I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants

Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19-Humor. © 2023 ACM.

Identifying Hate Speech and Attribution of Responsibility: An Analysis of Simulated WhatsApp Conversations during the Pandemic.

BACKGROUND: During the COVID-19 pandemic, public confrontations between people who had agreed to be vaccinated and those who had not, highlighted the relevance of the deepening dissemination of violent and discriminatory expressions and determined a level of perception of hate discourses. METHOD: A cross-sectional observational study was carried out, based on an innovative methodology: simulations of WhatsApp conversations. In addition, the following variables were considered among others: level of empathy, personality traits and conflict resolution. RESULTS: The participants were 567 nursing students (413 females, 153 males and 1 person who did not identify with any gender). The results showed that, for the most part, the participants correctly identified hate speech, but were unable to discern the frame of reference. CONCLUSIONS: It is necessary to implement intervention strategies to minimize the impact of hate speech, which continues to be used on many levels to harass others, justify violence or undermine rights, generating an environment of prejudice and intolerance that encourages discrimination and violent attacks against certain individuals or collectives.

Predicting hate intensity of twitter conversation threads

Tweets are the most concise form of communication in online social media. Wherein a single tweet has the potential to make or break the discourse of the conversation. Online hate speech is more accessible than ever, and stifling its propagation is of utmost importance for social media companies and users for congenial communication. Most of the research has focused on classifying an individual tweet regardless of the tweet thread/context leading up to that point. One of the classical approaches to curb hate speech is to adopt a reactive strategy after the hateful content has been published. This strategy results in neglecting subtle posts that do not show the potential to instigate hate speech on their own but may portend in the subsequent discussion ensuing in the post's replies. In this paper, we propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future. Our model uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet. We explore three publicly available Twitter datasets – Anti-Racism contains the reply tweets of a collection of social media discourse on racist remarks during US political and COVID-19 background;Anti-Social presents a dataset of 40 million tweets amidst the COVID-19 pandemic on anti-social behaviours with custom annotations;and Anti-Asian presents Twitter datasets collated based on anti-Asian behaviours during COVID-19 pandemic. All the curated datasets consist of structural graph information of the Tweet threads. We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly. It beats the best baseline by an 11% margin on the Person correlation coefficient and a decrease of 25% on RMSE for the Anti-Racism dataset with a similar performance on the other two datasets.

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 Toxicity, Harassment, and Abuse in Online Social Spaces: A Workshop at CHI 2023

Online social spaces provide much needed connection and belonging - particularly in a context of continued lack of global mobility due to the ongoing Covid-19 pandemic and climate crisis. However, the norms of online social spaces can create environments in which toxic behaviour is normalized, tolerated or even celebrated. This can occur without consequence, leaving its members vulnerable to hate, harassment, and abuse. A vast majority of adults have experienced toxicity online and the harm is even more prevalent for members of marginalized and minoritized groups, who are more often the targets of online abuse. Although there is significant work on toxicity in the SIGCHI community, approaches and knowledge have typically been siloed by the domain of investigation (e.g., social media, multiplayer games, social VR). We argue that cross-disciplinary efforts will benefit not only the various communities and situations in which abuse occurs, but that bringing together researchers from different backgrounds and specialties will provide a robust and rich understanding of how to tackle online toxicity at scale. © 2023 Owner/Author.

A Critical Discourse Analysis of the Microstructure of Covid-19 Health-Protocol Advertisements in Tourist Facilities

To alleviate the impact of the Covid-19 pandemic on tourism, tourist facilities in Bali are informing visitors of the relevant health protocols, using posters to describe the appropriate behaviours. Using critical discourse analysis, this study examines the microstructure of the texts in these posters to identify their semantic, syntactic, lexical, and rhetorical elements. The study findings show that the semantic aspects consist of background, intention, and detail. The syntactic elements involve coherence and the use of the pronouns 'you' and 'we', and of the imperative, and the declarative. The lexical aspects include abbreviations and vocabulary, related to the health protocol. The textual messages are delivered in official language, supported by pictures and photographs.

From Yellow Peril to Model Minority: Asian stereotypes in social media during the COVID-19 pandemic

Heightened racial tensions during the COVID-19 pandemic contributed to the increase and rapid propagation of online hate speech towards Asians. In this work, we study the relationship between the racist narratives and conspiracy theories that emerged related to COVID-19 and historical stereotypes underpinning Asian hate and counter-hate speech on Twitter, in particular the Yellow Peril and model minority tropes. We find that the pandemic catalyzed a broad increase in discourse engaging with racist stereotypes extending beyond COVID-19 specifically. We also find that racist narratives and conspiracy theories which emerged during the pandemic and gained widespread attention were rooted in deeply-embedded Asian stereotypes. In alignment with theories of idea habitat and processing fluency, our work suggests that historical stereotypes provided an environment vulnerable to the racist narratives and conspiracy theories which emerged during the pandemic. Our work offers insight for ongoing and future anti-racist efforts. © 2023 ACM.

