bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Weblog Post Filtering Based on User Browsing History
Authors: Farahmand Nejad Ali , Kharazmi Sadegh , Bayati Shahabedin , Abolhassani Hassan , Golmohammadi Koosha
Published: November 2022

Weblogs are one of important Web-based services that establish the foundations of the Web 2.0. These days many companies propose free Weblog hosting and population services, because they realize that Weblogs are evolving to be a more topic based systems, and so they could be good places to gain more revenue. One of effective factors for gaining more revenue is blog popularity. Weblogs have posts that arranged chronologically with most recent first. Some of Weblog has many posts that cause difficulty in finding specific posts by each viewer. On the other hand unsuitable posts order can redouble this problem. This difficulty occasions reduction of Weblog popularity. As our researches on Farsi weblogs shown many viewers close some Weblog windows before completely loaded in their own Web browsers. Based on our research an important reason is the existence of vast amount of Weblog posts. Viewer interests can be beneficial if they utilize for adaptive filtering Weblogs and showing their contents. This paper represents an approach for filtering and reordering Weblog posts based on user’s browsing history Experimental results show that our filtering approach can improve Weblog popularity and can increases Weblog viewers.
Analyzing and Predicting Community Preference of Socially Generated Metadata: A Case Study on Comments in the Digg Community
Authors: Khabiri Elham , Hsu Chiao-Fang , Caverlee James
Published: November 2022

Large-scale socially-generated metadata is one of the key features driving the growth and success of the emerging Social Web. Recently there have been many research efforts to study the quality of this metadata that relies on quality assessments made by human experts external to a Social Web community. We are interested in studying how an online community itself perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web. To this end, we study the community preference for user-contributed comments on the social news aggregator Digg. In our analysis, we study several factors impacting community preference. We propose a learning-based approach for predicting the community's preference rating of unseen comments, which can be used to promote high-quality comments and filter out low-quality comments based on the community's expressed preferences.
Blogging Together: An Examination of Group Blogs
Authors: Hearst Marti , Dumais Susan
Published: November 2022

This paper presents the first examination of the growing phenomenon of multi-author, or group blogs.  Using a large collection of blog posts gathered over an eight month period, we conducted some quantitative assessments.  Multi-author blogs were found to differ significantly from single-and-two author blogs on several quality metrics, including having longer posts on average and being ranked higher according to an in-link metric.  Visualization of patterns of posting revealed that individual authors often dominate the posts in multi-author blogs, and that in some cases tags are shared, but usually not extensively.
Large Scale Book Annotation with Social Tags
Authors: Givon Sharon , Lavrenko Victor
Published: November 2022

We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 tags used in earlier work to sets ranging up to 10,000 tags. We show how a RM based algorithm scales significantly better than a Boosting based algorithm when dealing with large sets of tags.
The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter
Authors: Romero Daniel , Kleinberg Jon
Published: November 2022

It has often been taken as a working assumption that directed links in information networks are frequently formed by "short-cutting'' a two-step path between the source and the destination — a kind of implicit "link copying'' analogous to the process of triadic closure in social networks. Despite the role of this assumption in theoretical models such as preferential attachment, it has received very little direct empirical investigation. Here we develop a formalization and methodology for studying this type of directed closure process, and we provide evidence for its important role in the formation of links on Twitter. We then analyze a sequence of models designed to capture the structural phenomena related to directed closure that we observe in the Twitter data.
Tweeting from the Town Square: Measuring Geographic Local Networks
Authors: Yardi Sarita , Boyd Danah
Published: November 2022

This paper examines tweets about two geographically local events—a shooting and a building collapse—that took place in Wichita, Kansas and Atlanta, Georgia, respectively. Most Internet research has focused on examining ways the Internet can connect people across long distances, yet there are benefits to being connected to others who are nearby. People in close geographic proximity can provide real-time information and eyewitness updates for one another about events of local interest. We first show a relationship between structural properties in the Twitter network and geographic properties in the physical world. We then describe the role of mainstream news in disseminating local information. Last, we present a poll of 164 users’ information seeking practices. We conclude with practical and theoretical implications for sharing information in local communities.
Study of Static Classification of Social Spam Profiles in MySpace
Authors: Irani Danesh , Webb Steve , Pu Calton
Published: November 2022

