bluuebunny/crossref_metadata_2025 huggingface dataset:
Social Innovation Meets Social Media: A Framework Proposal
Authors: Ruiz de Querol Ricard , Kappler Karolin , Kaltenbrunner Andreas , Volkovich Yana , Laniado David
Published: November 2022

In this paper, we describe the motivations for the organization of the SISoM-workshop on Social Innovation and Social Media, to be held in the context of the ICWSM 2011. We argue that social innovation and social media, two trends, that have started independently, are bound to increasingly overlap. Social innovators, as well as the growing number of public administrations fostering social innovation, regard social media as a potentially powerful communication and coordination tool. At the same time, it is argued that they will require functionalities and implement practices which differ from those prevalent in the most popular general purpose social media platforms. A theoretical framework and an initial set of research questions are proposed in order to address those matters, which are partially covered by the papers accepted as contributions to the workshop.
Retweet Reputation: A Bias-Free Evaluation Method for Tweeted Contents
Authors: Fujiki Shino , Yano Hiroya , Fukuda Takashi , Yamana Hayato
Published: November 2022

The widespread of word of mouth using retweets on Twitter has enabled us to estimate trends in the real world. Previous research methods estimate the value of a tweeted content by calculating the number of subscribers who receive the tweet. However, we should consider the numbers of followers for both the tweeter and retweeter(s) as a greater number of followers may result in more retweets, which we call “bias.” In this paper, we propose a bias-free evaluation method for tweeted contents. Experiments show that our method is successful at evaluating tweets without biases.
Community Engagement through Social Media
Authors: Jayakanthan R.
Published: November 2022

Community engagement, i.e., attracting eyeballs of citizens to focus on their civic government and its functioning is a vital aspect in nurturing healthy democracy at the local level, in both developed and developing countries. This paper looks into specific instances such as graffiti reporting through social media (in a developed country), collaborative focus on local community infrastructure issues in the form of digitizing the traditional "Letters to the Editor" mechanism used by citizens (in a developing country) as well as how the crowdsourced digitization of electoral records - wealth declaration documents of candidates for instance, lead to improved awareness of significant data among voters. Apart from these specific instances, the paper also looks into how patterns from these applications can be applied to other areas of social innovation, especially those involving utilizing social media to shape and administer both public policy as well as the awareness of the same by the citizens.
Social Media Revisited. User Generated Content as a Social Innovation for eInclusion
Authors: Kaletka Christoph , Kopp Ralf , Pelka Bastian
Published: November 2022

The paper raises the question whether Web 2.0 can be seen as a technological or a social innovation and describes interdependencies between these two phenomena. The paper argues that the core innovation of Web 2.0 is the communication of “user generated content” as a new social routine, which is largely congruent with the definition of “Web 2.0” in the communication science discourse (which the authors speak for). The authors distinguish between a technological and a social layer of this innovation. The concrete usage by a broad and heterogeneous audience of the different technologies is seen as the main driver for the spread of web 2.0 and the paradigmatic changes it exerts on numerous fields of life. While technology is seen as a “catalyst”, the innovation itself (user generated content) is considered a social one. In a final step, the paper describes the usage of social media in the field of adult learning and the way web 2.0 based learning principles can contribute to social inclusion. In “telecentres”, social media have started to prove their potential as easy-to-use applications for activating and integrating target groups with low ICT skills into the “digital society”.
Information Markets for Social Participation in Public Policy Design and Implementation
Authors: Mentzas Gregoris , Apostolou Dimitris , Bothos Efthimios , Magoutas Babis
Published: November 2022

In this paper we propose a research agenda on the use of information markets as tools to collect, aggregate and analyze citizens’ opinions, expectations and preferences from social media in order to support public policy design and implementation. We argue that markets are institutional settings able to efficiently allocate scarce resources, aggregate and disseminate information into prices and accommodate hedging against various types of risks. We discuss various types of information markets, as well as address the participation of both human and computational agents in such markets.
Social Media for Social Innovation. Towards a Multi-Layered Analytic Framework
Authors: Pavan Elena , Diani Mario , Padovani Claudia
Published: November 2022

