SEEFT is able to extract event titles with high accuracy, providing the foundation for practical applications such as event discovery, search, and recommendation.
Where Is the Soho of Rome? Measures and Algorithms for Finding Similar Neighborhoods in Cities
Authors: Le Falher Géraud , Gionis Aristides , Mathioudakis Michael
Published: November 2022

Data generated on location-aware social media provide rich information about the places (shopping malls, restaurants, cafés, etc) where citizens spend their time. That information can, in turn, be used to describe city neighborhoods in terms of the activity that takes place therein. For example, the data might reveal that citizens visit one neighborhood mainly for shopping, while another for its dining venues. In this paper, we present a methodology to analyze such data, describe neighborhoods in terms of the activity they host, and discover similar neighborhoods across cities. Using millions of Foursquare check-ins from cities in Europe and the US, we conduct an extensive study on features and measures that can be used to quantify similarity of city neighborhoods. We find that the earth-mover's distance outperforms other candidate measures in finding similar neighborhoods. Subsequently, using the earth-mover's distance as our measure of choice, we address the issue of computational efficiency: given a neighborhood in one city, how to efficiently retrieve the $k$ most similar neighborhoods in other cities. We propose a similarity-search strategy that yields significant speed improvement over the brute-force search, with minimal loss in accuracy. We conclude with a case study that compares neighborhoods of Paris to neighborhoods of other cities.
Inferring Sentiment from Web Images with Joint Inference on Visual and Social Cues: A Regulated Matrix Factorization Approach
Authors: Wang Yilin , Hu Yuheng , Kambhampati Subbarao , Li Baoxin
Published: November 2022

In this paper, we study the problem of understanding human sentiments from large scale collection of Internet images based on both image features and contextual social network information (such as friend comments and user description). Despite the great strides in analyzing user sentiment based on text information, the analysis of sentiment behind the image content has largely been ignored. Thus, we extend the significant advances in text-based sentiment prediction tasks to the higher-level challenge of predicting the underlying sentiments behind the images. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment labeling. Thus, we provide a way of using both of them. We leverage the low-level visual features and mid-level attributes of an image, and formulate sentiment prediction problem as a non-negative matrix tri-factorization framework, which has the flexibility to incorporate multiple modalities of information and the capability to learn from heterogeneous features jointly. We develop an optimization algorithm for finding a local-optima solution under the proposed framework. With experiments on two large-scale datasets, we show that the proposed method improves significantly over existing state-of-the-art methods.
Like Partying? Your Face Says It All. Predicting the Ambiance of Places with Profile Pictures
Authors: Redi Miriam , Quercia Daniele , Graham Lindsay , Gosling Samuel
Published: November 2022

To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent them. Descriptions and reviews have been widely explored in the research area of data mining. By contrast, profile pictures have received little attention. Previous work showed that people are able to partly guess a place's ambiance, clientele, and activities not only by observing the place itself but also by observing the profile pictures of its visitors. Here we further that work by determining which visual cues people may have relied upon to make their guesses; showing that a state-of-the-art algorithm could make predictions more accurately than humans at times; and demonstrating that the visual cues people relied upon partly differ from those of the algorithm.
Multilayer Brokerage in Geo-Social Networks
Authors: Hristova Desislava , Panzarasa Pietro , Mascolo Cecilia
Published: November 2022

Open network structures and brokerage positions have long been seen as playing a crucial role in sustaining social capital and competitive advantage. The degree to which individuals intermediate between otherwise disconnected others can differ across online and offline social networks. For example, users may broker online between two others who then exchange offline the information received through social media. Yet network studies of social capital have often neglected the interplay between online and offline interactions, and have concentrated primarily on a single layer. Here, we propose a geo-social multilayer approach to brokerage that casts light on the integrated online and offline foundations of social capital. Drawing on a data set of 37,722 Foursquare users in London, we extend the notion of brokerage by examining users’ positions in an online social network and their offline mobility patterns through checkins. We find that social and geographic brokerage positions are distinct and asymmetric across the social and co-location networks. On the one hand, users may appear to be brokers online when in fact their ability to intermediate would be mitigated if their offline positions were also taken into account. On the other, users who appear to have little brokerage power offline may be active brokers within networks that combine both online and offline interactions.
Impact of Entity Disambiguation Errors on Social Network Properties
Authors: Diesner Jana , Evans Craig , Kim Jinseok
Published: November 2022

