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Online interaction behavior between web users often makes some events go viral. Popularity prediction of events is a key task in many security related applications. It forecasts how widely events would spread based on the information of evolution at an early stage. Existing methods either rely on careful feature engineering, or solely consider time series, ignoring rich information of user and text...
Public sentiment permeated through social media is usually regarded as an important measure for public opinion monitoring, policy making, and so forth. However, the deluge of user-generated content in web, especially in social platform, causes great challenge to public sentiment analysis tasks. Therefore, Web-derived Emotional Word Detection (WEWD) is proposed as a fundamental tool aims to alleviate...
The ever-growing number of users makes social media a valuable information source about recent events. Event detection and tracking plays an important role in decision-making and public management. Despite recent progress, the performance of event detection and tracking is still limited. The majority of existing work lacks an effective way to judge whether a text related to a certain event, due to...
Public sentiment is regarded as an important measure for event detection, information security, policy making etc. Analyzing public sentiments relies more and more on large amount of multimodal contents, in contrast to the traditional text-based and image-based sentiment analysis. However, most previous works directly extract feature from image as the additional information for text modality and then...
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