Showing papers on "Microblogging published in 2010"

What is Twitter, a social network or a news media?

Abstract: Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing.We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet.To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

Abstract: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician Our results show that Twitter is indeed used extensively for political deliberation We find that the mere number of messages mentioning a party reflects the election result Moreover, joint mentions of two parties are in line with real world political ties and coalitions An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Abstract: Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previously proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.

TwitterRank: finding topic-sensitive influential twitterers

Abstract: This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter

Abstract: Twitter - a microblogging service that enables users to post messages ("tweets") of up to 140 characters - supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants retweet using different styles and for diverse reasons. We highlight how authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out retweeting as a conversational practice.

Microblogging during two natural hazards events: what twitter may contribute to situational awareness

Abstract: We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.

You are where you tweet: a content-based approach to geo-locating twitter users

Abstract: We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate The system estimates k possible locations for each user in descending order of confidence On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location

Short text classification in twitter to improve information filtering

Abstract: In microblogging services such as Twitter, the users may become overwhelmed by the raw data One solution to this problem is the classification of short text messages As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages

Characterizing Microblogs with Topic Models

Abstract: As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

Abstract: Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text. In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.

TWITTERING THE NEWS: The emergence of ambient journalism

Abstract: This paper examines new para-journalism forms such as micro-blogging as “awareness systems” that provide journalists with more complex ways of understanding and reporting on the subtleties of public communication. Traditional journalism defines fact as information and quotes from official sources, which have been identified as forming the vast majority of news and information content. This model of news is in flux, however, as new social media technologies such as Twitter facilitate the instant, online dissemination of short fragments of information from a variety of official and unofficial sources. This paper draws from computer science literature to suggest that these broad, asynchronous, lightweight and always-on systems are enabling citizens to maintain a mental model of news and events around them, giving rise to awareness systems that the paper describes as ambient journalism. The emergence of ambient journalism brought about by the use of these new digital delivery systems and evolving communicatio...

Twittering the News: The Emergence of Ambient Journalism

Abstract: This paper examines new para-journalism forms such as micro-blogging as "awareness systems" that provide journalists with more complex ways of understanding and reporting on the subtleties of public communication. Traditional journalism defines fact as information and quotes from official sources, which have been identified as forming the vast majority of news and information content. This model of news is in flux, however, as new social media technologies such as Twitter facilitate the instant, online dissemination of short fragments of information from a variety of official and unofficial sources. This paper draws from computer science literature to suggest that these broad, asynchronous, lightweight and always-on systems are enabling citizens to maintain a mental model of news and events around them, giving rise to awareness systems that the paper describes as ambient journalism. The emergence of ambient journalism brought about by the use of these new digital delivery systems and evolving communications protocols raises significant research questions for journalism scholars and professionals. This research offers an initial exploration of the impact of awareness systems on journalism norms and practices. It suggests that one of the future directions for journalism may be to develop approaches and systems that help the public negotiate and regulate the flow of awareness information, facilitating the collection and transmission of news.

Microblogs in Higher Education - A chance to facilitate informal and process-oriented learning?

Abstract: Microblogging is one of the latest Web 2.0 technologies. The key elements are online communication using 140 characters and the fact that it involves ''following'' anyone. There has been a great deal of excitement about this in recent months. This paper reports on a research study that was carried out on the use of a microblogging platform for process-oriented learning in Higher Education. Students of the University of Applied Sciences of Upper Austria used the tool throughout their course. All postings were carefully tracked, examined and analyzed in order to explore the possibilities offered by microblogging in education. It can be concluded that microblogging should be seen as a completely new form of communication that can support informal learning beyond classrooms.

Chatter on the red: what hazards threat reveals about the social life of microblogged information

Abstract: This paper considers a subset of the computer-mediated communication (CMC) that took place during the flooding of the Red River Valley in the US and Canada in March and April 2009. Focusing on the use of Twitter, a microblogging service, we identified mechanisms of information production, distribution, and organization. The Red River event resulted in a rapid generation of Twitter communications by numerous sources using a variety of communications forms, including autobiographical and mainstream media reporting, among other types. We examine the social life of microblogged information, identifying generative, synthetic, derivative and innovative properties that sustain the broader system of interaction. The landscape of Twitter is such that the production of new information is supported through derivative activities of directing, relaying, synthesizing, and redistributing, and is additionally complemented by socio-technical innovation. These activities comprise self-organization of information.

Twitter use by the U.S. Congress

Abstract: Twitter is a microblogging and social networking service with millions of members and growing at a tremendous rate. With the buzz surrounding the service have come claims of its ability to transform the way people interact and share information and calls for public figures to start using the service. In this study, we are interested in the type of content that legislators are posting to the service, particularly by members of the United States Congress. We read and analyzed the content of over 6,000 posts from all members of Congress using the site. Our analysis shows that Congresspeople are primarily using Twitter to disperse information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. These tend not to provide new insights into government or the legislative process or to improve transparency; rather, they are vehicles for self-promotion. However, Twitter is also facilitating direct communication between Congresspeople and citizens, though this is a less popular activity. We report on our findings and analysis and discuss other uses of Twitter for legislators. © 2010 Wiley Periodicals, Inc.

TweetMotif: Exploratory Search and Topic Summarization for Twitter

Abstract: We present TweetMotif, an exploratory search application for Twitter. Unlike traditional approaches to information retrieval, which present a simple list of messages, TweetMotif groups messages by frequent significant terms — a result set’s subtopics — which facilitate navigation and drilldown through a faceted search interface. The topic extraction system is based on syntactic filtering, language modeling, near-duplicate detection, and set cover heuristics. We have used TweetMotif to deflate rumors, uncover scams, summarize sentiment, and track political protests in real-time. A demo of TweetMotif, plus its source code, is available at http://tweetmotif.com.

