2,902 research outputs found
Sentiment Analysis for Troll Activity Detection on Sina Weibo
The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter and Facebook. In China, the micro-blogging service provider Sina Weibo is the most popular such service. To overcome negative publicity, Weibo trolls the so called Water Army can be hired to post deceptive comments.
In recent years, troll detection and sentiment analysis have been studied, but we are not aware of any research that considers troll detection based on sentiment analysis. In this research, we focus on troll detection via sentiment analysis with other user activity data gathered on the Sina Weibo platform, where the content is mainly in Chinese. We implement techniques for Chinese sentence segmentation, word embeddings, and sentiment score calculations. We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies. Experimental results are generated, analyzed and the troll detection model we proposed achieved 89% accuracy for the dataset presented in this research. A Chrome extension is presented that implements our proposed technique, which enables real-time troll detection and troll comments filtering when a user browses Sina Weibo tweets and comments
Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo
Recent decades have witnessed online social media being a big-data window for
quantificationally testifying conventional social theories and exploring much
detailed human behavioral patterns. In this paper, by tracing the emoticon use
in Weibo, a group of hidden "ambivalent users" are disclosed for frequently
posting ambivalent tweets containing both positive and negative emotions.
Further investigation reveals that this ambivalent expression could be a novel
indicator of many unusual social behaviors. For instance, ambivalent users with
the female as the majority like to make a sound in midnights or at weekends.
They mention their close friends frequently in ambivalent tweets, which attract
more replies and thus serve as a more private communication way. Ambivalent
users also respond differently to public affairs from others and demonstrate
more interests in entertainment and sports events. Moreover, the sentiment
shift of words adopted in ambivalent tweets is more evident than usual and
exhibits a clear "negative to positive" pattern. The above observations, though
being promiscuous seemingly, actually point to the self regulation of negative
mood in Weibo, which could find its base from the emotion management theories
in sociology but makes an interesting extension to the online environment.
Finally, as an interesting corollary, ambivalent users are found connected with
compulsive buyers and turn out to be perfect targets for online marketing.Comment: Data sets can be downloaded freely from www.datatang.com/data/47207
or http://pan.baidu.com/s/1mg67cbm. Any issues feel free to contact
[email protected]
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