bluuebunny/crossref_metadata_2025 huggingface dataset:
Thematic and Social Indicators for Flickr Groups
Authors: Prieur Christophe , Pissard Nicolas , Beuscart Jean-Samuel , Cardon Dominique , Pons Pascal
Published: November 2022

We study here Flickr groups in order to see whether they are actual communities or rather essentially thematic clusters. We describe a methodological framework for the analysis of networks of group members, measuring social density and tag dispersion among groups and give some results on a sample of 450 groups having around 500 members each.
A Shallow Approach to Subjectivity Classification
Authors: Raaijmakers Stephan , Kraaij Wessel
Published: November 2022

We present a shallow linguistic approach to subjectivity classification. Using multinomial kernel machines, we demonstrate that a data representation based on counting character n-grams is able to improve on results previously attained on the MPQA corpus using word-based n-grams and syntactic information. We compare two types of string-based representations: key substring groups and character n-grams. We find that word-spanning character n-grams significantly reduce the bias of a classifier, and boost its accuracy.
Clustering Tags in Enterprise and Web Folksonomies
Authors: Simpson Edwin
Published: November 2022

Tags lack organizational structure limiting their utility for navigation. We present two clustering algorithms that improve this by organizing tags automatically. We apply the algorithms to two very different datasets, visualize the results and propose future improvements.
BlogTrackers: A Tool for Sociologists to Track and Analyze Blogosphere
Authors: Agarwal Nitin , Kumar Shamanth , Liu Huan , Woodward Mark
Published: November 2022

We present a tool, BlogTrackers, which helps sociologists to track and analyze blogs of particular interests by designing and integrating unique features. We present an overview of BlogTrackers, illustrate its functions of various components of BlogTrackers, and outline future work for expansion in meeting the growing needs of sociologists.
Attentio Brand Maps
Authors: Janssens Frizo
Published: November 2022

Attentio Brand Maps (patent pending) automatically measure and visualise the evolution of perceived associations or relatedness between "entities" and "topics". Entities can be brands, products, organisations, people, etc., while topics can be, for example, events or features. Attentio Brand Maps (ABMs) can be generated for any type of textual data having a temporal aspect (timestamps): blogs, forums, news, scientific articles, patent collections, corporate data, etc. The result is a temporal visualisation of large-scale data sets, facilitating human pattern detection. Part of the commercial value of ABMs lies in the possibility to identify brands and topics that are discussed online together, as well as their evolution, and to identify why certain brands and topics are related.
Stochastic Models of User-Contributory Web Sites
Authors: Hogg Tad , Lerman Kristina
Published: November 2022

We describe a general stochastic processes-based approach to modeling user-contributory web sites, where users create, rate and share content. These models describe aggregate measures of activity and how they arise from simple models of individual users. This approach provides a tractable method to understand user activity on the web site and how this activity depends on web site design choices, especially the choice of what information about other users' behaviors is shown to each user. We illustrate this modeling approach in the context of user-created content on the news rating site Digg.
Modeling Blog Dynamics
Authors: Goetz Michaela , Leskovec Jure , McGlohon Mary , Faloutsos Christos
Published: November 2022

How do blogs produce posts? What local, underlying mechanisms lead to the bursty temporal behaviors observed in blog networks? Earlier work analyzed network patterns of blogs and found that blog behavior is bursty and often follows power laws in both topological and temporal characteristics. However, no intuitive and realistic model has yet been introduced, that can lead to such patterns. This is exactly the focus of this work. We propose a generative model that uses simple and intuitive principles for each individual blog, and yet it is able to produce the temporal characteristics of the blogosphere together with global topological network patterns, like power-laws for degree distributions, for inter-posting times, and several more. Our model ZC uses a novel 'zero-crossing' approach based on a random walk, combined with other powerful ideas like exploration and exploitation. This makes it the first model to simultaneously model the topology and temporal dynamics of the blogosphere. We validate our model with experiments on a large collection of 45,000 blogs and 2.2 million posts.
Seeking and Offering Expertise Across Categories: A Sustainable Mechanism Works for Baidu Knows
Authors: Yang Jiang , Wei Xiao
Published: November 2022

