By Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker
This quantity offers a suite of conscientiously chosen contributions within the quarter of social media research.
Each bankruptcy opens up a few examine instructions that experience the aptitude to be taken on additional during this speedily becoming zone of study.
The chapters are assorted sufficient to serve a couple of instructions of analysis with Sentiment research because the dominant subject within the book.
The authors have supplied a large variety of study achievements from multimodal sentiment id to emotion detection in a chinese language microblogging website.
The e-book should be important to analyze scholars, teachers and practitioners within the zone of social media research.
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Additional resources for Advances in Social Media Analysis
A distinct but related approach is to identify reliable “reporters” of events, such as people watching a football match who also provide regular, accurate tweets about it . In common with several event-detection approaches [42, 43], they rely on spikes in the overall activity of message streams to identify events, unlike our work which only needs the frequency of terms to shift within a (potentially) unchanging volume of messages. Although not strictly related to topic detection, we have also analysed tweets sent by fans of different teams during English Premier League matches.
No such clarity is in the politics graphs, which instead is best viewed as many overlapping trends. This difference is reflected in the way that major news stories often emerge: an initial single, focussed story emerges but is later replaced with several potentially overlapping sub-stories covering different aspects of the story. Our results suggest that a dynamic approach may be required for newsworthy topic detection, finding an initial clear burst and subsequently seeking more subtle and overlapping topics.
2]. 1 The three data sets differ in the rates of tweets, determined by the popularity of the topic and the choice of filter keywords. The mean tweets per minute (tpm) were: Super Tuesday, 832 tpm; FA Cup, 1293 tpm; and US elections, 2209 tpm. eu/results/datasets/72-twitter-tdt-dataset. 30 C. Martin et al. 41 s respectively. This means that a user interface displaying these topics could be updated every 1–2 min to show the current top-10 (or top-m) stories. To generate the sets of Tweets used in the evaluation, we crawled Twitter during the events using appropriate sets of filter keywords, such as the names of the participants.
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