Toxicity, Morality, and Speech Act Guided Stance Detection (2023)

Abstract: In this work, we focus on the task of determining the public attitude toward various social issues discussed on social media platforms. Platforms such as Twitter, however, are often used to spread misinformation, fake news through polarizing views. Existing literature suggests that higher levels of toxicity prevalent in Twitter conversations often spread negativity and delay addressing issues. Further, the embedded moral values and speech acts specifying the intention of the tweet correlate with public opinions expressed on various topics. However, previous works, which mainly focus on stance detection, either ignore the speech act, toxic, and moral features of these tweets that can collectively help capture public opinion or lack an efficient architecture that can detect the attitudes across targets. Therefore, in our work, we focus on the main task of stance detection by exploiting the toxicity, morality, and speech act as auxiliary tasks. We propose a multitasking model TWISTED that initially extracts the valence, arousal, and dominance aspects hidden in the tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance by using the shared features of toxicity, morality, and speech acts present in the tweet. Extensive experiments conducted on 4 benchmark stance detection datasets (SemEval-2016, P-Stance, COVID19-Stance, and ClimateChange) comprising different domains demonstrate the effectiveness and generalizability of our approach.

## introduction social media platforms are often used to express public opinions and raise awareness about various issues and current problems in society @xcite @xcite . however, conversations on platforms like twitter can lead to users being exposed to false, misinformed messages, often resulting in polarizing beliefs and echo chambers @xcite . a recent american press article foot_0 claims that misinformation about climate is widespread on twitter and often delays appropriate climate action. in addition, a study conducted by mit researchers foot_1 showed that false news spread 6 times faster on twitter than truthful news. therefore, it is necessary to recognize the attitudes of the posts so as to curb the spread of misinformation. in our work, we perform stance detection of tweets to detect the opinion of the post, whether it is in favor, against, or none toward a target topic. there is a wealth of research on identifying attitudes in social media. several popular and benchmark stance datasets exist in the literature such as semeval-2016 @xcite , p-stance @xcite , covid-19 @xcite , and climatechange @xcite ) that focus on various social issues such as feminism, abortion, covid19, climate, and many current crises whose public opinions need to be identified in order to address these issues and change society for the better. we also use these 4 benchmark datasets in our study, which covers different domains, to get an assessment of generalizability of our approach. existing literature suggests that the tweet content consists of abusive and insulting statements, leading to the spread of hatred, bullying, and negativity in the public sphere, often disturbing social peace @xcite @xcite . moreover, the moral values in the tweets correlate with the different public opinions, which helps to understand the intentions behind each expressed attitude, which in turn helps to culturally integrate society and mitigate such differences @xcite . in the popular benchmark datasets, we also note that the speech act of the tweets, i.e., the form of expression, statement, question, or thought, also differs significantly across viewpoints on different topics (refer appendix b.2), which helps to determine the communicative intent of the viewpoint, further supporting the stance task. the following are few examples of tweets from public datasets: (ii)feminism (against):"@hiddentara no one wants to see feminists naked, so the petty, vindictive bit**** want to drag everyone else down to their level (toxic,harm,expression)"; (ii)biden (favor): "lets go joe #teamjoe #go-joe #biden2020 (non_toxic, care, loyalty, suggestion)". these relationships between toxic, moral, and speech act labels associated with the tweets' stances led us to use them to decipher their underlying attitudes. while several works @xcite @xcite focused on the linguistic properties of the input text for stance, they often lack an advanced architecture in the form of a better encoder trained exclusively on tweets, thus failing to extract the masked toxic and moral aspects from tweets in a multi-tasking form. other recent works such as memoclic @xcite , sp-amt @xcite have used sentiment, emotion, and toxicity aspects for stance detection, but these models focus primarily on climate change and often suffer from the drawbacks of detecting overall stance in the case of composite tweets with contrasting emotions or complexity of tweet content due to sarcasm. it has been proven that the valence, arousal, and dominance features help in analyzing emotional tone and its intensity @xcite @xcite , which eventually supports the detection of hidden intent or any sarcasm present in the tweet @xcite . therefore, our approach leverages these interdependencies to extract additional features of valence, arousal, and dominance (vad) along with insult, morality, and speech act to understand the psychology of the tweet and gain emotional insights. these features can then ultimately help identify the collective meaning and intent of the tweet, aiding in the detection of attitudes and thus addressing the drawbacks of the presence of sarcasm and implicit stance from previous works. the main contributions of our work are as follows: (i.) to the best of our knowledge, this is the first cross-sectional study to incorporate toxicity, morality, and speech act to determine the tweet's stance. this opens a new dimension in psychological and social science research to investigate in-depth the interdependencies between these toxic, moral, speech acts and public opinion. (ii.) we propose a multi-tasking system twisted that performs the main task of stance detection (sd) by using toxicity detection (td), morality classification (mc), and speech act classification (ac) as auxiliary tasks. our