This paper presents the first comprehensive exploration of the largest Chinese online knowledge sharing community-Baidu Knows. With analyzing 5.2 millions questions and 2.7 million users participated during 4.5 months on the site in 2008, we investigate how users adjust initial attempts and behave differently according to the level of participation; in particular, there is a positive dynamic for answerers to input more, be more focused, win more, and thus be rewarded more. As the result, a core user group forms to actively participate in both asking and answering across categories, thus maintaining a self-sufficient community. In addition, a prominent "sense of community" would enhance the social bonds within the community, especially for the contributors who can offer expertise but can rarely learn from others. The study suggests Baibu Knows as a successful design instance for further studies.
Characterizing the Portuguese Blogosphere
Authors: Couto Telmo , Ribeiro Cristina , Nunes Sérgio
Published: November 2022

Over the years, blogs have become a popular and influent part of the web, having many distinctive features - they are usually thematic and organized by communities, and their contents have a chronological order. In this paper, we present a characterization of the portuguese blogosphere based on a large set of portuguese blogs collected from multiple sources across a 5-year period. The extent of our collection allows us to draw some conclusions on the evolution of the blogging activity over the years. We survey and apply methods that have been used for web and blog characterization and, using samples of blogs obtained from other sources, we try to estimate the extent to which this collection can be representative of the portuguese blogosphere. The paper presents multiple statistics that allow us to characterize the portuguese blogosphere and compare it to results obtained with other studies.
MakeMyPage: Social Media Meets Automatic Content Generation
Authors: Iacobelli Francisco , Hammond Kristian , Birnbaum Larry
Published: November 2022

Finding out about a topic online can be time consuming. It involves visiting multiple news sites, encyclopedia entries, video repositories and other resources while discarding irrelevant information. MakeMyPage aims to speed this process by combining automatic aggregation of information with social media to build web pages with images, videos and links to important information about a topic. MakeMyPage uses automatic aggregation to provide the initial content of the web pages. This content is organized by type: blogs, news, web links, images, video and a main article. MakeMyPage creates a web page by selecting a few items from each category, plus links to more resources within it. Users can vote on the links and media they like best for a given topic and, based on these votes, the system promotes them to and within the main web page. MakeMyPage can be thought of as a collection of wiki pages where people enhance automatically generated content not by editing the text in it, but by voting and suggesting new links. The system's focus is on the organization of content that is genuinely useful and on point. MakeMyPage continuously tracks popular search queries and maintains a database of web pages about these topics.
Delta TFIDF: An Improved Feature Space for Sentiment Analysis
Authors: Martineau Justin , Finin Tim
Published: November 2022

Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets.
Predicting the Polarity Strength of Adjectives Using WordNet
Authors: Williams Gbolahan , Anand Sarabjot
Published: November 2022

A key element of any sentiment analysis system is the ability to assign a polarity strength value to words appearing within the documents. In this paper we present a novel approach to polarity strength assignment. The approach is knowledge based in that it uses WordNet to build an adjective graph which is used to measure semantic distance between words of known polarity (reference or seed words) and the target word, which is then used to assign a polarity to the target word. We extend previous work in this area by using a small training data set to learn an optimal predictor of polarity strength and to dampen polarity assigned to non-polar adjectives. We also extend the coverage of previous approaches by exploring additional lexical relations not studied previously. The method has been evaluated on a validation set and shows excellent potential in reducing the assignment of spurious polarity and accurately predicting polarity values for polar adjectives.
Leaders and Negotiators: An Influence-based Metric for Rank
Authors: Ghosh Rumi , Lerman Kristina
Published: November 2022

We propose influence as a measure of the centrality of nodes in a network. Influence takes into account not only direct links but also all paths between nodes. We parametrize the influence metric by a variable alpha that measures the strength of links. Variations in rankings as alpha changes provides a mechanism to identify the central nodes within communities (leaders), as well as nodes that act as bridges between communities (negotiators).
The Evolution of Scientific Paper Title Networks
Authors: Qazvinian Vahed , Radev Dragomir R.
Published: November 2022