This paper proposes a preliminary conceptual approach for studying how social media (i.e., a class of specific Internet-based communication tools) can be strategically employed to promote social innovation (i.e., the actual consolidation of norms guiding democratic and participated policy-making processes). To this aim, we propose to overcome monolithic conceptions of Internet users, uses and politics thus grounding empirical studies on the joint observation of a) the type of users; b) the type of Internet-enabled communication; c) the different facets of political processes; d) the multi-level nature of contemporary politics
Modeling the Detection of Textual Cyberbullying
Authors: Dinakar Karthik , Reichart Roi , Lieberman Henry
Published: November 2022

The scourge of cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Anonymity and the lack of meaningful supervision in the electronic medium are two factors that have exacerbated this social menace. Comments or posts involving sensitive topics that are personal to an individual are more likely to be internalized by a victim, often resulting in tragic outcomes. We decompose the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems. We experiment with a corpus of 4500 YouTube comments, applying a range of binary and multiclass classifiers. We find that binary classifiers for individual labels outperform multiclass classifiers. Our findings show that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.
Asked and Answered: On Qualities and Quantities of Answers in Online Q&A Sites
Authors: Logie John , Weinberg Joseph , Harper F. , Konstan Joseph
Published: November 2022

This paper builds upon several recent research efforts that have explored the nature and qualities of questions asked on these social Q&A sites by offering a focused examination of answers posted to three of the most popular Q&A sites. Specifically, this paper examines sets of answers responding to specific types of questions and explores the degree to which question types are predictive of answer quantity and answer quality. Blending qualitative and quantitative methods, the paper builds upon rich coding of a representative sets of real questions — drawn from Answerbag, (Ask) MetaFilter, and Yahoo! Answers — in order to better understand whether the explicit and implicit theories and predictions drawn from coding of these questions were borne out in the corresponding answer sets found on these sites. Quantitative findings include data underscoring the general overall success of social Q&A sites in producing answers that can satisfy the needs of those who pose questions. Additionally, this paper presents a predictive model that can anticipate the archival value of answers based on the category and qualities of questions asked. Qualitative findings include an analysis of the variation in responses to questions that are primarily seeking objective, grounded information relative to those seeking subjective opinions.
Using Hierarchical Community Structure to Improve Community-Based Message Routing
Authors: Stabeler Matthew , Lee Conrad , Williamson Graham , Cunningham Pádraig
Published: November 2022

Information about community structure can be useful in a variety of mobile web applications. For instance, it has been shown that community-based methods can be more effective than alternatives for routing messages in delay-tolerant networks. In this paper we present initial research that shows that information on hierarchical structures in communities can further improve the effectiveness of message routing. This is interesting because despite much previous work on the topic, there have been few concrete applications which exploit hierarchical community structure.
Brazilian Digital Culture Forum: A New Way of Making Public Policies
Authors: Carvalho Aline , Cabral Adilson
Published: November 2022

This article discusses the Brazilian Digital Culture Forum, and the platform www.culturadigital.br, created by the Ministry of Culture to bring together managers, researchers, designers and artists in the discussion and formulation of cultural policies for the country. The aim is to present it as "a new way of making public policy" insofar as it proposes a dialogue with society about the new challenges of the digital scene, using for this, a collaborative tool, comprising the role of social networks today. However, the creation of a Digital Culture Forum to discuss and manage public policies cannot be separated from a larger process by which the country passed since 2003, when the emergence of networks using new technologies found then in government an opening to the institutionalization and augmentation of ongoing experiments. What quest to present here is the concept of "digital culture" built by the Brazilian government in this period, and also the context that led to the existence of this Forum, as well as identifying some consequences of this “networks policy” in terms of civil society.
Social Mechanics: An Empirically Grounded Science of Social Media
Authors: Lerman Kristina , Galstyan Aram , Ver Steeg Greg , Hogg Tad
Published: November 2022

What will social media sites of tomorrow look like? What behaviors will their interfaces enable? A major challenge for designing new sites that allow a broader range of user actions is the difficulty of extrapolating from experience with current sites without first distinguishing correlations from underlying causal mechanisms. The growing availability of data on user activities provides new opportunities to uncover correlations among user activity, contributed content and the structure of links among users. However, such correlations do not necessarily translate into predictive models. Instead, empirically grounded mechanistic models provide a stronger basis for establishing causal mechanisms and discovering the underlying statistical laws governing social behavior. We describe a statistical physics-based framework for modeling and analyzing social media and illustrate its application to the problems of prediction and inference. We hope these examples will inspire the research community to explore these methods to look for empirically valid causal mechanisms for the observed correlations.
Definition and Multi-Dimensional Comparative Analysis of Ad Hoc Communities in Twitter
Authors: Macskassy Sofus
Published: November 2022