Predicting the Speed, Scale, and Range of Information Diffusion in Twitter

Abstract: 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.

Scientometrics 2.0: New metrics of scholarly impact on the social Web

Abstract: The growing flood of scholarly literature is exposing the weaknesses of current, citation-based methods of evaluating and filtering articles. A novel and promising approach is to examine the use and citation of articles in a new forum: Web 2.0 services like social bookmarking and microblogging. Metrics based on this data could build a “Scientometics 2.0,” supporting richer and more timely pictures of articles' impact. This paper develops the most comprehensive list of these services to date, assessing the potential value and availability of data from each. We also suggest the next steps toward building and validating metrics drawn from the social Web.

Tweets and Trades: The Information Content of Stock Microblogs

Abstract: Microblogging forums have become a vibrant online platform to exchange trading ideas and other stock-related information. Using methods from computational linguistics, we analyze roughly 250,000 stock-related microblogging messages, so-called tweets, on a daily basis. We find the sentiment (i.e., bullishness) of tweets to be associated with abnormal stock returns and message volume to predict next-day trading volume. In addition, we analyze the mechanism leading to efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice in microblogging forums.

Conversational tagging in twitter

Abstract: Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

Abstract: Online social media are complementing and in some cases replac- ing person-to-person social interaction and redefining the diffu- sion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinforma- tion in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowd- sourced annotations.

Pass It On?: Retweeting in Mass Emergency

Abstract: We examine microblogged information generated during two different co-occurring natural hazards events in Spring 2009. Due to its rapid and widespread adoption, microblogging in emergency response is a place for serious consideration and experimentation for future application. Because microblogging is comprised of a set of practices shaped by a number of forces, it is important to measure and describe the diffuse, multi-party information exchange behaviors to anticipate how emergency governance might best play a role. Here we direct consideration toward information propagation properties in the Twitterverse, describing features of information redistribution related to the retweet (RT @) convention. Our analysis shows that during an emergency, for tweets authored by local users and tweets that contain emergency-related search terms, retweets are more likely than non-retweets to be about the event. We note that users are more likely to retweet information originally distributed through Twitter accounts run by media, especially the local media, and traditional service organizations. Comparing local users to the broader audience, we also find that tweet-based information redistribution is different for those who are local to an emergency event.

Outtweeting the twitterers - predicting information cascades in microblogs

Abstract: Microblogging sites are a unique and dynamic Web 2.0 communication medium. Understanding the information flow in these systems can not only provide better insights into the underlying sociology, but is also crucial for applications such as content ranking, recommendation and filtering, spam detection and viral marketing. In this paper, we characterize the propagation of URLs in the social network of Twitter, a popular microblogging site. We track 15 million URLs exchanged among 2.7 million users over a 300 hour period. Data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades and the communication dynamics. Based on these results we propose a propagation model that predicts which users are likely to mention which URLs. The model correctly accounts for more than half of the URL mentions in our data set, while maintaining a false positive rate lower than 15%.

Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

Abstract: Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.

How and why scholars cite on Twitter

Abstract: Scholars are increasingly using the microblogging service Twitter as a communication platform. Since citing is a central practice of scholarly communication, we investigated whether and how scholars cite on Twitter. We conducted interviews and harvested 46,515 tweets from a sample of 28 scholars and found that they do cite on Twitter, though often indirectly. Twitter citations are part of a fast-moving conversation that participants believe reflects scholarly impact. Twitter citation metrics could augment traditional citation analysis, supporting a "scientometrics 2.0."

Look who's talking - athletes on twitter: a case study.

Abstract: This case study investigated athletes’ use of a specific social-media platform—Twitter. Social media are a rising force in marketing and have been fully embraced by the sport industry, with teams, leagues, coaches, athletes, and managers establishing presences. Primarily these presences have been focused on Twitter, a microblogging site that allows users to post their personal thoughts in 140 characters or less. Athletes, in particular, have engaged in tweeting at a fast pace, which raises the question, What are they saying? This case study investigated the tweets of athletes over a 7-d period in an attempt to answer that question. The findings indicate that athletes are talking predominantly about their personal lives and responding to fans’ queries through Twitter. The results indicate that Twitter is a powerful tool for increasing fan–athlete interaction.

Understanding retweeting behaviors in social networks

Abstract: Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.

A case study of micro-blogging in the enterprise: use, value, and related issues

Abstract: This is a case study about the early adoption and use of micro-blogging in a Fortune 500 company. The study used several independent data sources: five months of empirical micro-blogging data, user demographic information from corporate HR records, a web based survey, and targeted interviews. The results revealed that users vary in their posting activities, reading behaviors, and perceived benefits. The analysis also identified barriers to adoption, such as the noise-to-value ratio paradoxes. The findings can help both practitioners and scholars build an initial understanding of how knowledge workers are likely to use micro-blogging in the enterprise.

Crowd-sourced sensing and collaboration using twitter

Abstract: Despite the availability of the sensor and smart-phone devices to fulfill the ubiquitous computing vision, the-state-of-the-art falls short of this vision. We argue that the reason for this gap is the lack of an infrastructure to task/utilize these devices for collaboration. We propose that microblogging services like Twitter can provide an "open" publish-subscribe infrastructure for sensors and smartphones, and pave the way for ubiquitous crowd-sourced sensing and collaboration applications. We design and implement a crowd-sourced sensing and collaboration system over Twitter, and showcase our system in the context of two applications: a crowd-sourced weather radar, and a participatory noise-mapping application. Our results from real-world Twitter experiments give insights into the feasibility of this approach and outline the research challenges in sensor/smartphone integration to Twitter.

Summarizing Microblogs Automatically

Abstract: In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial evaluation.