In spite of enormous previous efforts to model the growth of various networks, there have only been a few works that successfully describe the evolution of latent networks. In a latent network edges do not represent interactions between nodes, but show some proximity values. In this paper we analyze the structure and evolution of a specific type of latent networks over time by looking at a wide range of document similarity networks, in which scientific titles are nodes and their similarities are weighted edges. We use scientific papers as the corpora in order to determine the behavior of authors in choosing words for article titles. The aim of our work is to see whether term selection for titles depends on earlier published titles.
Contextuality and Beyond: Investigating an Online Diary Corpus
Authors: Teddiman Laura
Published: November 2022

Heylighen and Dewaele’s (2002) F-score, a measure of formality developed based on categorical frequencies of word types, is used as a starting point for an investigation of an online diary corpus. Comparisons are made between results in the main corpus of diary entries, a smaller corpus of diary comments, and with previously calculated F-scores for similar types of data (Nowson, Oberlander & Gill, 2005). While the overall F-score is similar in these two corpora, results show that internal make-up of the categories upon which the calculation is based can differ. This suggests that while the F-score is a good measure of formality/contextuality and is useful in distinguishing between genres on a large scale, more detailed analyses are required to more completely describe and situate genres with respect to one another.
User Evaluation of a System for Classifying and Displaying Political Viewpoints of Weblogs
Authors: Oh Alice , Lee Hyunjong , Kim Youngmin
Published: November 2022

This paper presents a Web-based user evaluation of a system for classifying and presenting political viewpoints of blog posts. The system is based on a classification model trained using a supervised learning algorithm, and the data set consists of recent posts from blogs that are self-identified as a liberal or a conservative viewpoint. We first discuss the classification process. Then, with a prototype system for retrieving and classifying political blogs, we look at how the classification results can be presented to users in order to improve the blog search experience. We describe an online user study with 15 users, and the study shows that users preferred the search results page that clearly shows the political viewpoint classification.
Your Brain on Facebook: Neuropsychological Associations with Social Versus other Media
Authors: Fisher Kristie , Counts Scott
Published: November 2022

We measured individuals’ mental associations between four types of media (books, television, social/Facebook, and general informational web pages) and relevant concepts (Addictive, Story, Interesting, Frivolous, Personal, and Useful) using three different measurements: a Likert scale questionnaire, a speeded Yes/No judgment task, and electrical brain activity. The three measures were designed to capture associations at different levels of mental processing, from very automatic (electrical brain activity) to conscious and reasoned (questionnaire). At more conscious levels of cognitive processing, Facebook was seen as interesting, addictive, and highly personal. Results for the electrical brain activity measure show that Facebook tells less of a story and, surprisingly, is less personal than other forms of media. We discuss differences in results across the three measures and how our findings can inform the design of future social media systems.
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
Authors: O'Connor Brendan , Balasubramanyan Ramnath , Routledge Bryan , Smith Noah
Published: November 2022

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer conﬁdence and political opinion over the 2008 to 2009 period, and ﬁnd they correlate to sentiment word frequencies in contempora- neous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The re- sults highlight the potential of text streams as a substi- tute and supplement for traditional polling. consumer conﬁdence and political opinion, and can also pre- dict future movements in the polls. We ﬁnd that temporal smoothing is a critically important issue to support a suc- cessful model.
Predicting the Speed, Scale, and Range of Information Diffusion in Twitter
Authors: Yang Jiang , Counts Scott
Published: November 2022

We present results of network analyses of information diffusion on Twitter, via users’ ongoing social interactions as denoted by “@username” mentions. Incorporating survival analysis, we constructed a novel model to capture the three major properties of information diffusion: speed, scale, and range. On the whole, we find that some properties of the tweets themselves predict greater information propagation but that properties of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors. Implications for end users and system designers are discussed.
Devils, Angels, and Robots: Tempting Destructive Users in Social Media
Authors: Lee Kyumin , Eoff Brian , Caverlee James
Published: November 2022

