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2011 11th IEEE International Conference on Data Mining Workshops Twitter Trending Topic Classification
"... Abstract—With the increasing popularity of microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated every day. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is ..."
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Abstract—With the increasing popularity of microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated every day. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time
What is Twitter, a Social Network or a News Media?
"... 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 charac ..."
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Cited by 991 (12 self)
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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
Real-time classification of twitter trends
- Journal of the Association for Information Science and Technology (JASIST
, 2014
"... Abstract The community of users participating in social media tends to share about common interests at the same time, giving rise to what are known as social trends. A social trend reflects the voice of a large number of users which, for some reason, becomes popular in a specific moment. Through so ..."
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Cited by 2 (0 self)
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social trends, users therefore suggest that some occurrence of wide interest is taking place and subsequently triggering the trend. In this work, we explore the types of triggers that spark trends on the microblogging site Twitter, and introduce a typology that includes the following four types: news
Study of trend-stuffing on twitter through text classification
- In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS
, 2010
"... Twitter has become an important mechanism for users to keep up with friends as well as the latest popular topics, reaching over 20 million unique visitors monthly and generating over 1.2 billion tweets a month. To make popular topics easily accessible, Twitter lists the current most tweeted topics o ..."
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Cited by 8 (1 self)
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to such topics – a practice we call trend-stuffing. We study the use of text-classification over 600 trends consisting of 1.3 million tweets and their associated web pages to identify tweets that are closely-related to a trend as well as unrelated tweets. Using Information Gain, we reduce the original set
Twittermonitor: Trend detection over the twitter stream
- In Proceedings of the International Conference on Management of Data (SIGMOD’10
"... We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. ‘trends’) on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering ..."
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Cited by 130 (0 self)
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We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. ‘trends’) on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system
Empirical Study of Topic Modeling in Twitter
- PROCEEDINGS OF THE SIGKDD WORKSHOP ON SOCIAL MEDIA ANALYTICS (SOMA)
, 2010
"... Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes im ..."
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Cited by 78 (1 self)
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of carefully designed experiments from both qualitative and quantitative perspectives. We show that by training a topic model on aggregated messages we can obtain a higher quality of learned model which results in significantly better performance in two realworld classification problems. We also discuss how
Beyond Trending Topics: Real-World Event Identification on Twitter
"... User-contributed messages on social media sites such as Twitter have emerged as powerful, 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 ..."
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Cited by 97 (3 self)
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. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events and non-event messages. Our approach relies on a rich family of aggregate statistics of topically similar message clusters. Large-scale experiments over millions of Twitter
Information Credibility on Twitter
"... We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this pap ..."
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Cited by 125 (5 self)
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. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to “trending ” topics, and classify them as credible or not credible, based on features extracted from them. We use features from users ’ posting and re
Who Says What to Whom on Twitter
, 2011
"... We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as “lists ” to distinguish between elite ..."
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Cited by 136 (7 self)
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users—by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations—and ordinary users. Based on this classification, we find a striking concentration of attention on Twitter, in that roughly 50 % of URLs consumed are generated by just 20K elite users, where
Detecting spammers on twitter
- In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS
, 2010
"... With millions of users tweeting around the world, real time search systems and different types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. However, although appealing as mechanisms to ease the spread of news and allow users to discuss events ..."
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Cited by 110 (5 self)
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and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post tweets containing typical words of a trending topic and URLs
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