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Classifying trending topics: a typology of conversation triggers on Twitter

Authors:

Arkaitz Zubiaga,

Damiano Spina,

Víctor Fresno,

Raquel MartínezAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2461 - 2464

https://doi.org/10.1145/2063576.2063992

Published: 24 October 2011 Publication History

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Abstract

Twitter summarizes the great deal of messages posted by users in the form of trending topics that reflect the top conversations being discussed at a given moment. These trending topics tend to be connected to current affairs. Different happenings can give rise to the emergence of these trending topics. For instance, a sports event broadcasted on TV, or a viral meme introduced by a community of users. Detecting the type of origin can facilitate information filtering, enhance real-time data processing, and improve user experience. In this paper, we introduce a typology to categorize the triggers that leverage trending topics: news, current events, memes, and commemoratives. We define a set of straightforward language-independent features that rely on the social spread of the trends to discriminate among those types of trending topics. Our method provides an efficient way to immediately and accurately categorize trending topics without need of external data, outperforming a content-based approach.

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Classifying trending topics: a typology of conversation triggers on Twitter

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CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 October 2011

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October 24 - 28, 2011

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https://doi.org/10.23919/JSC.2023.0001
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https://doi.org/10.1109/ICCITM56309.2022.10031932
Kumar SPanwar SSingh JSharma ANisha Z(2022)iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic processInternational Journal of System Assurance Engineering and Management10.1007/s13198-022-01666-6Online publication date: 12-Jun-2022
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Analysis and forecasting of trending topics in online media streams

Are trending topics useful for marketing?: visibility of trending topics vs traditional advertisement

Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams

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