Social media sites derive their value by providing a popular and dependable community for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure long-term success, we present a prototype system for automatically detecting and profiling destructive users in social media. We described the architecture of the system - inspired by the "broken windows" theory embraced by law enforcement - the results and insights gained from a preliminary study conducted to determine the efficacy of our approach, and a discussion of our ongoing research.
Mining User Home Location and Gender from Flickr Tags
Authors: Popescu Adrian , Grefenstette Gregory
Published: November 2022

Personal photos and their associated metadata reveal different aspects of our lives and, when shared online, let others have an idea about us. Automating the extraction of personal information is an arduous task but it contributes to better understanding and serving users. Here we present methods for analyzing textual metadata associated to Flickr photos that unveil users’ home location and gender. We test our techniques on a sample of 30,000 people coming from six different countries, allowing us to compare results across cultures and point out similarities and differences.
Classifier Calibration for Multi-Domain Sentiment Classification
Authors: Raaijmakers Stephan , Kraaij Wessel
Published: November 2022

Textual sentiment classifiers classify texts into a fixed number of affective classes, such as positive, negative or neutral sentiment, or subjective versus objective information. It has been observed that sentiment classifiers suffer from a lack of generalization capability: a classifier trained on a certain domain generally performs worse on data from another domain. This phenomenon has been attributed to domain-specific affective vocabulary. In this paper, we propose a voting-based thresholding approach, which calibrates a number of existing single-domain classifiers with respect to sentiment data from a new domain. The approach presupposes only a small amount of annotated data from the new domain. We evaluate three criteria for estimating thresholds, and discuss the ramifications of these criteria for the trade-off between classifier performance and manual annotation effort.
Voices of Vlogging
Authors: Biel Joan-Isaac , Gatica-Perez Daniel
Published: November 2022

Vlogs have rapidly evolved from the ’chat from your bedroom’ format to a highly creative form of expression and communication. However, despite the high popularity of vlogging, automatic analysis of conversational vlogs have not been attempted in the literature. In this paper, we present a novel analysis of conversational vlogs based on the characterization of vloggers’ nonverbal behavior. We investigate the use of four nonverbal cues extracted automatically from the audio channel to measure the behavior of vloggers and explore the relation to their degree of popularity and that of their videos. Our study is validated on over 2200 videos and 150 hours of data, and shows that one nonverbal cue (speaking time) is correlated with levels of popularity with a medium size effect.
Local Networks, Local Topics: Structural and Semantic Proximity in Blogspace
Authors: Cointet Jean-Philippe , Roth Camille
Published: November 2022

Blog networks are often described as "small world" social networks where links are primarily created towards similar-minded individuals and well-connected bloggers. Examining a portion of the US blogosphere on several months, we find that bloggers relate to each other in a relatively local fashion, overwhelmingly and preferentially establishing links towards a limited neighborhood rather than the whole network. Furthermore, while long-distance interactions may indeed be dominated by homophily and authority effects, we show that close neighborhoods feature significantly more horizontal and diversified interactions — thereby challenging the conjecture of a widespread balkanization of Internet communities. We shed further light on this issue by describing the dual evolution of social and semantic proximity before and after two individuals interact with each other. We discuss in particular whether interactions are preceded or followed by a structural "contraction" and/or by an increasing similarity of the surrounding local social network.
An Overview of SMOB 2: Open, Semantic and Distributed Microblogging
Authors: Passant Alexandre , Bojars Uldis , Breslin John , Hastrup Tuukka , Stankovic Milan , Laublet Philippe
Published: November 2022

This short paper provides an overview of the architecture and features of SMOB 2 — a platform for open, semantic and distributed microblogging combining social web principles and state-of-the-art semantic web and linked data technologies.
Ranking Social News Articles Based on Voter Credibility
Authors: Kim Kanghak , Go Geonhyeok , Park Hyun-woo , Han Sangki
Published: November 2022