We here present an early-stage prototype tool for defining and analyzing communities in Twitter. The tool takes a set of Twitter users and profiles them based on their tweets. This profiling is based on earlier work, where we map entities mentioned in tweets to Wikipedia entries, which in turn lets us profile a user based on the Wikipedia categories are related to his or her tweets. From here, we can define ad hoc topic-based communities (e.g., all users who discuss Wikipedia topic K). The tool is focused on contrast analysis, where we have baseline behavior or another community to compare against.
Modeling Spread of Disease from Social Interactions
Authors: Sadilek Adam , Kautz Henry , Silenzio Vincent
Published: November 2022

Research in computational epidemiology to date has concentrated on coarse-grained statistical analysis of populations, often synthetic ones. By contrast, this paper focuses on fine-grained modeling of the spread of infectious diseases throughout a large real-world social network. Specifically, we study the roles that social ties and interactions between specific individuals play in the progress of a contagion. We focus on public Twitter data, where we find that for every health-related message there are more than 1,000 unrelated ones. This class imbalance makes classification particularly challenging. Nonetheless, we present a framework that accurately identifies sick individuals from the content of online communication. Evaluation on a sample of 2.5 million geo-tagged Twitter messages shows that social ties to infected, symptomatic people, as well as the intensity of recent co-location, sharply increase one's likelihood of contracting the illness in the near future. To our knowledge, this work is the first to model the interplay of social activity, human mobility, and the spread of infectious disease in a large real-world population. Furthermore, we provide the first quantifiable estimates of the characteristics of disease transmission on a large scale without active user participation---a step towards our ability to model and predict the emergence of global epidemics from day-to-day interpersonal interactions.
People Are Strange When You're a Stranger: Impact and Influence of Bots on Social Networks
Authors: Aiello Luca Maria , Deplano Martina , Schifanella Rossano , Ruffo Giancarlo
Published: November 2022

Bots are, for many Web and social media users, the source of many dangerous attacks or the carrier of unwanted messages, such as spam. Nevertheless, crawlers and software agents are a precious tool for analysts, and they are continuously executed to collect data or to test distributed applications. However, no one knows which is the real potential of a bot whose purpose is to control a community, to manipulate consensus, or to influence user behavior. It is commonly believed that the better an agent simulates human behavior in a social network, the more it can succeed to generate an impact in that community. We contribute to shed light on this issue through an online social experiment aimed to study to what extent a bot with no trust, no profile, and no aims to reproduce human behavior, can become popular and influential in a social media. Results show that a basic social probing activity can be used to acquire social relevance on the network and that the so-acquired popularity can be effectively leveraged to drive users in their social connectivity choices. We also register that our bot activity unveiled hidden social polarization patterns in the community and triggered an emotional response of individuals that brings to light subtle privacy hazards perceived by the user base.
#bias: Measuring the Tweeting Behavior of Propagandists
Authors: Lumezanu Cristian , Feamster Nick , Klein Hans
Published: November 2022

Twitter is an efficient conduit of information for millions of usersaround the world. Its ability to quickly spread information to a largenumber of people makes it an efficient way to shape information and,hence, shape public opinion. We study the tweeting behavior of Twitter propagandists, users who consistently expressthe same opinion or ideology, focusing on two online communities: the2010 Nevada senate race and the 2011 debt-ceiling debate. We identify several extreme tweeting patterns thatcould characterize users who spread propaganda: (1) sending high volumesof tweets over short periods of time, (2) retweeting whilepublishing little original content, (3) quickly retweeting, and (4) colluding with other, seeminglyunrelated, users to send duplicate or near-duplicate messages on thesame topic simultaneously. These four features appear to distinguishtweeters who spread propaganda from other more neutral users and could serve asstarting point for developing behavioral-based propaganda detection techniquesfor Twitter.
Learning the Nature of Information in Social Networks
Authors: Agrawal Rakesh , Potamias Michalis , Terzi Evimaria
Published: November 2022