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r.t. features which may be easily removed by spammers.
Widespread Worry and the Stock Market
Authors: Gilbert Eric , Karahalios Karrie
Published: November 2022

Our emotional state influences our choices. Research on how it happens usually comes from the lab. We know relatively little about how real world emotions affect real world settings, like financial markets. Here, we demonstrate that estimating emotions from weblogs provides novel information about future stock market prices. That is, it provides information not already apparent from market data. Specifically, we estimate anxiety, worry and fear from a dataset of over 20 million posts made on the site LiveJournal. Using a Granger-causal framework, we find that increases in expressions of anxiety, evidenced by computationally-identified linguistic features, predict downward pressure on the S&P 500 index. We also present a confirmation of this result via Monte Carlo simulation. The findings show how the mood of millions in a large online community, even one that primarily discusses daily life, can anticipate changes in a seemingly unrelated system. Beyond this, the results suggest new ways to gauge public opinion and predict its impact.
Who Acquires Friends Through Social Media and Why? “Rich Get Richer” Versus “Seek and Ye Shall Find”
Authors: Tufekci Zeynep
Published: November 2022

There is an ongoing debate, not just among academics but in popular culture, about whether social media can expand people’s social networks, and whether online friends can be “real” friends. The debate refuses to die. This paper addresses this question subjectively, from the point of view of the user, and examines the predictors of acquiring new friends through social media use. This is a multi-method study with quantitative (n=617) and qualitative sections. Some previous studies have found a “rich-get-richer” effect where people who are socially active offline appear to benefit most from online interactions. This paper is based on the idea that whether online friends can be real friends may be subject to a self-fulfilling prophecy: those who do not believe that online friendships can be real friendships are not likely to make such connections. I compare the “Rich Get Richer” and “Seek and Ye Shall Find” models by examining relationships between the amount of offline socializing, amount of online social activity and the belief in online friendships. I also qualitatively examine reasons cited by respondents as to why online sociality is or is not a plausible route to meaningful friendship. The results show strong support for one of the earliest theories of computer-mediated-communication: hyperpersonal interaction. It appears that a sizable portion of the young generation finds online interaction positive because it is perceived to concentrate on the conversation itself, rather than on appearances, and is seen as freer of social judgments. African-Americans are significantly more likely to meet new friends online. There also appear to be underlying personality traits, apart from traditional demographic variables, that divide the population in terms of their attitude towards online sociality.
Responses to Remixing on a Social Media Sharing Website
Authors: Hill Benjamin , Monroy-Hernández Andrés , Olson Kristina
Published: November 2022

In this paper we describe the ways participants of the Scratch online community, primarily young people, engage in remixing of each others' shared animations, games, and interactive projects. In particular, we try to answer the following questions: How do users respond to remixing in a social media environment where remixing is explicitly permitted? What qualities of originators and their projects correspond to a higher likelihood of plagiarism accusations? Is there a connection between plagiarism complaints and similarities between a remix and the work it is based on? Our findings indicate that users have a very wide range of reactions to remixing and that as many users react positively as accuse remixers of plagiarism. We test several hypotheses that might explain the high number of plagiarism accusations related to original project complexity, cumulative remixing, originators' integration into remixing practice, and remixee-remixer project similarity, and find support for the first and last explanations.
ePluribus: Ethnicity on Social Networks
Authors: Chang Jonathan , Rosenn Itamar , Backstrom Lars , Marlow Cameron
Published: November 2022