Given the wide spread of web-based tools and social news media services which are facilitating grassroots journalism, there is a growing interest in selecting credible news content among a huge number of articles. Currently, most of social news services rely on reader votes to select articles for their front pages. However, the fundamental problem is that users’ votes often stand for popularity rather than credibility. In this paper, we propose a system to address this problem using a weighted voting system. Specifically, we trace thousands of users and their votes, differentiating them depending on how credible the articles voted for are. We then calculate each user’s voting credibility and use it as the user’s voting weight in our system. The results indicate that our method performs better in selecting credible news articles than other methods replying on a “one person, one vote” system. The results suggest feasible solutions to problems in social news media concerning media credibility.
Longevity in Second Life
Authors: Teng ChunYuen , Adamic Lada
Published: November 2022

User retention is important to the success of online social media, particularly in virtual world settings where users shape one another’s online experience. We study a rich set of variables, including social network and group membership, chatting, and transactions, in order to predict which users will stay and which ones will leave. We find that simple variables directly measuring the intensity and diversity of a user’s interaction with others are most predictive.
Trading Strategies to Exploit Blog and News Sentiment
Authors: Zhang Wenbin , Skiena Steven
Published: November 2022

We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified.
TweetTrader.net: Leveraging Crowd Wisdom in a Stock Microblogging Forum
Authors: Sprenger Timm
Published: November 2022

TweetTrader.net is a stock microblogging forum that leverages the wisdom of crowds to aggregate the information contained in stock-related tweets. Based on insights from academic research on stock microblogs, the application integrates inputs from text classification, user voting and a proprietary Stock Game in order to extract the sentiment (i.e., the bullishness) of online investors with respect to all publicly traded companies of the S&P 500.
Task Specialization in Social Production Communities: The Case of Geographic Volunteer Work
Authors: Masli Mikhil , Priedhorsky Reid , Terveen Loren
Published: November 2022

In social production communities, users' individual and collective efforts lead to the creation of valuable resources — cf. Wikipedia, Open Street Map, and Reddit. Contributors to such communities often specialize in the tasks they choose to do. We found evidence for specialization by work type in Cyclopath, a geographic wiki for bicyclists -- most users edit a single type of map feature, such as points of interest or roads and trails. We also saw a user lifecycle effect: as users gain experience, they specialize in editing roads and trails. Our findings suggest more effective ways to organize social production interfaces, compose units of work, and match them to users who want to help.
What Stops Social Epidemics?
Authors: Ver Steeg Greg , Ghosh Rumi , Lerman Kristina
Published: November 2022

Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.
Why do People Retweet? Anti-Homophily Wins the Day!
Authors: Macskassy Sofus , Michelson Matthew
Published: November 2022

Twitter and other microblogs have rapidly become a significant means by which people communicate with the world and each other in near realtime. There has been a large number of studies surrounding these social media, focusing on areas such as information spread, various centrality measures, topic detection and more. However, one area which has not received much attention is trying to better understand what information is being spread and why it is being spread. This work looks to get a better understanding of what makes people spread information in tweets or microblogs through the use of retweeting. Several retweet behavior models are presented and evaluated on a Twitter data set consisting of over 768,000 tweets gathered from monitoring over 30,000 users for a period of one month. We evaluate the proposed models against each user and show how people use different retweet behavior models. For example, we find that although users in the majority of cases do not retweet information on topics that they themselves Tweet about as or from people who are "like them" (hence anti-homophily), we do find that models which do take homophily, or similarity, into account fits the observed retweet behaviors much better than other more general models which do not take this into account. We further find that, not surprisingly, people's retweeting behavior is better explained through multiple different models rather than one model.
Find Me the Right Content! Diversity-Based Sampling of Social Media Spaces for Topic-Centric Search
Authors: De Choudhury Munmun , Counts Scott , Czerwinski Mary
Published: November 2022

Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is: how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large-scale information spaces are inherently diverse, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can significantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase breadth in the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the "compressive sensing" literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users find samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15--45%.
More Voices Than Ever? Quantifying Media Bias in Networks
Authors: Lin Yu-Ru , Bagrow James , Lazer David
Published: November 2022

Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Here we propose empirical measures to quantify the extent and dynamics of social (blog) and mainstream (news) media bias. We focus on a particular form of bias--coverage quantity--as applied to stories about the 111th US Congress. We compare observed coverage of Members of Congress against a null model of unbiased coverage, testing for biases with respect to political party, popular front runners, regions of the country, and more. Our measures suggest distinct characteristics in news and blog media. A simple generative model, in agreement with data, reveals differences in the process of coverage selection between the two media.
Just a Click Away: Social Search and Metadata in Predicting File Discovery
Authors: Shami N. Sadat , Muller Michael , Millen David
Published: November 2022

Social search has been claimed to improve content discovery by allowing users to draw on their social network to find relevant content. Thus social network information, complemented with metadata, can enhance the search for new information. We examine the relative contribution of social network information and file metadata in predicting downloads of files by analyzing the file browsing behavior of 5,723 users of a social file sharing service in a large global company. The following factors increase the likelihood of download: (a) if the file author is in the user’s social network; (b) if the file has been downloaded by or shared with others in the user’s network; and (c) if the file’s metadata is a good match to the user’s interests. Semi-structured interviews with 18 users provided deeper insight into why these factors are important. Our findings demonstrate the value of the social graph for finding files, with implications for relevant content and people discovery.
4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community
Authors: Bernstein Michael , Monroy-Hernández Andrés , Harry Drew , André Paul , Panovich Katrina , Vargas Greg
Published: November 2022

We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b/ succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b/ and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90% of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b/ participants use to establish status and frame their interactions.
Selecting Quality Twitter Content for Events
Authors: Becker Hila , Naaman Mor , Gravano Luis
Published: November 2022

Social media sites such as Twitter contain large amounts of user contributed messages for a wide variety of real-world events. While some of these "event messages" might contain interesting and useful information (e.g., event time, location, participants, opinions), others might provide little value (e.g., using heavy slang, incomprehensible language) to people interested in learning about an event. Techniques for effective selection of quality event content may therefore help improve applications such as event browsing and search.In this paper, we explore approaches for finding representative messages among a set of Twitter messages that correspond to the same event, with the goal of identifying high quality, relevant messages that provide useful event information. We evaluate our approaches using a large-scale dataset of Twitter messages, and show that we can automatically select event messages that are both relevant and useful.
Beyond Trending Topics: Real-World Event Identification on Twitter
Authors: Becker Hila , Naaman Mor , Gravano Luis
Published: November 2022

User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.
Factors Affecting Response Quantity, Quality, and Speed for Questions Asked Via Social Network Status Messages
Authors: Teevan Jaime , Morris Meredith , Panovich Katrina
Published: November 2022

Social networking tools enable people to easily ask questions of large groups of personal acquaintances, but the effectiveness of doing so depends in part on how the question is phrased. In this paper we present a study in which 282 participants posted variants of the same question as their status message on Facebook. We analyze the quantity, quality, and speed of the responses each variant received. We find that by ending an information need with a question mark, explicitly scoping the audience, and being succinct, a person can increase the likelihood of quickly receiving many high-quality answers.
Using Twitter to Detect and Tag Important Events in Sports Media
Authors: Lanagan James , Smeaton Alan
Published: November 2022

In this paper we examine the effectiveness of using a filtered stream of tweets from Twitter to automatically identify events of interest within the video of live sports transmissions. We show that using just the volume of tweets generated at any moment of a game actually provides a very accurate means of event detection, as well as an automatic method for tagging events with representative words from the tweet stream. We compare this method with an alternative approach that uses complex audio-visual content analysis of the video, showing that it provides near-equivalent accuracy for major event detection at a fraction of the computational cost. Using community tweets and discussion also provides a sense of what the audience themselves found to be the talking points of a video.
Facebook Use and Social Capital — A Longitudinal Study
Authors: Brandtzæg Petter , Nov Oded
Published: November 2022

