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Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for...
Many threats in the real world can be related to activities in public sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these sources. Therefore, it is important to have a system that...
This article examines how different social media platforms affect opinion composition and evolution. We differentiate between product and non-product oriented outlets as they differ in the salience of social cues, thus resulting in distinct user behaviours. We extend prior research in several ways. First, comparing between comments from different types of social media platforms, we show that the product...
Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture...
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