We propose an approach to determine the ethnic breakdown of a population based solely on people's names and data provided by the U.S. Census Bureau. We demonstrate that our approach is able to predict the ethnicities of individuals as well as the ethnicity of an entire population better than natural alternatives. We apply our technique to the population of U.S. Facebook users and uncover the demographic characteristics of ethnicities and how they relate. We also discover that while Facebook has always been diverse, diversity has increased over time leading to a population that today looks very similar to the overall U.S. population. We also find that different ethnic groups relate to one another in an assortative manner, and that these groups have different profiles across demographics, beliefs, and usage of site features.
The Social Dynamics of Economic Activity in a Virtual World
Authors: Bakshy Eytan , Simmons Matthew , Huffaker David , Cheng Chun-Yuen , Adamic Lada
Published: November 2022

This paper examines social structures underlying economic activity in Second Life (SL), a massively multiplayer virtual world that allows users to create and trade virtual objects and commodities. We find that users conduct many of their transactions both within their social networks and within groups. Using frequency of chat as a proxy of tie strength, we observe that free items are more likely to be exchanged as the strength of the tie increases. Social ties particularly play a significant role in paid transactions for sellers with a moderately sized customer base. We further find that sellers enjoying repeat business are likely to be selling to niche markets, because their customers tend to be contained in a smaller number of groups. But while social structure and interaction can help explain a seller's revenues and repeat business, they provide little information in the forecasting a seller's future performance. Our quantitative analysis is complemented by a novel method of visualizing the transaction activity of a seller, including revenue, customer base growth, and repeat business.
Coping With Noise in a Real-World Weblog Crawler and Retrieval System
Authors: Lanagan James , Ferguson Paul , O'Hare Neil , Smeaton Alan
Published: November 2022

In this paper we examine the effects of noise when creating a real-world weblog corpus for information retrieval. We focus on the DiffPost (Lee et al. 2008) approach to noise removal from blog pages, examining the difficulties encountered when crawling the blogosphere during the creation of a real-world corpus of blog pages. We introduce and evaluate a number of enhancements to the original DiffPost approach in order to increase the robustness of the algorithm. We then extend DiffPost by looking at the anchor-text to text ratio, and discover that the time-interval between crawls is more important to the successful application of noise-removal algorithms within the blog context, than any additional improvements to the removal algorithm itself.
Effective Question Recommendation Based on Multiple Features for Question Answering Communities
Authors: Kabutoya Yutaka , Iwata Tomoharu , Shiohara Hisako , Fujimura Ko
Published: November 2022

We propose a new method of recommending questions to answerers so as to suit the answerers’ knowledge and interests in User-Interactive Question Answering (QA) communities. A question recommender can help answerers select the questions that interest them. This increases the number of answers, which will activate QA communities. An effective question recommender should satisfy the following three requirements: First, its accuracy should be higher than the existing category-based approach; more than 50% of answerers select the questions to answer according a fixed system of categories. Second, it should be able to recommend unanswered questions because more than 2,000 questions are posted every day. Third, it should be able to support even those people who have never answered a question previously, because more than 50% of users in current QA communities have never given any answer. To achieve an effective question recommender, we use question histories as well as the answer histories of each user by combining collaborative filtering schemes and content-base filtering schemes. Experiments on real log data sets of a famous Japanese QA community, Oshiete goo, show that our recommender satisfies the three requirements.
Socio-Legal Analysis of Criminal Sentences: A Preliminary Study
Authors: Giura Giuseppe , Giuffrida Giovanni , Pennisi Carlo , Zarba Calogero
Published: November 2022

This paper discusses a research based on analyzing criminal sentences on criminal trials on organized crime activity in Sicily pronounced from 2000 through 2006. Large criminal sentences related dataset collection activity in Italy is severely constrained for various reasons such as difficulty of data collection at the courthouses, unavailability of data in digital format, and classification criteria used in the public archives. Thus, in general, judicial statistics suffer from lack of reliability and informativeness. The objective of this research is to analyze the text of criminal sentences in a revisable and verifiable way, so that information is extracted on the trial leading to the sentence, the socio-economic environment in which the relevant events occurred, and the differences between the various districts conducting the trials. The purpose is to elaborate a tool of automated analysis of the text of the sentences that is generalizable to other areas of jurisprudence, and, outside of jurisprudence, to other temporal and geographical contexts. The 726 criminal sentences that have been converted into text files have been pronounced at all judicial levels in the four Sicilian districts for mafia-related crimes. This research is relevant because, for the first time in Italy, we aim to empirically describe the juridical response to the phenomenon of organized crime, by using a large and extendable database of criminal sentences that can be analyzed with data mining techniques, rather than deriving general conclusions from a focused small set of sentences.
A Qualitative Examination of Topical Tweet and Retweet Practices
Authors: Nagarajan Meenakshi , Purohit Hemant , Sheth Amit
Published: November 2022