How does the use of social networking sites (SNSs) affect social capital offline and online? The increasing popularity of SNSs such as Facebook (FB) implies a significant change in the way we interact with others. In an extensive longitudinal study (N = 311), carried out in Norway between 2008 - 2010, we examined the relationship between FB use and social capital, with a focus on interpersonal interaction, including face-to-face (F2F) interaction. Our findings suggest that overall FB use does not significantly affect offline capital, despite a significant increase in the use of FB from 2008 to 2010, after controlling for age, gender and education. However, different types of communication strategies on FB correlate with various kinds of F2F interaction. Importantly, FB users who focus on FB communication with their close friends, also interact more frequently with their close friends F2F, compared to those using FB to communicate with "online strangers”. The results also indicate that FB has become an important tool for keeping in touch with family members and existing friends rather than forming new connections.
Describing the Web in less than 140 Characters
Authors: Raux Stéphane , Grünwald Nils , Prieur Christophe
Published: November 2022

Links curation, that is, finding relevant information within the World-Wide-Web and its ever-growing amount of content is a crucial problem for information access. Hyperlinks recommendation has been for a long time a common way to share references between web users, be it by e-mail exchanges, instant messages or forums. We explore in this paper how Social Media extend this recommendation practice by focusing on the citation of hyperlinks on Twitter. We investigate how people deal with the strong limitation of 140 characters per message, showing that this constraint encourages people to perform a good synthesis of the content they are linking to. We take advantage of this practice to efficiently cluster the actual content of the linked pages with an algorithm based on lexical proximities between messages. Our method yields topical clusters that are consistent with the dynamics of user interests with no need to extract text from the pages themselves.
Structure and Reciprocity in Technology-Centered Q&A Communities
Authors: Jiang Ming , Dong Tao , Chang Yung-Ju
Published: November 2022

In this paper we examine the network structure of the MythTV mailing list, an online technology Q&A user community, and we use time-series analysis techniques to study users’ reciprocity behavior in this community. We find that the amount of help users provide is strongly correlated to the amount of help they receive. Further, by conducting the Granger Causality test on the time series data of active users’ activity, we find that the amount of help given is actually the reason why one gets a lot of help. This finding corresponds to the concept of directed reciprocity in social networks and provides insights into social dynamics in technology-centered online communities.
Limits of Electoral Predictions Using Twitter
Authors: Gayo-Avello Daniel , Metaxas Panagiotis , Mustafaraj Eni
Published: November 2022

Using social media for political discourse is becoming common practice, especially around election time. One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press. Allegedly, predicting electoral outcomes from social media data can be feasible and even simple. Positive results have been reported, but without an analysis on what principle enables them. Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections. Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections. Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports. Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box. Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one.
Relevance Modeling for Microblog Summarization
Authors: Harabagiu Sanda , Hickl Andrew
Published: November 2022

This paper introduces a new type of summarization task, known as microblog summarization, which aims to synthesize content from multiple microblog posts on the same topic into a human-readable prose description of fixed length. Our approach leverages (1) a generative model which induces event structures from text and (2) a user behavior model which captures how users convey relevant content.
Supervised Topic Segmentation of Email Conversations
Authors: Joty Shafiq , Carenini Giuseppe , Murray Gabriel , Ng Raymond
Published: November 2022

We propose a graph-theoretic supervised topic segmentation model for email conversations which combines (i) lexical knowledge, (ii) conversational features, and (iii) topic features. We compare our results with the existing unsupervised models (i.e., LCSeg and LDA), and with their two extensions for email conversations (i.e., LCSeg+FQG and LDA+FQG) that not only use lexical information but also exploit finer conversation structure. Empirical evaluation shows that our supervised model is the best performer and achieves highest accuracy by combining the three different knowledge sources, where knowledge about the conversation has proved to be the most important indicator for segmenting emails.
Sensing Urban Social Geography Using Online Social Networking Data
Authors: Phithakkitnukoon Santi
Published: November 2022

