2,852 research outputs found

    Preprocessing Techniques to Support Event Detection Data Fusion on Social Media Data

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    This thesis focuses on collection and preprocessing of streaming social media feeds for metadata as well as the visual and textual information. Today, news media has been the main source of immediate news events, large and small. However, the information conveyed on these news sources is delayed due to the lack of proximity and general knowledge of the event. Such news have started relying on social media sources for initial knowledge of these events. Previous works focused on captured textual data from social media as a data source to detect events. This preprocessing framework postures to facilitate the data fusion of images and text for event detection. Results from the preprocessing techniques explained in this work show the textual and visual data collected are able to be proceeded into a workable format for further processing. Moreover, the textual and visual data collected are transformed into bag-of-words vectors for future data fusion and event detection

    Seminar Users in the Arabic Twitter Sphere

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    We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

    Fake news identification on Twitter with hybrid CNN and RNN models

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    The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain
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