This work contributes to the study of retweet behavior on Twitter surrounding real-world events. We analyze over a million tweets pertaining to three events, present general tweet properties in such topical datasets and qualitatively analyze the properties of the retweet behavior surrounding the most tweeted/viral content pieces. Findings include a clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself; suggesting the need to study content properties in link-based diffusion models.
Modeling Personality Influences on YouTube Usage
Authors: Yeo T. E.
Published: November 2022

Understanding the personality-contextual attributes of target users can help developers design tools that increase the effectiveness of social media sites or identify appropriate lead users to seed products already in development. This paper illustrates that users' personality traits serve as a distinguishable set of characteristics that can be meaningfully related to YouTube usage. A model outlining the structural relations among: personality and motives (predictors), involvement (mediator), and video preferences and user activities on YouTube (dependent constructs) was proposed and successfully tested. The analysis was able to capitalize on the full statistical advantage of structural equation modeling in testing the unattenuated effects of the constructs through the use of fully latent variables. In particular, through the use of the new exploratory structural equation modeling technique, a full set of Big Five personality factors was modeled as latent variables – which was not methodologically viable until recently. Modeling a fuller set of predictors allows researchers to control for the influence of non-salient predictors and to ascertain the unique and common variances among predictors. Each of the Big Five personality factors was found to have a significant influence on at least one aspect of user participation. Extraversion and openness to experience had the most number of significant effects, showing not only proximal effects on motives but direct effects on video preferences and user activities as well. Although the effects of the other personality factors — neuroticism, conscientiousness, and agreeableness — were limited to motives, they were moderately stronger than extraversion and openness to experience.
Decomposing Discussion Forums and Boards Using User Roles
Authors: Chan Jeffrey , Hayes Conor , Daly Elizabeth
Published: November 2022

Discussion forums are a central part of Web 2.0 and Enterprise 2.0 infrastructures. The health and sustainability of forums is dependent on the information exchange behaviour of its contributors, which is expressed through online conversation. The increasing popularity and importance of forums requires a better understanding and characterisation of communication behaviour so that forums can be better managed, new services delivered and opportunities and risks detected. In this paper, we present an empirical analysis of user communication roles in a medium-sized bulletin board and we analyse the composition of several forums in terms of these roles, demonstrating similarities between forums based on underlying user behaviour rather than topic.
A Second Chance to Make a First Impression: Factors Affecting the Longevity of Online Dating Relationships
Authors: Shaw Taylor Lindsay , Fiore Andrew , Mendelsohn G. , Cheshire Coye
Published: November 2022

This research explored the transition of romantic relationships from meeting online to the first face-to-face date. It is inevitable that impressions of a partner will change to some degree, but how much, and with what consequences? One hundred and fifty users of a popular online dating site participated in the study. They recalled a person whom they had met through the site, reporting their impressions of their partners from both before and after the first face-to-face meeting. We expected, based on prior research demonstrating the importance of physical attractiveness in romantic attraction both on- and offline, that changes in beliefs about partners’ physical appeal would be the most powerful predictor of relationship longevity. However, they were unrelated to relationship success. Across all the dimensions we examined, impressions were in fact relatively stable, but when respondents said they knew their partners better after meeting face-to-face, relationships lasted longer.v
What Makes a Blog a Blog? Exploring User Conceptualizations of an Old “New” Online Medium
Authors: Treem Jeffrey , Thomas Kristin
Published: November 2022

