Conversational tagging in twitter

Huang, Jeff; Thornton, Katherine; Efthimiadis, Efthimis N.

doi:10.1145/1810617.1810647

Cited by 208 publications

(156 citation statements)

References 13 publications

(10 reference statements)

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“…Huang et al further characterize the temporal dynamics of hashtags via statistical measures such as standard deviation and discover that some hashtags are used widely for a few days but then disappear quickly [9]. Recent research on collaborative filtering showed that the consideration of such temporal dynamics impacts recommendation quality significantly [10].…”

Section: Related Workmentioning

confidence: 99%

Analyzing User Modeling on Twitter for Personalized News Recommendations

Abel

Gao

Houben

et al. 2011

Lecture Notes in Computer Science

254

220

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Abstract. How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.

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Section: Related Workmentioning

confidence: 99%

Analyzing User Modeling on Twitter for Personalized News Recommendations

Abel

Gao

Houben

et al. 2011

Lecture Notes in Computer Science

254

220

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“…Various models have been proposed for studying and predicting general information diffusion on Twitter based on a combination of message content, user profiles and tweeting timings [6], [7], [8]. Romero et al [9] and Huang et al [10] studied the diffusion of #hashtags on Twitter and investigated the factors behind the mass adoption of #hashtags and their subsequent dying off.…”

Section: Background and Related Workmentioning

confidence: 99%

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

Lim¹,

Datta²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users.

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“…Some use them as mass broadcast media for disasters or special events like elections. Hashtags are also used for brand promotion or micro-meme discussions [12]. Since hashtags are neither registered nor controlled by any user or group, it may be hard for some users to find appropriate hashtags for their tweets.…”

Section: Hashtag Recommendationmentioning

confidence: 99%

A Survey of Recommender Systems in Twitter

Kywe

Lim

Zhu

2012

Lecture Notes in Computer Science

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Abstract. Twitter is a social information network where short messages or tweets are shared among a large number of users through a very simple messaging mechanism. With a population of more than 100M users generating more than 300M tweets each day, Twitter users can be easily overwhelmed by the massive amount of information available and the huge number of people they can interact with. To overcome the above information overload problem, recommender systems can be introduced to help users make the appropriate selection. Researchers have began to study recommendation problems in Twitter but their works usually address individual recommendation tasks. There is so far no comprehensive survey for the realm of recommendation in Twitter to categorize the existing works as well as to identify areas that need to be further studied. The paper therefore aims to fill this gap by introducing a taxonomy of recommendation tasks in Twitter, and to use the taxonomy to describe the relevant works in recent years. The paper further presents the datasets and techniques used in these works. Finally, it proposes a few research directions for recommendation tasks in Twitter.

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Conversational tagging in twitter

Cited by 208 publications

References 13 publications

Analyzing User Modeling on Twitter for Personalized News Recommendations

Analyzing User Modeling on Twitter for Personalized News Recommendations

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

A Survey of Recommender Systems in Twitter

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