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SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services ; CU-CS-1077-11
Tweet Acts: A Speech Act Classifier for Twitter
Speech acts are a way to conceptualize speech as action. This holds true for
communication on any platform, including social media platforms such as
Twitter. In this paper, we explored speech act recognition on Twitter by
treating it as a multi-class classification problem. We created a taxonomy of
six speech acts for Twitter and proposed a set of semantic and syntactic
features. We trained and tested a logistic regression classifier using a data
set of manually labelled tweets. Our method achieved a state-of-the-art
performance with an average F1 score of more than . We also explored
classifiers with three different granularities (Twitter-wide, type-specific and
topic-specific) in order to find the right balance between generalization and
overfitting for our task.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th
AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media
When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of
statistical and non-semantic deep learning models
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