Entities in social networks may be subject to consolidation when they are inconsistently indexed, and subject to splitting when multiple entities share the same name. How much do errors or shortfalls in entity disambiguation distort network properties? We show empirically how network analysis results and derived implications can tremendously change depending solely on entity resolution techniques. We present a series of controlled experiments where we vary disambiguation accuracy to study error propagation and the robustness of common network metrics, topologies and key players. Our results suggest that for email data, not conducting deduplication, e.g. when operating on the level of email addressed instead of individuals, can make organizational communication networks appear to be less coherent and integrated as well as bigger than they truly are. For co-publishing networks, improper merging as caused by the commonly used initial based disambiguation techniques can make a scientific sector seem more dense and cohesive than it really is, and individual authors appear to be more productive, collaborative and diversified than they actually are. Disambiguation errors can also lead to the false detection of power law distributions of node degree; suggesting preferential attachment processes that might not apply.
The Many Shades of Anonymity: Characterizing Anonymous Social Media Content
Authors: Correa Denzil , Silva Leandro , Mondal Mainack , Benevenuto Fabrício , Gummadi Krishna
Published: November 2022

Recently, there has been a significant increase in the popularity of anonymous social media sites like Whisper and Secret. Unlike traditional social media sites like Facebook and Twitter, posts on anonymous social media sites are not associated with well defined user identities or profiles. In this study, our goals are two-fold: (i) to understand the nature (sensitivity, types) of content posted on anonymous social media sites and (ii) to investigate the differences between content posted on anonymous and non-anonymous social me- dia sites like Twitter. To this end, we gather and analyze ex- tensive content traces from Whisper (anonymous) and Twitter (non-anonymous) social media sites. We introduce the notion of anonymity sensitivity of a social media post, which captures the extent to which users think the post should be anonymous. We also propose a human annotator based methodology to measure the same for Whisper and Twitter posts. Our analysis reveals that anonymity sensitivity of most whispers (unlike tweets) is not binary. Instead, most whispers exhibit many shades or different levels of anonymity. We also find that the linguistic differences between whispers and tweets are so significant that we could train automated classifiers to distinguish between them with reasonable accuracy. Our findings shed light on human behavior in anonymous media systems that lack the notion of an identity and they have important implications for the future designs of such systems.
Participation Barriers to Youth Civic Engagement in Social Media
Authors: Brandtzæg Petter , Haugstveit Ida Maria , Lüders Marika , Følstad Asbjørn
Published: November 2022

Organizations aiming to foster civic engagement, such as government bodies, news outlets, political parties, and NGOs, struggle to purposefully use social media to engage young people. To respond to this challenge we conducted five group-interviews with 27 youth, 16–26 years, about their experiences of and barriers to civic engagement in social media. Our paper contributes to identifying specific participation barriers that young people experience concerning social media civic engagement, and how organizations should work to overcome these barriers.
Two Studies Investigating Gender Differences in Response to Facebook Status Updates
Authors: Joiner Richard , Dapkeviciute Lina , Johnson Helen , Gavin Jeff , Brosnan Mark
Published: November 2022

We conducted two studies to examine gender differences in in response to Facebook status updates. The first study surveyed 600 undergraduate students (388 females and 207 males), and analysed males’ and females’ responses to Facebook status updates. Females were significantly more likely to post a public reply than males, and female public replies also contained higher levels of emotional support. There were no significant gender differences in private replies to Facebook status updates. Males showed significantly higher levels of emotional support in private messages than in public replies. There was no significant difference in terms of level of emotional support between females’ public replies and private messages. The second study investigated gender differences in response to Facebook status updates from same gender friends compared to opposite gender friends. We surveyed 522 undergraduate students (216 females and 306 males), and analysed males’ and females’ responses to two Facebook status updates: one from a same gender close friend and one from an opposite gender close friend. Females showed higher levels of emotional support than males to a Facebook status update from a same gender friend. In contrast, there were no significant gender differences in response to an opposite gender friend. Males showed higher levels of emotional support in private replies than public replies to same gender friends. There was no difference in level of emotional support between females’ public replies and private messages. The implications of these findings for explanations of gender differences in language use are discussed
Interactive Visual Graph Analytics on the Web
Authors: Ahmed Nesreen , Rossi Ryan
Published: November 2022