We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openness by introducing the receptivity of a node. Given a social network and data related to the ordering of adoption of information items by nodes, we develop a maximum-likelihood framework for estimating endogeneity, exogeneity and receptivity parameters. We apply our methodology to synthetic and real data and demonstrate its efficacy as a data-analytic tool.
Have You Heard?: How Gossip Flows Through Workplace Email
Authors: Mitra Tanushree , Gilbert Eric
Published: November 2022

We spend a significant part of our lives chatting about other people. In other words, we all gossip. Although sometimes a contentious topic, various researchers have shown gossip to be fundamental to social life—from small groups to large, formal organizations. In this paper, we present the first study of gossip in a large CMC corpus. Adopting the Enron email dataset and natural language techniques, we arrive at four main findings. First, workplace gossip is common at all levels of the organizational hierarchy, with people most likely to gossip with their peers. Moreover, employees at the lowest level play a major role in circulating it. Second, gossip appears as often in personal exchanges as it does in formal business communication. Third, by deriving a power-law relation, we show that it is more likely for an email to contain gossip if targeted to a smaller audience. Finally, we explore the sentiment associated with gossip email, finding that gossip is in fact quite often negative: 2.7 times more frequent than positive gossip.
SearchBuddies: Bringing Search Engines into the Conversation
Authors: Hecht Brent , Teevan Jaime , Morris Meredith , Liebling Dan
Published: November 2022

Although people receive trusted, personalized recommendations and auxiliary social benefits when they ask questions of their friends, using a search engine is often a more effective way to find an answer. Attempts to integrate social and algorithmic search have thus far focused on bringing social content into algorithmic search results. However, more of the benefits of social search can be preserved by reversing this approach and bringing algorithmic content into natural question-based conversations. To do this successfully, it is necessary to adapt search engine interaction to a social context. In this paper, we present SearchBuddies, a system that responds to Facebook status message questions with algorithmic search results. Via a three-month deployment of the system to 122 social network users, we explore how people responded to search content in a highly social environment. Our experience deploying SearchBuddies shows that a socially embedded search engine can successfully provide users with unique and highly relevant information in a social context and can be integrated into conversations around an information need. The deployment also illuminates specific challenges of embedding a search engine in a social environment and provides guidance toward solutions.
Don't Disturb My Circles! Boundary Preservation Is at the Center of Location-Sharing Concerns
Authors: Page Xinru , Kobsa Alfred , Knijnenburg Bart
Published: November 2022

Past research on location-sharing technologies and social media has uncovered many types of privacy concerns such as informational privacy, impression management and interactional privacy. We interviewed 21 users and nonusers of location-sharing technology and found that many of these privacy concerns are actually just symptoms of a higher-level motivation: the desire to preserve one’s existing offline relationship boundaries. We confirmed and generalized this finding through a nation-wide survey (N=1532) and path analysis. These results imply that designers of location-sharing systems should focus on preserving users’ relationship boundaries to address this cause of privacy concerns.
A Sentiment-Aware Approach to Community Formation in Social Media
Authors: Nguyen Thin , Phung Dinh , Adams Brett , Venkatesh Svetha
Published: November 2022

Participating in a community exemplifies the aspect of sharing, networking and interacting in a social media system. There has been extensive work on characterising on-line communities by their contents and tags using topic modelling tools. However, the role of sentiment and mood has not been studied. Arguably, mood is an integral feature of a text, and becomes more significant in the context of social media: two communities might discuss precisely the same topics, yet within an entirely different atmosphere. Such sentiment-related distinctions are important for many kinds of analysis and applications, such as community recommendation. We present a novel approach to identification of latent hyper-groups in social communities based on users’ sentiment. The results show that a sentiment-based approach can yield useful insights into community formation and meta-communities, having potential applications in, for example, mental health—by targeting support or surveillance to communities with negative mood—or in marketing—by targeting customer communities having the same sentiment on similar topics.
Emotional Divergence Influences Information Spreading in Twitter
Authors: Pfitzner Rene , Garas Antonios , Schweitzer Frank
Published: November 2022

We analyze data about the micro-blogging site Twitter using sentiment extraction techniques. From an information perspective, Twitter users are involved mostly in two processes: information creation and subsequent distribution (tweeting), and pure information distribution (retweeting), with pronounced preference to the first. However a rather substantial fraction of tweets are retweeted. Here, we address the role of the sentiment expressed in tweets for their potential aftermath. We find that although the overall sentiment (polarity) does not influence the probability of a tweet to be retweeted, a new measure called "emotional divergence" does have an impact. In general, tweets with high emotional diversity have a better chance of being retweeted, hence influencing the distribution of information.
Filtering Noisy Web Data by Identifying and Leveraging Users' Contributions
Authors: Stoica Alina
Published: November 2022

