Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19.

Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful content. Automatic hate speech detection on social media platforms is an essential task that has not been solved efficiently despite multiple attempts by various researchers. It is a challenging task that involves identifying hateful content from social media posts. These posts may reveal hate outrageously, or they may be subjective to the user or a community. Relying on manual inspection delays the process, and the hateful content may remain available online for a long time. The current state-of-the-art methods for tackling hate speech perform well when tested on the same dataset but fail miserably on cross-datasets. Therefore, we propose an ensemble learning-based adaptive model for automatic hate speech detection, improving the cross-dataset generalization. The proposed expert model for hate speech detection works towards overcoming the strong user-bias present in the available annotated datasets. We conduct our experiments under various experimental setups and demonstrate the proposed model's efficacy on the latest issues such as COVID-19 and US presidential elections. In particular, the loss in performance observed under cross-dataset evaluation is the least among all the models. Also, while restricting the maximum number of tweets per user, we incur no drop in performance.

Modeling coherence by ordering paragraphs using pointer networks.

Coherence is a distinctive feature in well-written documents. One method to study coherence is to analyze how sentences are ordered in a document. In Multi-document Summarization, sentences from different sources need to be ordered. Cluster-based ordering algorithms aim to study various themes or topics that are present in a set of sentences. After the clusters of sentences have been identified, sentences are ordered within each cluster in isolation. One challenge that remains is to order these clusters or paragraphs to obtain a coherent ordering of information. Inspired by the success of deep neural networks in several NLP tasks, we propose an RNN-based encoder-decoder system to predict order for a given set of loose clusters or paragraphs. Universal Sentence Encoder (USE) is used to encode paragraphs into high dimensional embeddings, which are then fed into an LSTM encoder and consecutively passed to a pointer network, which finally outputs the paragraph order. Since Wikipedia is a source of well- structured articles, it is used to generate multiple datasets. Based on our experimental results, the proposed model satisfactorily outperforms the baseline model across multiple datasets. We observe a two-fold increase in Kendall's tau values for the final paragraph orderings.