We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particular, the multi-level network analysis engine of GraphVis gives rise to a variety of new possibilities for exploring, analyzing, and understanding complex networks interactively in real-time. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning (community/role discovery), as well as tools for dynamic network analysis and visualization, interactive graph generators (including two new block model approaches), and a variety of multi-level network analysis and statistical techniques.
Uncovering User Interaction Dynamics in Online Social Networks
Authors: Yang Zhi , Xue Jilong , Wilson Christo , Zhao Ben , Dai Yafei
Published: November 2022

Measurement studies of online social networks (OSNs)show that all social links are not equal, and the strength of each link is best characterized by the frequency of interactions between the linked users. To date, few studieshave been able to examine detailed interactiondata over time. In this paper, we first analyze the interaction dynamics in a large online social network. We find that users invite new friends to interact at a nearly constant rate, prefer to continue interacting with friends with whom they have a larger number of historical interactions,and most social links drop in interaction frequency over time. Then, we use our insights from the analysis to derive a generative model of social interactionsthat can capture fundamental processes underlinguser interactions.
Moving from Static to Dynamic Modeling of Expertise for Question Routing in CQA Sites
Authors: Yeniterzi Reyyan , Callan Jamie
Published: November 2022

CQA sites are dynamic environments where new users join constantly, or the activity levels or interest of existing users change over time. Classic expertise estimation approaches which were mostly developed for static datasets, cannot effectively model changing expertise and interest levels in these sites. This paper proposes how available temporal information in CQA sites can be used to make these existing approaches more effective for expertise related applications like question routing. Adapting two widely used expert finding approaches for question routing returned consistent and statistically significant improvements over the original approaches, which shows the effectiveness of the proposed temporal modeling.
Twitter Geolocation and Regional Classification via Sparse Coding
Authors: Cha Miriam , Gwon Youngjune , Kung H.
Published: November 2022

We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.
Towards a Community Support System; Social Media Culture, Games and Planning tools
Authors: Shakeri Moozhan , Kingston Richard , Pinto Nuno
Published: November 2022

Social media is revolutionizing social dynamics and the way people take decisions. For town planners it is important not to see social media just as a supporting tool in data gathering and qualitative analysis but to explore the emergent culture of social media and its effect on the planning processes and decision support tools. This study shows how games and gamification can be used in this new culture to stimulate issue formation and participation among communities.
Colocation Cascade: Bringing Social Cascade to the Physical Space
Authors: Alsafty Aysha , Greaves David
Published: November 2022

The need to exploit the physical vicinity of mobile users in social platforms has never been more evident as increasingly more users access social networks from mobile devices. The effect of online social connections has already been shown as instrumental in spreading content and influence via social cascade. In this paper, we examine the phenomenon of social cascade when confined to the user's physical vicinity. We examine the intersection between patterns of both social cascade and physical colcoation. We combine traces of user mobility with models of online social cascade and show that content propagated via social cascade on online social networks are likely to be already cached on the devices of colocated peers.
Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research?
Authors: Weller Katrin , Kinder-Kurlanda Katharina
Published: November 2022

This paper offers insights into social media researchers’ everyday practices in dealing with constraints and challenges in the areas of data access, in terms of data collection and sharing, and data publication. We believe that such insights need to be taken into account when discussing methodology and epistemology of social media data to ensure that strategies employed to achieve validity are appropriate. A qualitative, ethnographic research approach (based on expert interviews, observation and a qualitative questionnaire) is used to capture the practices and discussions of a variety of social media researchers. The paper concludes that due to the current situation in data sharing and publication opportunities, both the actual research data and much of the technical knowledge in social media research remain hidden unless revealed by studying researchers’ everyday practices. Such studies reveal a considerable impact of external constraints on researchers’ attempts to achieve validity and better re-search quality.