Accelerating automatic hate speech detection using parallelized ensemble learning models

With increasing number of social media users and online engagement, it is essential to study hate speech propagation on social media platforms (SMPs). Automatic hate speech detection on social media is of utmost importance as hate speech can create discomfort among users and potentially generate a strong reaction in society. Ensemble learning algorithms are helpful in addressing sentiment-based classification due to their fault tolerance and efficiency. However, a simple, scalable, and robust framework is required to deal with large-scale data efficiently and accurately. Therefore, we propose parallelization to the standard ensemble learning algorithms to speed up the automatic hate speech detection on SMPs. In this study, we parallelize bagging, A-stacking, and random sub-space algorithms and test their serial and parallel versions on the standard high-dimensional datasets for hate speech detection. The experiments are performed using six datasets that address hate speech propagation during events like the COVID-19 pandemic, the US presidential election (2020), and the farmers' protest in India (2021). Our parallel models observe a significant speedup with high efficiency, claiming that the proposed models are suitable for the considered application. Also, one of the main motivations of this study is to highlight the importance of generalization by testing the models under the cross-dataset environment. We observed that the accuracy is not affected while parallelizing the algorithms compared with serial algorithms executing on a single machine.

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.