Growing pool of public-generated bits like online social networking data provides possibility to sense social dynamics in the urban space. In this position paper, we use a location-based online social networking data to sense geo-social activity and analyze the underlying social activity distribution of three different cities: London, Paris, and New York. We find a non-linear distribution of social activity, which follows the Power Law decay function. We perform inter-urban analysis based on social activity distribution and clustering. We believe that our study sheds new light on context-aware urban computing and social sensing.
Marketing Ecosystem: The Dynamics of Twitter, TV Advertising, and Customer Acquisition
Authors: Yamamoto Hikaru , Matsumura Naohiro
Published: November 2022

Social media, especially the micro-blogging network Twitter, have gained much popularity among users and have thus attracted attention from firms. Social media can serve as advertising media and platforms of online word-of-mouth, because they enable consumers to share their consumption experiences with others easily. When firms advertise their products and services, they usually do not rely on one single medium. Instead, they combine various media to promote their products and services. Recently, they have started to include social media. The aim of this paper is to capture the dynamic relationships among TV advertising, tweets, offline word-of-mouth, and customer acquisition. We call these dynamics the “marketing ecosystem” and attempt to investigate this model. To achieve this goal, this paper employs a structural equation approach, which allows developing a more complete and accurate picture of the inter-relationships among constructs. We incorporate both the direct effects and the indirect effects of traditional marketing actions such as TV advertising, which in turn increase tweets and new customer acquisition. The results of our analysis show that TV advertising increases buzz on social media and offline word-of-mouth. The results also show that TV advertising, offline-word-of-mouth, and tweets have a strong impact on customer acquisition.
How Bad Do You Spell?: The Lexical Quality of Social Media
Authors: Baeza-Yates Ricardo , Rello Luz
Published: November 2022

In this study we present an analysis of the lexical quality of social media in the Web, focusing on the Web 2.0, social networks, blogs and micro-blogs, multimedia and opinions. We find that blogs and social networks are the main players and also the main contributors to the bad lexical quality of the Web. We also compare our results with the rest of the Web finding that in general social media has worse lexical quality than the average Web and that their quality is one order of magnitude worse than high quality sites.
Coping with the Document Frequency Bias in Sentiment Classification
Authors: Rafrafi Abdelhalim , Guigue Vincent , Gallinari Patrick
Published: November 2022

In this article, we study the polarity detection problem using linear supervised classifiers. We show the interest of penalizing the document frequencies in the regularization process to increase the accuracy. We propose a systematic comparison of different loss and regularization functions on this particular task using the Amazon dataset. Then, we evaluate our models according to three criteria: accuracy, sparsity and subjectivity. The subjectivity is measured by projecting our dictionary and optimized weight vector on the SentiWordNet lexicon. This original approach highlights a bias in the selection of the relevant terms during the regularization procedure: frequent terms are overweighted compared to their intrinsic subjectivities.We show that this bias appears whatever the chosen loss or regularization and on all datasets: it is closely link to the gradient descent technique. Penalizing the document frequency during the learning step enables us to improve significantly our performances. A lot of sentimental markers appear rarely and thus, are unappreciated by statistical learning algorithms. Explicitly boosting their influences leads to increasing the accuracy in the sentiment classification task.
Modeling Destructive Group Dynamics in On-Line Gaming Communities
Authors: Patil Akshay , Liu Juan , Price Bob , Sharara Hossam , Brdiczka Oliver
Published: November 2022

Social groups often exhibit a high degree of dynamism. Some groups thrive, while many others die over time. Modeling destructive dynamics and understanding whether/why/when a person will depart from a group can be important in a number of social domains. In this paper, we take the World of Warcraft game as an exemplar platform for studying destructive group dynamics. We build models to predict if and when an individual is going to quit his/her guild, and whether this quitting event will inflict substantial damage on the guild. Our predictors start from in-game census data and extract features from multiple perspectives such as individual-level, guild-level, game activity, and social interaction features. Our study shows that destructive group dynamics can often be predicted with modest to high accuracy, and feature diversity is critical to prediction performance.