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Transactions on Computational Collective Intelligence XXI pp 97–138Cite as

Subject-Related Message Filtering in Social Media Through Context-Enriched Language Models

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Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9630))

Abstract

Efficiently retrieving and understanding messages from social media is challenging, considering that shorter messages are strongly dependent on context. Assuming that their audience is aware of background and real world events, users can shorten their messages without compromising communication. However, traditional data mining algorithms do not account for contextual information. We argue that exploiting context can lead to advancements in the analysis of social media messages. Recall rate increases if context is taken into account, leading to context-aware methods for filtering messages without resorting only to keywords. A novel approach for subject classification of social media messages, using computational linguistics techniques, is proposed, employing both textual and extra-textual (or contextual) information. Experimental analysis over sports-related messages indicates over 50 % improvement in retrieval rate over text-based approaches due to the use of contextual information.

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Notes

  1. 1.

    WhatsApp, according to http://www.statista.com/statistics/258743/daily-mobile-message-volume-of-whatsapp-messenger/.

  2. 2.

    Source: https://zephoria.com/social-media/top-15-valuable-facebook-statistics/.

  3. 3.

    By context we mean all non-written information that is relevant to understand a message, such as real world events and common sense knowledge.

  4. 4.

    More than 35 million tweets were issued during the Brazil vs Germany match in the FIFA World Cup 2014 semifinals, an event of global repercussion, the most tweeted event on record so far – but who knows how many tweets are not included in this figure due to lack of contextual information?.

  5. 5.

    An uninterrupted chain of spoken or written language.

  6. 6.

    For simplicity, fixed-length time windows are adopted.

  7. 7.

    http://mashable.com/2013/04/25/nestivity-engaged-brands/.

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Authors and Affiliations

  1. Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627, Belo Horizonte, MG, 31270-901, Brazil

    Alexandre Davis & Adriano Veloso

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  1. Alexandre Davis
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  2. Adriano Veloso
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Correspondence to Alexandre Davis .

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Editors and Affiliations

  1. Wrocław University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Ryszard Kowalczyk

  3. CISUC, Department of Informatics Engineering, Universidade de Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

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Davis, A., Veloso, A. (2016). Subject-Related Message Filtering in Social Media Through Context-Enriched Language Models. In: Nguyen, N.T., Kowalczyk, R., Rupino da Cunha, P. (eds) Transactions on Computational Collective Intelligence XXI. Lecture Notes in Computer Science(), vol 9630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49521-6_5

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  • DOI: https://doi.org/10.1007/978-3-662-49521-6_5

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