TweetIT- Analyzing Topics for Twitter Users to garner Maximum Attention
Twitter, a microblogging service, is todays most popular platform for communication in the form of short text messages, called Tweets. Users use Twitter to publish their content either for expressing concerns on information news or views on daily conversations. When this expression emerges, they are...
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Zusammenfassung: | Twitter, a microblogging service, is todays most popular platform for
communication in the form of short text messages, called Tweets. Users use
Twitter to publish their content either for expressing concerns on information
news or views on daily conversations. When this expression emerges, they are
experienced by the worldwide distribution network of users and not only by the
interlocutor(s). Depending upon the impact of the tweet in the form of the
likes, retweets and percentage of followers increases for the user considering
a window of time frame, we compute attention factor for each tweet for the
selected user profiles. This factor is used to select the top 1000 Tweets, from
each user profile, to form a document. Topic modelling is then applied to this
document to determine the intent of the user behind the Tweets. After topics
are modelled, the similarity is determined between the BBC news data-set
containing the modelled topic, and the user document under evaluation. Finally,
we determine the top words for a user which would enable us to find the topics
which garnered attention and has been posted recently. The experiment is
performed using more than 1.1M Tweets from around 500 Twitter profiles spanning
Politics, Entertainment, Sports etc. and hundreds of BBC news articles. The
results show that our analysis is efficient enough to enable us to find the
topics which would act as a suggestion for users to get higher popularity
rating for the user in the future. |
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DOI: | 10.48550/arxiv.1711.10002 |