twisted model first extracts the vad features to capture the associated emotions and intentions of the tweet. emosense-injector then induces the emotional sense to the tweet by integrating the embedded text with the vad vectors. the emotionally enriched tweet is followed by an efficient attention framework that uses the task-specific and shared toxic, moral, and act features to identify the attitude of the tweet (favor/against/none). (iii.) extensive experiments are conducted on 4 benchmark stance detection datasets (semeval-2016, p-stance, cli-matechange, covid-19-stance) and the reported experimental results demonstrate the usefulness of our approach. the code and datasets with annotations are available here foot_2 . ## related works stance detection has been investigated in a number of studies. some of the existing works focus on exploring the impact and influence of polarizing attitudes @xcite , while others use the characteristics of tweet texts or networks to classify attitudes towards a target domain by developing machine and deep learning algorithms @xcite . since it is critical to understand public attitudes toward any pressing issue, our study also focuses on detecting the stance of a tweet text toward an urgent social issue. this can help technology companies and government agencies monitor public opinions and intervene to curb the spread of fake news, misinformation, or online hatred that can harm the peace. previous works have enriched the stance task with popular datasets covering controversial topics such as feminism, abortion, political leaders, covid-19, and the climate crisis @xcite . various existing studies have primarily focused on the semeval 2016 dataset @xcite while others have focused on political and covid domains @xcite . the climate change domain, which focuses on the climate crisis, has also been recently explored for stance task @xcite . however, in our study, we use these 4 benchmark datasets and investigate the performance of stance task that span a variety of contrasting domains with different embedding spaces of toxic, moral, and act features to understand the practicality of our approach. recently, research has explored multitasking and federated learning for various classification tasks such as hate speech, toxicity detection, and infrastructure damage identification, as the mtl paradigm aims to improve generalization performance by learning multiple related tasks at once @xcite @xcite @xcite . however, as far as we know, this is the first crosssectional study to utilize toxicity, morality, and speech act detection tasks in a multitask learning framework to identify the attitude of a tweet. in addition, the existing literature pointing to the correlation between toxic and moral aspects in relation to public opinions expressed on twitter motivated us to use these features as auxiliary tasks @xcite . different forms of speech act further help to decode the pragmatics of the author of the tweet and to identify the attitude encoded in the tweet @xcite . models such as esd @xcite and han @xcite exploit the linguistic characteristics of tweets by incorporating sentiments, stylistic, and stanceindicative features. however, these earlier studies did not use the better embedding techniques by using the bertweet encoder along with extracting vad features that provide the emotional tone and intensity that better identify different attitudes even when similar emotions or feelings are present. recent climate stance models use emotion and toxicity to identify stance @xcite but suffer from problems with the presence of contrasting emotions or sarcasm in the stances. however, our model addresses these problems by extracting the vad features along with the toxic and moral aspects, which together understand the overall intent of expressed public opinion and aid the stance task. 3 methodology problem definition given a tweet, we propose a stance detection approach that combines the tweet text with the extracted valence, arousal, and dominance features while taking advantage of toxic, moral, and speech act aspects associated with the tweet to efficiently detect the stance toward a target (favor/against/none). our proposed framework twisted comprises of the model components: bertweet-encoder, vad-embedder, emosense-injector, multiheadattention, and output layer. figure 1 represents the overview of our twisted approach. the tweet is passed in parallel to the bertweet encoder to generate the embedding of the tweet text, and to the vad embedder to extract the valence, arousal, and dominance features related to the tweet. the encoded text and the vad features are passed through the emosense-injector which is responsible for integrating the emotional sense in the tweet and obtaining the emotionally intelligent representation of the input tweet. this emotion-aware tweet is then followed by the multiheadattention layer to focus on the most relevant input features, followed by the task-specific dense and output layers to finally obtain the outputs of the main task of sd and the secondary tasks of td, mc, and ac. ## results please note that we report the results of the main task sd as we aim to improve the performance of the primary task by using other auxiliary tasks in our current study. ## conclusion in this work, we present the first cross-sectional study focusing on the problem of stance detection using the associated toxic, moral, and speech act traits present in tweets. our proposed approach is enriched with the emotional quotient of the tweet by extracting the vad features and further integrating them into the embedded tweet, thus evoking the emotional intent of the tweet. the experimental results on 4 benchmark datasets indicate that multitasking enhances the performance of the stance task by exploiting the efficient attention and encoder frameworks along with the auxiliary tasks. this demonstrates that the model is widely applicable in multiple domains, proving the generalizability of our proposed approach. in the future, we plan to include additional tasks such as entityrecognition and aspect-based sentiment detection, which could help disambiguate the attitudes and conflicting emotions in a tweet to predict a more accurate classification of polarized attitudes toward a target domain.