Adversaries and anti-social elements have exploited the rapid proliferation of computing technology and online social media in the form of novel security threats, such as fake profiles, hate speech, social bots, and rumors. The hate speech problem on online social networks (OSNs) is also widespread. The existing literature has machine learning approaches for hate speech detection on OSNs. However, the effectiveness of contextual information at different orientations is understudied. This study presents a novel Convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech. The proposed model is evaluated over two Twitter-based benchmark datasets -DS1(balanced) and DS2(unbalanced) with the best performance of 0.90, 0.80, and 0.84 respectively considering precision, recall, and f-score over unbalanced dataset. In terms of training and validation accuracy, the proposed model shows the best performance of 0.93 and 0.90, respectively, over the unbalanced dataset. In comparative evaluation, HCovBi-Caps demonstrates a significantly better performance than state-of-the-art approaches. In addition, HCovBi-Caps shows comparatively better performance over the unbalanced dataset. We also investigate the impact of different hyperparameters on the efficacy of HCovBi-Caps to ascertain the selection of their values. We observed that a higher value of routing iterations adversely affects the model performance, whereas a higher value of capsule dimension improves the performance.

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Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like DBpedia, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual features in recommender systems improves rating prediction accuracy. However, identification of contextual features for building context-aware recommender systems is a major bottleneck. To this end, in this paper, we present the development of a context-based recommender system, CRecSys, for item ratings prediction in movie domain. CRecSys extracts item-based contextual features from the underlying dataset and generates an RDF graph to model items and their contextual features for computing context-based items similarity using graph matching techniques and item-based collaborative filtering. It uses LOD and two well-known movie data sources-Rotten Tomatoes and IMDB for item profiling using a dataset of 1300 movies. CRecSys is experimentally evaluated over two movie datasets, one is generated by the authors and second is the MovieLens-1M benchmark dataset. CRecSys is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that CRecSys is able to effectively deal with some of the open challenges like cold-start and limited content problems of the traditional recommender systems.

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