What is a blog? While this may seem like a basic question, it is the subject of dispute among researchers, and hardly settled among users of the medium. We return to this fundamental inquiry by asking 89 blog-reading college students how they conceptualize the term and what purpose blogs serve for them. Our findings suggest that perceptions of blogs are varied and ambiguous, and definitions largely fail to distinguish blogs from other online media. We discuss what this means for the study of blogs and other “new” media and argue that more constructionist approaches are necessary to develop ecologically valid theories of media perceptions and use.
Automatic Identification and Presentation of Twitter Content for Planned Events
Authors: Becker Hila , Chen Feiyang , Iter Dan , Naaman Mor , Gravano Luis
Published: November 2022

We demonstrate a system for augmenting information about planned events with Twitter messages, using a set of automatic query building strategies. We present two alternative interfaces to our system, namely, a browser plug-in and a customizable Web interface.
Modelling Action Cascades in Social Networks
Authors: Dave Kushal , Bhatt Rushi , Varma Vasudeva
Published: November 2022

The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce ``word-of-mouth" effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual's influencing capability on its peers is by its reach for a certain action. We formulate identifying the influencers in a network as a problem of predicting the average depth of cascades an individual can trigger. We first empirically identify factors that play crucial role in triggering long cascades. Based on the analysis, we build a model for predicting the cascades triggered by a user for an action. The model uses features like influencing capabilities of the user and their friends, influencing capabilities of the particular action and other user and network characteristics. Experiments show that the model effectively improves the predictions over several baselines.
Insights into Internet Memes
Authors: Bauckhage Christian
Published: November 2022

Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings.
Classifying the Political Leaning of News Articles and Users from User Votes
Authors: Zhou Daniel Xiaodan , Resnick Paul , Mei Qiaozhu
Published: November 2022

Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0% accuracy on articles.
How Does Social Capital Affect Retweets?
Authors: Recuero Raquel , Araujo Ricardo , Zago Gabriela
Published: November 2022

In Twitter, a retweet happens when a user copies another user's tweet and indicates that the content is originally from someone else, and is a common way to share information. This paper explores user’s perceptions of the way social capital influences retweet practices. We discuss how Twitter users perceive values and how they create benefits for the social network and themselves from three points of view: referrals, information access and timing. We do so through a qualitative analysis of questionnaires answered by Twitter users and four quantitative case studies of retweets. We analyze users' motivations, map the spread of retweets and their content, and discuss how referrals, information access and timing can play important roles in what and how users will retweet. Our findings lead to the conclusion that users seek individual benefits but the competition for social capital allows for collective benefits to emerge.
Searching Twitter: Separating the Tweet from the Chaff
Authors: Hurlock Jonathan , Wilson Max
Published: November 2022

Within the millions of digital communications posted in online social networks, there is undoubtedly some valuable and useful information. Although a large portion of social media content is considered to be babble, research shows that people share useful links, provide recommendations to friends, answer questions, and solve problems. In this paper, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features. Further, we saw that these features can be weighted as according to different types of search tasks. Our results contribute a novel framework for extracting useful information from real-time streams of social-media content that will be used in the design of a future retrieval system.
Media Landscape in Twitter: A World of New Conventions and Political Diversity
Authors: An Jisun , Cha Meeyoung , Gummadi Krishna , Crowcroft Jon
Published: November 2022

We present a preliminary but groundbreaking study of the media landscape of Twitter. We use public data on whom follows who to uncover common behaviour in media consumption, the relationship between various classes of media, and the diversity of media content which social links may bring. Our analysis shows that there is a non-negligible amount of indirect media exposure, either through friends who follow particular media sources, or via retweeted messages. We show that the indirect media exposure expands the political diversity of news to which users are exposed to a surprising extent, increasing the range by between 60-98%. These results are valuable because they have not been readily available to traditional media, and they can help predict how we will read news, and how publishers will interact with us in the future.
Memes Online: Extracted, Subtracted, Injected, and Recollected
Authors: Simmons Matthew , Adamic Lada , Adar Eytan
Published: November 2022

Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media.
Rating Friends Without Making Enemies
Authors: Adamic Lada , Lauterbach Debra , Teng Chun-Yuen , Ackerman Mark
Published: November 2022

As online social networks expand their role beyond maintaining existing relationships, they may look to more faceted ratings to support the formation of new connections between their users. Our study focuses on one community employing faceted ratings, CouchSurfing.org, and combines data analysis of ratings, a large-scale survey, and in-depth interviews. In order to understand the ratings, we revisit the notions of friendship and trust and uncover an asymmetry: close friendship includes trust, but high levels of trust can be achieved without close friendship. To users, providing faceted ratings presents challenges, including differentiating and quantifying inherently subjective feelings such as friendship and trust, concern over a friend's reaction to a rating, and knowledge of how ratings can affect others' reputations. One consequence of these issues is the near absence of negative feedback, even though a small portion of actual experiences and privately held ratings are negative. We show how users take this into account when formulating and interpreting ratings, and discuss designs that could encourage more balanced feedback.
Detecting and Tracking Political Abuse in Social Media
Authors: Ratkiewicz Jacob , Conover Michael , Meiss Mark , Goncalves Bruno , Flammini Alessandro , Menczer Filippo
Published: November 2022

We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation.  We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.
The Prevalence of Political Discourse in Non-Political Blogs
Authors: Munson Sean , Resnick Paul
Published: November 2022

Though political theorists have emphasized the importance of political discussion in non-political spaces, past study of online political discussion has focused on primarily political websites. Using a random sample from Blogger.com, we find that 25% of all political posts are from blogs that post about politics less than 20% of the time, because the vast majority of blogs post about politics some of the time but infrequently. Far from being taboo topics in those non- political blogs, political posts got slightly more comments than non-political posts in those same blogs, and the comments overwhelmingly engage the political topics of the post, mostly agreeing but frequently disagreeing as well. We argue that non-political spaces devoted primarily to personal diaries, hobbies, and other topics represent a substantial place of online political discussion and should be a site for further study.
Center of Attention: How Facebook Users Allocate Attention across Friends
Authors: Backstrom Lars , Bakshy Eytan , Kleinberg Jon , Lento Thomas , Rosenn Itamar
Published: November 2022

An individual's personal network — their set of social contacts — is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely. Using data from Facebook, we find that this balance of attention is a relatively stable property of an individual over time, and that it displays interesting variation across both different groups of people and different modes of interaction. In particular, activities based on communication involve a much higher focus of attention than activities based simply on observation, and these two types of modalities also exhibit different forms of variation in interaction patterns both within and across groups. Finally, we contrast the amount of attention paid by individuals to their most frequent contacts with the rate of change in the identities of these contacts, providing a measure of churn for this set.
Connecting Mutually Influencing Bloggers
Authors: Pal Aditya , Kawale Jaya
Published: November 2022

The blogosphere shows the characteristics of a power law distribution where a small set of the bloggers (influentials) get the majority of readership and the vast majority receives little traffic. Blogger recommendation algorithms aim at finding influentials for recommendation, putting bloggers with limited readership at further disadvantage. These bloggers could benefit from mutual endorsement of each other with the eventual goal of forming strong local communities with broader readership. In this paper, we propose a recommendation algorithm to connect blogger pairs with the intent that once connected the bloggers would share a mutually influencing relationship between them. In particular, we compute bloggers' influence profile based on how much she influences her blog friends and recommend bloggers with similar influence profiles. We characterize bloggers into four different groups: global leaders, connectors, local leaders, isolates. Our result shows marginal benefit for isolates and significant benefit for local leaders. Our approach can be instructive in building intelligent recommendation engine for bloggers with limited readership to build strong local communities.
LeadLag LDA: Estimating Topic Specific Leads and Lags of Information Outlets
Authors: Nallapati Ramesh , Shi Xiaolin , McFarland Daniel , Leskovec Jure , Jurafsky Daniel
Published: November 2022