In this paper we present several methods for collecting Web textual contents and filtering noisy data. We show that knowing which user publishes which contents can contribute to detecting noise. We begin by collecting data from two forums and from Twitter. For the forums, we extract the meaningful information from each discussion (texts of question and answers, IDs of users, date). For the Twitter dataset, we first detect tweets with very similar texts, which helps avoiding redundancy in further analysis. Also, this leads us to clusters of tweets that can be used in the same way as the forum discussions: they can be modeled by bipartite graphs. The analysis of nodes of the resulting graphs shows that network structure and content type (noisy or relevant) are not independent, so network studying can help in filtering noise.
A Study of "Churn" in Tweets and Real-Time Search Queries
Authors: Lin Jimmy , Mishne Gilad
Published: November 2022

The real-time nature of Twitter means that term distributions in tweets and in search queries change rapidly: the most frequent terms in one hour may look very different from those in the next. Informally, we call this phenomenon "churn". Our interest in analyzing churn stems from the perspective of real-time search. How do we "correctly" compute term statistics, considering that the underlying distributions change rapidly? In this paper, we present an analysis of tweet and query churn on Twitter, as a first step to answering this question. Analyses reveal interesting insights on the temporal dynamics of term distributions on Twitter and hold implications for the design of search systems.
More or Less: Amount of Personal Information Displayed in Social Network Site Profiles and Its Impact on Viewers’ Intentions to Socialize with the Profile Owner
Authors: Baruh Lemi , Chisik Yoram , Bisson Christophe , Senova Basak
Published: November 2022

This paper presents the results of an experiment that employed a 2 (low vs. high information) by 2 (male vs. female profile) design to investigate the relationship between amount of information displayed in a Social Network Site (SNS) profile and profile viewers’ intentions to engage in further social interactions (communicate online, add to SNS profile, and meet face-to-face) with the profile owner. The results indicate that more information increases the likelihood of relationship initiation for male profiles but decreases it for female profiles. Also, viewers are inclined to initiate an interaction when less information is presented in an SNS profile of a person from the opposite sex; but require more information from their own sex.
Inferring Gender from the Content of Tweets: A Region Specific Example
Authors: Fink Clay , Kopecky Jonathon , Morawski Maksym
Published: November 2022

There is growing interest in using social networking sites such as Twitter to gather real-time data on the reactions and opinions of a region's population, including locations in the developing world where social media has played an important role in recent events, such as the 2011 Arab Spring. However, many interesting and important opinions and reactions may differ significantly within a given region depending on the demographics of the subpopulation, including such categories as gender and ethnicity. Unfortunately, the demographic characteristics of social media users are often unknown because such categories are not always captured in user metadata. Twitter, for example, does not capture a user’s gender in their profile, and inferring gender from first names is difficult since Twitter users are not required to give their real names. There is thus a need for automated methods that can infer such hidden attributes of users from other data sources. In this paper we describe a method to infer the gender of Twitter users from only the content of their tweets. Looking at Twitter users from the West African nation of Nigeria, we applied supervised machine learning using features derived from the content of user tweets to train a classifier. Using unigram features alone, we obtained an accuracy of 80% for predicting gender, suggesting that content alone can be a good predictor of gender. An analysis of the highest weighted features shows some interesting distinctions between men and women both topically and emotionally. We argue that approaches such as the one described here can give us a clearer picture of who is utilizing social media when certain user attributes are unreliable or not available.
Tweetin' in the Rain: Exploring Societal-Scale Effects of Weather on Mood
Authors: Hannak Aniko , Anderson Eric , Feldman Barrett Lisa , Lehmann Sune , Mislove Alan , Riedewald Mirek
Published: November 2022

