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.
Highlights
With the hasty increase in the use of multimedia and mobile phones, people are getting attached to gadgets like never before
This research resulted in findings on feeling restlessness in case they do not have access to their social media application messages, this gives rise to a syndrome called Phantom Vibration Syndrome (PVS)
Rothberg et al [4] researched that any person who imitates PVS behavior reflects obsession of frequent checking of social messages which portrays an indication of anxiety caused by mobile phones
Summary
With the hasty increase in the use of multimedia and mobile phones, people are getting attached to gadgets like never before. People get connected with their friends using social media. Social media connect people worldwide and helps in sharing information. This shared information is not kept secret usually and is further shared with the chain of friends. This sharing of information makes a chain and affects all the people included in the chain differently. It is found that approximately 63 percent of the users are affected by the words written by someone in their friend list. The proposed algorithm works on the count of retweets done for a particular tweet
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