Identifying which outlet in social media leads the rest in disseminating novel information on specific topics is an interesting challenge for information analysts and social scientists. In this work, we hypothesize that novel ideas are disseminated through the creation and propagation of new or newly emphasized key words, and therefore lead/lag of outlets can be estimated by tracking word usage across these outlets. First, we demonstrate the validaty of our hypothesis by showing that a simple TF-IDF based nearest-neighbors approach can recover generally accepted lead/lag behavior on the outlets pair of ACM journal articles and conference papers. Next, we build a new topic model called LeadLag LDA that estimates the lead/lag of the outlets on specific topics. We validate the topic model using the lead/lag results from the TF-IDF nearest neighbors approach. Finally, we present results from our model on two different outlet pairs of blogs vs. news media and grant proposals vs. research publications that reveal interesting patterns.
Improving Text Clustering with Social Tagging
Authors: Ares M. Eduardo , Parapar Javier , Barreiro Álvaro
Published: November 2022

In this paper we study the use of social bookmarking to improve the quality of text clustering. Recently constrained clustering algorithms have been presented as a successful tool to introduce domain knowledge in the clustering process. This paper uses the tags saved by the users of Delicious to generate non artificial constraints for constrained clustering algorithms. The study demonstrates that it is possible to achieve a high percentage of good constraints with this simple approach and, more importantly, the evaluation shows that the use of these constraints produces a great improvement (up to 91.25%) of the clustering algorithms effectiveness.
Divided They Tweet: The Network Structure of Political Microbloggers and Discussion Topics
Authors: Feller Albert , Kuhnert Matthias , Sprenger Timm , Welpe Isabell
Published: November 2022

In the context of a national election, this study explores more than 69,000 Twitter messages containing mentions of political parties and about 2,500 related user profiles to investigate the network structure of political microbloggers with respect to, first, their party preference and, second, the topics they discuss. We find that political microbloggers tend to follow like-minded peers. Microbloggers in a cohesive group tend to have the same political preferences. In addition, we conduct a content analysis of the political debate on Twitter to explore which topics and politicians are discussed and whether this debate reflects an ideological divide among participating users. While there are some discussion topics that are dominated by politically like-minded microbloggers, the majority of topics is discussed by a diverse group of microbloggers with various political preferences.
Towards Discovery of Influence and Personality Traits through Social Link Prediction
Authors: Nguyen Thin , Phung Dinh , Adams Brett , Venkatesh Svetha
Published: November 2022

Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.
Automatically Identifying Groups Based on Content and Collective Behavioral Patterns of Group Members
Authors: Gregory Michelle , Engel Dave , Bell Eric , Piatt Andy , Dowson Scott , Cowell Andrew
Published: November 2022

Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member’s interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.
Is Twitter a Good Place for Asking Questions? A Characterization Study
Authors: Paul Sharoda , Hong Lichan , Chi Ed
Published: November 2022

People often turn to their social networks to fulfill their information needs. We conducted a study of question asking and answering (Q&A) behavior on Twitter. We found that the most popular question types were rhetorical and factual. Surprisingly, along with entertainment and technology questions, people asked personal and health-related questions. The majority of questions received no response, while a handful of questions received a high number of responses. The larger the askers’ network, the more responses she received; however, posting more tweets or posting more frequently did not increase chances of receiving a response. Most often the ‘follow’ relationship between asker and answerer was one-way. We provide a rich characterization of Q&A in social information streams and discuss implications for design.
Trust, Reputation and the Small Firm: Building Online Brand Reputation for SMEs
Authors: Gligorijevic Barbara , Leong Benjamin
Published: November 2022