There has been significant recent interest in using the aggregate sentiment from social media sites to understand and predict real-world phenomena. However, the data from social media sites also offers a unique and — so far — unexplored opportunity to study the impact of external factors on aggregate sentiment, at the scale of a society. Using a Twitter-specific sentiment extraction methodology, we the explore patterns of sentiment present in a corpus of over 1.5 billion tweets. We focus primarily on the effect of the weather and time on aggregate sentiment, evaluating how clearly the well-known individual patterns translate into population-wide patterns. Using machine learning techniques on the Twitter corpus correlated with the weather at the time and location of the tweets, we find that aggregate sentiment follows distinct climate, temporal, and seasonal patterns.
Network Sampling Designs for Relational Classification
Authors: Ahmed Nesreen , Neville Jennifer , Kompella Ramana
Published: November 2022

Relational classification has been extensively studied recently due to its applications in social, biological, technological, and information networks. Much of the work in relational learning has focused on analyzing input data that comprise a single network. Although machine learning researchers have considered the issue of how to sample training and test sets from the input network (for evaluation), the mechanisms which are used to construct the input networks have largely been ignored. In most cases, the input network has itself been sampled from a larger target network (e.g., Facebook) and often the researcher is unaware of how the input network was constructed or what impact that may have on evaluation of the relational models. Since the goal in evaluating relational classification algorithms is to accurately assess their performance on the larger target network, it is critical to understand what impact the initial sampling method may have on our estimates of classification accuracy.In this paper, we present different sampling methods and systematically study their impact on evaluation of relational classification. Our results indicate that the choice of sampling method can impact classification performance, and thus consequently affects the accuracy of evaluation.
Using Group Membership Markers for Group Identification
Authors: Gawron Jean , Gupta Dipak , Stephens Kellen , Tsou Ming-Hsiang , Spitzberg Brian , An Li
Published: November 2022

We describe a system for automatically ranking documents by degree of militancy, designed as a tool both for finding militant websites and prioritizing the data found. We compare three ranking systems, one employing a small hand-selected vocabulary based on group membership markers used by insiders to identify members and member properties (us) and outsiders and threats (them), one with a much larger vocabulary, and another with a small vocabulary chosen by Mutual Information. We use the same vocabularies to build classifiers. The ranker that achieves the best correlations with human judgments uses the small us-them vocabulary. We confirm and extend recent results in sentiment analysis (paltoglou 2010), showing that a feature-weighting scheme taken from classical IR (TFIDF) produces the best ranking system; we also find, surprisingly, that adjusting these weights with SVM training, while producing a better classifier, produces a worse ranker. Increasing vocabulary size similarly improves classification (while worsening ranking).
Narcotweets: Social Media in Wartime
Authors: Monroy-Hernández Andrés , Kiciman Emre , Boyd Danah , Counts Scott
Published: November 2022

This paper describes how people living in armed-conflict environments use social media as a participatory news platform in lieu of damaged state and media apparatuses. We investigate this by analyzing the microblogging practices of Mexican citizens whose everyday life is affected by the Drug War. We provide a descriptive analysis of the phenomenon, combining content and quantitative Twitter data analyses. We focus on three interrelated phenomena: general participation patterns of ordinary citizens, the emergence and role of information curators, and the tension between governmental regulation and drug cartel intimidation. This study reveals the complex tensions among citizens, media actors, and the government in light of large-scale organized crime.
Social Media Is NOT that Bad! The Lexical Quality of Social Media
Authors: Rello Luz , Baeza-Yates Ricardo
Published: November 2022

There is a strong correlation between spelling errors and web text content quality. Using our lexical quality measure,  based in a small corpus of spelling errors, we present an estimation of the lexical quality of the main Social Media sites. This paper presents an updated and complete analysis of the lexical quality of Social Media written in English and Spanish, including how lexical quality changes in time.
Unsupervised Real-Time Company Name Disambiguation in Twitter
Authors: Delgado Muñoz Agustín , Martínez Unanue Raquel , Pérez García-Plaza Alberto , Fresno Víctor
Published: November 2022

This paper presents a new approach to disambiguate company names in the Twitter social network. We have focused on making lighter the processing of comparing company profiles with tweets in order to obtain a competitive real-time system. With this aim, we only use the home page of each company as information source to create a unique profile. On the other hand, we compute the similarity of a tweet in connection to a profile by comparing the content of the tweet with the profile. Both steps do not use any other external information source and all the process is developed in an unsupervised way. We have tested our application with the test WePS-3 CLEF ORM corpus obtaining encouraging results.
Aggregating Social Media for Enhancing Conference Experience
Authors: Khrouf Houda , Atemezing Ghislain , Rizzo Giuseppe , Troncy Raphaël , Steiner Thomas
Published: November 2022

