Impact of Swear and Negative texts on Social Media Users 
Srishty Jindal1, S.V.A.V. Prasad2, Kamlesh Sharma3
1Srishty Jindal, Research Scholar, Lingaya’s Vidyapeeth, Assistant Professor, CSE, FET, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.
2Dr. S.V.A.V. Prasad, Prof., EEE Dept., Lingaya’s Vidyapeeth, Faridabad, Haryana, India.
3Dr. Kamlesh Sharma, Associate Professor, CSE, FET, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.
Manuscript received on 2 October 2021 | Revised Manuscript received on 19 October 2021 | Manuscript Accepted on 15 November 2021 | Manuscript published on 30 November 2021 | PP: 14-19 | Volume-1 Issue-2 November 2021 | Retrieval Number: 100.1/ijdm.B1614111221 | DOI:10.54105/ijdm.B1614.111221
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, the use of social media has increased exponentially. People show different behavior on social media depending on the kind of responses and behavior of people around them. It is important now to analyze the behavior of social media users and the way how they affect their friends. In this paper, behavioral analysis of people is done based on Twitter data. An algorithm is proposed which helps in finding the impact of text written by someone on social media and its effect on others. The impact of written text is calculated with the help of the number of retweets done for the same tweet. The severity of the used word is calculated based on AFINN dictionary. According to the proposed algorithm, the score of the dictionary is recalculated when a negative word is forwarded multiple times. This is done with the understanding that if a less severe negative word is used many times, it may affect the person in a highly negative manner. With this, Severity of words is recalculated and its impact on people is found with the help of the proposed algorithm. The impact of using negative words on social media affect 32 % of the total users (in their friend-list). Behavior change is demonstrated with the help of graphs week-wise, month-wise and year-wise analyses. The research helps in finding the impact of swear words on social media users depending on the frequency and severity score of the words.
Keywords: Social Media, Negative Text, Tweets, Behavioral Analysis
Scope: Data Mining