Multi-Task Learning for Faux-Hate Detection in H indi- E nglish Code-Mixed Text (2024)

Abstract: The prevalence of harmful internet content is on the rise, especially among young people. Thismakes social media sites breeding grounds forhate speech and negativity even though theirpurpose is to create connections. The study pro-poses a multi-task learning model for the iden-tification and analysis of harmful social mediacontent. This classifies the text into fake/realand hate/non-hate categories and further identi-fies the target and severity of the harmful con-tent. The proposed model showed significantimprovements in performance with training ontransliterated data as compared to code-mixeddata. It ranked 2nd and 3rd in the ICON 2024Faux-Hate Shared Task and the performanceshave made it very effective against harmful con-tent.

## introduction with rising availability of the use of smartphones and cheaper internet, social media has become an added part of life. new technologies are gaining greater interest in the minds of the younger generation, coupled with a desire to connect with people from different walks of lives. social media offers several benefits, such as improved communication and opportunities for network building. however, it also has its drawbacks, particularly concerning privacy. misinformation as well as hateful narratives on social media have been prominent issues in the last decade. such narratives have become significant and broad-ranging regarding effects on society in times of crisis. hate comments are fueled by misinformation that may intensify hostility in communities. it may also have the potential to polarize groups, increase tensions, and create an atmosphere that leads to conflicts. most of the escalations driven by hate and fake narratives often tend to translate into verbal and physical violence that threatens the safety and cohesion of a community. it could cause sig-nificant psychological harm to targeted individuals, affecting the emotional toll that translates to long-term mental health issues resulting in anxiety, depression, and a sense of vulnerability. false information or fake news can mislead individuals during an emergency and distort public perception and opinion, leading to misguided beliefs and actions. the scale of hate speech experienced during the 2020-2021 covid-19 pandemic towards groups such as chinese people, asian communities, religions and other nationalities improperly blamed for the spread of the virus and social ills was unprecedented. adding onto that is also a direct consequence, the spread of fake news, manifested in a variety of conspiracy theories and more general misinformation was also closely linked to the perpetration of violence against these targeted groups. @xcite most of the previous research (biradar et al., 2024) viewed hate speech through a binary classification lens. this led to a risk of violating the use of freedom of speech by blocking all forms of possible hateful content in an unsystematic way. hence it is necessary that hateful and fake narratives are to be addressed in terms of both target and severity. a target comprises different individuals, groups, or any religion that might be affected by hateful or fake content. a severity measure refers to the level or intensity of damage that such content might cause @xcite , @xcite . as a contribution towards combating hateful content from social media and bringing about peace and harmony to society, we took part in the shared task decoding fake narratives in spreading hateful stories, called faux-hate, organized by icon 2024. this shared task proposed to develop a hardparameterized multi-task learning model to be able to effectively detect, classify, and analyze the fake narratives used in spreading hate while focusing on the target and severity dimensions. ## methodology this section describes the approach for identifying the faux-hate shared task. ## results and discussions this section presents the performance evaluation of the proposed multi-task model for both tasks. ## conclusion and future work in conclusion, we designed our hard parameterized multi-task learning model using xlm-r, mbert and hatebert as encoder models for the embedding layer, then we used a dropout layer with two fully connected dense layers, each connected to a relu function, which was then linked to the specific label classifiers which predicted the output with the help of a softmax function.