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Irrelevant Racist Tweets Identification Using Data Mining Techniques

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Computer Networks and Inventive Communication Technologies

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 75))

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Abstract

In recent times, Twitter is one of the major sources to access information. Its feature of the hashtag is something that grabs more attention from the users. One can write one’s mind and heart out on Twitter at any given minute. Due to which there is a rapid increase in the generation of irrelevant content on Twitter. Lately, a new hashtag called “#whitelivesmatter” was used as a counter for another hashtag “#blacklivesmatter”. A lot of anti-government protests and various other violent activities were conducted, recorded, and posted on Twitter with this hashtag. A lot of Kpop fans had taken over this hashtag and flooded Twitter with extremely irrelevant content. Due to which the main and important content of the protests was drowned in these irrelevant tweets, which made it extremely hard for the officials to find and reinforce the law and order. This paper aims at building a model that helps in finding the relevance of text content in the tweet and its hashtag #whitelivesmatter in specific. In this paper, supervised data analysis techniques like text classification are used to get the required output.

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Author information

Authors and Affiliations

  1. Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

    Jyothirlatha Kodali, Vyshnavi Kandikatla, Princy Nagati, Veena Nerendla & M. Sreedevi

Authors
  1. Jyothirlatha Kodali
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  2. Vyshnavi Kandikatla
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  3. Princy Nagati
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  4. Veena Nerendla
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  5. M. Sreedevi
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Corresponding author

Correspondence to M. Sreedevi .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Praha, Czech Republic

    Robert Bestak

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Faculty of Informatics and Information Technology, Slovak University Technology, Bratislava, Slovakia

    Ivan Kotuliak

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Kodali, J., Kandikatla, V., Nagati, P., Nerendla, V., Sreedevi, M. (2022). Irrelevant Racist Tweets Identification Using Data Mining Techniques. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_15

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  • DOI: https://doi.org/10.1007/978-981-16-3728-5_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3727-8

  • Online ISBN: 978-981-16-3728-5

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