Faux Hate: unravelling the web of fake narratives in spreading hateful stories: a multi-label and multi-class dataset in cross-lingual Hindi-English code-mixed text

Social media has undeniably transformed the way people communicate; however, it also comes with unquestionable drawbacks, notably the proliferation of fake and hateful comments. Recent observations have indicated that these two issues often coexist, with discussions on hate topics frequently being dominated by the fake. Therefore, it has become imperative to explore the role of fake narratives in the dissemination of hate in contemporary times. In this direction, the proposed article introduces a novel data set known as the Faux Hate Multi-Label Data set (FHMLD) comprising 8014 fake-instigated hateful comments in Hindi-English code-mixed text. To the best of our knowledge, this marks the first endeavour to bring together both fake and hateful content within a unified framework. Further, the proposed data set is collected from diverse platforms such as YouTube and Twitter to mitigate user-associated bias. To investigate a relation between the presence of fake narratives and its impact on the intensity of the hate, this study presents a statistical analysis using the Chi-square test. The statistical findings indicate that the calculated \(\chi ^2\) ${\chi }^2$ value is greater than the value from the standard table, leading to the rejection of the null hypothesis. Additionally, the current study present baseline methods for categorizing multi-class and multi-label data set, utilizing syntactical and semantic features at both word and sentence levels. The experimental results demonstrate that the fastText and SVM based method outperforms others models with an accuracy of 71% and 58% for binary fake–hate and severity prediction respectively.