A scientific conference is a type of event where attendees have a tremendous activity on social media platforms. Participants tweet or post longer status messages, engage in discussions with comments, share slides and other media captured during the conference. This information can be used to generate informative reports of what is happening, where (which specific room) and when (which time slot), and who are the active participants. However, this information is locked in different data silos and platforms forcing the user to monitor many different channels at the same time to fully benefit from the event. In this paper, we propose a framework named Confomaton that aggregates in real-time social media shared by conference attendees and aligns it with event descriptions. Developed with Semantic Web technologies, this framework enables to relive past events and to follow live conferences. A demonstrator is available at http://eventmedia.eurecom.fr/confomaton.
Cultural Analytics of Large Datasets from Flickr
Authors: Ushizima Daniela , Manovich Lev , Margolis Todd , Douglas Jeremy
Published: November 2022

Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heterogenous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.
Visualizing Instagram: Tracing Cultural Visual Rhythms
Authors: Hochman Nadav , Schwartz Raz
Published: November 2022

Picture-taking has never been easier. We now use our phones to snap photos and instantly share them with friends, family and strangers all around the world. Consequently, we seek ways to visualize, analyze and discover concealed socio-cultural characteristics and trends in this ever-growing flow of visual information. How do we then trace global and local patterns from the analysis of visual planetary–scale data? What types of insights can we draw from the study of these massive visual materials? In this study we use Cultural Analytics visualization techniques for the study of approximately 550,000 images taken by users of the location-based social photo sharing application Instagram. By analyzing images from New York City and Tokyo, we offer a comparative visualization research that indicates differences in local color usage, cultural production rate, and varied hue’s intensities— all form a unique, local, ‘Visual Rhythm’: a framework for the analysis of location-based visual information flows.
TripEneer: User-Based Travel Plan Recommendation Application
Authors: Yerva Surender , Grosan Flavia , Tandrau Alexandru , Aberer Karl
Published: November 2022

Current travel recommendation systems are helpful in addressing a traveler's information needs to certain extent, however, most of them fail to factor in the user in their recommendations. TripEneer proposes travel recommendations to a traveler by keeping the user preferences and constraints as first class citizens. We present an intuitive UI for helping users plan their travel trips quickly and easily. In the current demo we present various global and user-specific ranking models used for recommending travel destinations. Our preliminary evaluation showed that the users found the personalized recommendations, based on the user model, most useful.
Visualization Techniques for the Analysis of Twitter Users’ Behavior
Authors: Rotta Guilherme , Lemos Vinicius , Lammel Felipe , Manssour Isabel , Silveira Milene , Pase André
Published: November 2022

One of the most popular social networking sites in Brazil is Twitter, which is used by one in five people who have internet at home. Based on that, we investigated how information visualization techniques can improve the analysis of the behavior of Twitter users’. These studies were done through a set of standard visualization techniques implemented in an interactive system entitled DeepTwitter.
TwitterViz: A Robotics System for Remote Data Visualization
Authors: Jones Alexander , Carlson Eric
Published: November 2022

We demonstrate a portable and functional Internet-connected robotics system called TwitterViz, which visualizes real-time Twitter data on a kinetic sculpture. The purpose of our project is to explore how robotics can 'understand' and visualize remote data streams. We have constructed an overall system architecture with custom hardware and software that drives a robotic sculpture in real-time. Our system monitors Twitter data from the public API feed, analyzes the Tweets, and then converts the Tweets to motion on a kinetic robot. Our live demonstration of the TwitterViz robotics system fits onto a desktop, and includes the functioning kinetic robot, mini-ITX server, display for raw Tweets, and 4G connectivity to communicate with the Twitter API.
The Secret Life of Online Moms: Anonymity and Disinhibition on YouBeMom.com
Authors: Schoenebeck Sarita
Published: November 2022

Moms are one of the fastest growing demographics online. While much is known about where they spend their time, little is known about how they spend it. Using a dataset of over 51 million posts and comments from the website YouBeMom.com, this paper explores what kinds of topics moms talk about when they are not constrained by norms and expectations of face-to-face culture. Results show that almost 5% of posts are about dh, or “dear husband,” but these posts tend to express more negative emotion than other posts. The average post is only 124 characters long and family and daily life are common categories of posting. This suggests that YouBeMom is used as a fast-paced social outlet that may not be available to moms in other parts of their lives. This work concludes with a discussion of anonymity and disinhibition and puts forth a new provocation that moms, too, spend time online “for the lulz.”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter
Authors: Nguyen Dong , Gravel Rilana , Trieschnigg Dolf , Meder Theo
Published: November 2022