Social media provides numerous opportunities for small businesses to promote their products and services, build brand communities and reach diverse market niches. An important factor in seizing these opportunities is developing trust and creating reputation among consumers. This qualitative study examines how a group of Australian small business managers utilize social media websites to connect to, communicate with and maintain their customer base. For the purpose of this paper we are using case studies of four companies physically based in Victoria, Australia. These businesses have a high presence in online consumer groups, being both active members of communities and representatives of their businesses. The duality of their role as participant and company representative imposes difficulties in creating reputation among community members. We have used in-depth interviews as a primary research method, additionally monitoring their activities on social media sites such as forums, social networking services, blogs and micro-blogs. We have identified practices helpful for developing trust, building reputation and create a brand image in online communities.
Mapping the Political Twitterverse: Candidates and Their Followers in the Midterms
Authors: Hanna Alexander , Sayre Ben , Bode Leticia , Yang JungHwan , Shah Dhavan
Published: November 2022

Twitter provides a new and important tool for political actors. In the 2010 midterm elections, the vast majority of candidates for the U.S. House of Representatives and virtually all candidates for U.S. Senate and governorships used Twitter to reach out to potential supporters, direct them to particular pieces of information, request campaign contributions from them, and mobilize their political action. Despite the level of activity, we have little understanding of what the political Twitterverse looks like in terms of communication and discourse. This project seeks to remedy that lack of understanding by mapping candidates and their followers according to their use of hashtags (keywords) and user mentions (direct mentioning of other Twitter users). We have a unique data set constructed from tweets of most of the candidates running for the U.S. House of Representatives in 2010, all the candidates for the Senate and governorships, and a random sample of their followers. From this we utilize multidimensional scaling to construct a visual map based on hashtag and user mention usage. We find that our data have both local and global interpretations that reflect both political leaning and strategies of communication. This study provides insight into innovation in new media usage in political behavior in particular and a bounded topic space in general.
Unearthing People from the SaND: Relationship Discovery with Social Media in the Enterprise
Authors: Perer Adam , Guy Ido , Uziel Erel , Ronen Inbal , Jacovi Michal
Published: November 2022

The popularity of social media across the Internet re- sults in people-centric data sources that users can potentially leverage for relationship discovery tasks. In this paper, we describe SaNDVis, a visual system that supports tasks like expertise location, team building, and team coordination in the enterprise. By integrating social position, evidence, and facets into SaNDVis, we demonstrate how users can reflect on existing relationships as well as build new relationships in an enterprise setting.
Maintaining Ties on Social Media Sites: The Competing Effects of Balance, Exchange, and Betweenness
Authors: Romero Daniel , Meeder Brendan , Barash Vladimir , Kleinberg Jon
Published: November 2022

When users interact with one another on social media sites, the volume and frequency of their communication can shift over time, as their interaction strengthens or weakens. We study the interplay of several competing factors in the maintainance of such links, developing a methodology that can begin to separate out the effects of several distinct social forces. In particular, if two users develop mutual relationships to third parties, this can exert a complex effect on the level of interaction between the two users — it has the potential to strengthen their relationship, through processes related to triadic closure, but it can also weaken their relationship, by drawing their communication away from one another and toward these newly formed connections. We analyze the interplay of these competing forces and relate the underlying issues to classical principles in sociology — specifically, the theories of balance, exchange, and betweenness. In the course of our analysis, we also provide novel approaches for dealing with a common methodological problem in studying ties on social media sites: the tremendous volatility of these ties over time makes it hard to compare one's results to simple baselines that assume static or stable ties, and hence we must develop a set of more complex baselines that takes this temporal behavior into account.
Hierarchical Bayesian Models for Latent Attribute Detection in Social Media
Authors: Rao Delip , Paul Michael , Fink Clay , Yarowsky David , Oates Timothy , Coppersmith Glen
Published: November 2022

We present several novel minimally-supervised models for detecting latent attributes of social media users, with a focus on ethnicity and gender. Previouswork on ethnicity detection has used coarse-grained widely separated classes of ethnicity and assumed the existence of large amounts of training data such as the US census, simplifying the problem. Instead, we examine content generated by users in addition to name morpho-phonemics to detect ethnicity and gender. Further, weaddress this problem in a challenging setting where the ethnicity classes are more fine grained -- ethnicity classes in Nigeria -- and with very limited training data.