As human beings utilize computing technologies to mediate multiple aspects of their lives, cyberbullying has grown as an important societal challenge. Cyberbullying may lead to deep psychiatric and emotional disorders for those affected. Hence, there is an urgent need to devise automated methods for cyberbullying detection and prevention. While recent cyberbullying detection efforts have defined sophisticated text processing methods for cyberbullying detection, there are as yet few efforts that leverage visual data processing to automatically detect cyberbullying. Based on early analysis of a public, labeled cyberbullying dataset, we report that visual features complement textual features in cyberbullying detection and can help improve predictive results.
The interaction of technology with humans has many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Many research studies have been conducted to tackle the detection and spreading of fake news, which is misinformation that looks genuine. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We propose a generalized method based on Deep Neural Networks to detect if a given claim is fake or genuine. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main submodules. The first submodule uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word‐level features for prediction. The second submodule uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives.
A common step in the processing of any text is the part-of-speech tagging of the input text. In this paper, we present an approach to tackle code-mixed text from three different languages Bengali, Hindi, and Tamilapart from English. Our system uses Conditional Random Field, a sequence learning method, which is useful to capture patterns of sequences containing code switching to tag each word with accurate part-of-speech information. We have used various pre-processing and post-processing modules to improve the performance of our system. The results were satisfactory, with a highest of 75.22% accuracy in Bengali-English mixed data. The methodology that we employed in the task can be used for any resource poor language. We adapted standard learning approaches that work well with scarce data. We have also ensured that the system is portable to different platforms and languages and can be deployed for real-time analysis.
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