In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages.
Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media
Authors: Lee Kyumin , Tamilarasan Prithivi , Caverlee James
Published: November 2022

Crowdturfing has recently been identified as a sinister counterpart to the enormous positive opportunities of crowdsourcing. Crowdturfers leverage human-powered crowdsourcing platforms to spread malicious URLs in social media, form "astroturf" campaigns, and manipulate search engines, ultimately degrading the quality of online information and threatening the usefulness of these systems. In this paper we present a framework for "pulling back the curtain" on crowdturfers to reveal their underlying ecosystem. Concretely, we analyze the types of malicious tasks and the properties of requesters and workers in crowdsourcing sites such as Microworkers.com, ShortTask.com and Rapidworkers.com, and link these tasks (and their associated workers) on crowdsourcing sites to social media, by monitoring the activities of social media participants. Based on this linkage, we identify the relationship structure connecting these workers in social media, which can reveal the implicit power structure of crowdturfers identified on crowdsourcing sites. We identify three classes of crowdturfers -- professional workers, casual workers, and middlemen -- and we develop statistical user models to automatically differentiate these workers and regular social media users.
Reviving Dormant Ties in an Online Social Network Experiment
Authors: Lim Ee-Peng , Correa Denzil , Lo David , Finegold Michael , Zhu Feida
Published: November 2022

Social network users connect and interact with one another to fulfil different kinds of social and information needs.When interaction ceases between two users, we say that their tie becomes dormant. While there are different underlying reasons of dormant ties, it is important to find means to revive such ties so as to maintain vibrancy in the relationships. In this work, we thus focus on designing an online experiment to evaluate the effectiveness of personalized social messages to revive dormant ties. The experiment carefully selects users with dormant ties so that no user gets mixed treatments and be affected by the responses of other users undergoing treatment. Our results show that personalized message content plays an important part in reviving dormant ties. Specifically, we find the message containing friend's recent activity information is more effective than that containing inter-friend activity information. We observe that the quality of engagement of at least 50% of the revived ties can effectively be restored to the level before the ties become dormant. We also observe that it is easier to revive dormant ties that involve users from the same country than those involving users from different countries.
Structural Dynamics of Knowledge Networks
Authors: Preusse Julia , Kunegis Jérôme , Thimm Matthias , Staab Steffen , Gottron Thomas
Published: November 2022

We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.
A Data-Driven Analysis to Question Epidemic Models for Citation Cascades on the Blogosphere
Authors: Salah Brahim Abdelhamid , Tabourier Lionel , Le Grand Bénédicte
Published: November 2022

Citation cascades in blog networks are often considered as traces of information spreading on this social medium. In this work, we question this point of view using both a structural and semantic analysis of five months activity of the most representative blogs of the french-speaking community. Statistical measures reveal that our dataset shares many features with those that can be found in the literature, suggesting the existence of an identical underlying process. However, a closer analysis of the post content indicates that the popular epidemic-like descriptions of cascades are misleading in this context. A basic model, taking only into account the behavior of bloggers and their restricted social network, accounts for several important statistical features of the data. These arguments support the idea that citations primary goal may not be information spreading on the blogosphere.
Extracting Diurnal Patterns of Real World Activity from Social Media
Authors: Grinberg Nir , Naaman Mor , Shaw Blake , Lotan Gilad
Published: November 2022

In this study, we develop methods to identify verbal expressions in social media streams that refer to real-world activities. Using aggregate daily patterns of Foursquare checkins, our methods extract similar patterns from Twitter, extending the amount of available content while preserving high relevance. We devise and test several methods to extract such content, using time-series and semantic similarity. Evaluating on key activity categories available from Foursquare (coffee, food, shopping and nightlife), we show that our extraction methods are able to capture equivalent patterns in Twitter. By examining rudimentary categories of activity such as nightlife, food or shopping we peek at the fundamental rhythm of human behavior and observe when it is disrupted. We use data compiled during the abnormal conditions in New York City throughout Hurricane Sandy to examine the outcome of our methods.