Hate Speech Dynamics Against African descent, Roma and LGBTQ plus Communities in Portugal

This paper introduces FIGHT, a dataset containing 63,450 tweets, posted before and after the official declaration of Covid-19 as a pandemic by online users in Portugal. This resource aims at contributing to the analysis of online hate speech targeting the most representative minorities in Portugal, namely the African descent and the Roma communities, and the LGBTQ+ community, the most commonly reported target of hate speech in social media at the European context. We present the methods for collecting the data, and provide insightful statistics on the distribution of tweets included in FIGHT, considering both the temporal and spatial dimensions. We also analyze the availability over time of tweets targeting the aforementioned communities, distinguishing public, private, and deleted tweets. We believe this study will contribute to better understand the dynamics of online hate speech in Portugal, particularly in adverse contexts, such as a pandemic outbreak, allowing the development of more informed and accurate hate speech resources for Portuguese.

Offensive and hateful communication or programmed disinformation?An analysis of the characteristics and discourse of the #ExposeBillGates conspiracy event

First, some electoral processes and then the COVID-19 crisis have brought offensive and dangerous disinformation events in social media into the spotlight. This research analyses an event concerning disinformation and the launch and dissemination of the hashtag #ExposeBillGates, through the 183,016 tweets that used this hashtag during its period of activity in June 2020. Through network analysis and by processing the content of the messages through text mining, it was observed that the size of the event was highly dependent on the participation of a small number of accounts, and some violent and abusive communication was found, although not hate speech. The need to deeply study the relations-hip between two macro communicative phenomena of a different nature, but more intertwined in their "problematic" origin than may appear, is discussed.

Hate Speech in a Telegram Conspiracy Channel During the First Year of the COVID-19 Pandemic.

Research has explored how the COVID-19 pandemic triggered a wave of conspiratorial thinking and online hate speech, but little is empirically known about how different phases of the pandemic are associated with hate speech against adversaries identified by online conspiracy communities. This study addresses this gap by combining observational methods with exploratory automated text analysis of content from an Italian-themed conspiracy channel on Telegram during the first year of the pandemic. We found that, before the first lockdown in early 2020, the primary target of hate was China, which was blamed for a new bioweapon. Yet over the course of 2020 and particularly after the beginning of the second lockdown, the primary targets became journalists and healthcare workers, who were blamed for exaggerating the threat of COVID-19. This study advances our understanding of the association between hate speech and a complex and protracted event like the COVID-19 pandemic, and it suggests that country-specific responses to the virus (e.g., lockdowns and re-openings) are associated with online hate speech against different adversaries depending on the social and political context.

Hate Speech and Discrimination as Mundane Violence against Rohingya Refugees during covid-19

This article advances the critical atrocity lens in challenging the dominant atrocity framework that overly emphasises systematic and large-scale killings in conflict settings. To do so, it argues for the broadened scope of violence to illustrate that hate speech and discrimination produce similar consequences of stripping vulnerable populations of their rights and livelihoods despite the absence of mass killings. This article captures such mundane violence by unpacking the interplay between atrocity crimes, hate speech and discrimination against Rohingya refugees during the covid-19 pandemic. The findings urge scholars and practitioners to consider broader human rights protection during peace time to address root causes of atrocities. In doing so, it can foster inter-communal respect and tolerance, hence preventing grievances from turning into incitement of mass violence. © 2023 Ruji Auethavornpipat.

Assessing the Impact of Contextual Information in Hate Speech Detection

Social networks and other digital media deal with huge amounts of user-generated contents where hate speech has become a problematic more and more relevant. A great effort has been made to develop automatic tools for its analysis and moderation, at least in its most threatening forms, such as in violent acts against people and groups protected by law. One limitation of current approaches to automatic hate speech detection is the lack of context. The spotlight on isolated messages, without considering any type of conversational context or even the topic being discussed, severely restricts the available information to determine whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task. We specifically study a subdomain of Twitter data consisting of replies to digital newspapers posts, which provides a natural environment for contextualized hate speech detection. We built a new corpus in Spanish (Rioplatense variant) focused on hate speech associated to the COVID-19 pandemic, annotated using guidelines carefully designed by our interdisciplinary team. Our classification experiments using state-of-the-art transformer-based machine learning techniques show evidence that adding contextual information improves the performance of hate speech detection for two proposed tasks: binary and multi-label prediction, increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results highlight the importance of using contextual information in hate speech detection. Our code, models, and corpus has been made available for further research. © 2013 IEEE.

Editorial Understanding COVID-19 Communication: Linguistic and Discursive Perspectives

This collaborative essay addresses COVID-19 communication, focussing on the linguistic strategies and discursive constructions that were adopted, first to cope with the unprecedented crisis scenarios of the pandemic and later to hail the post-pandemic times. It recapitulates the unfolding of COVID-19 communication from 2020 to 2022, espousing a linguistic and discursive perspective. To that purpose, it elaborates on a few keywords and key phrases that consistently identify the different pandemic and post-pandemic phases in the public domain. i.e. ‘recovery and resilience', ‘smart' and ‘virtual', and the ‘new normal', to finish with a few reflections on the challenges of legal communication faced with mounting social intolerance and the exacerbation of hate speech and xenophobia. The overview privileges the European Union and the UK, the latter launching the first mass vaccination campaign in December 2020, although with the awareness of the global nature of the phenomenon and its present repercussions. The aim of the essay is to frame the nine research articles in this issue as attempts to interpret an exceptionally difficult time span and as a form of intellectual resilience. Copyright (©) 2022 Maria Cristina Paganoni, Joanna Osiejewicz

HateNet: A Graph Convolutional Network Approach to Hate Speech Detection

The COVID-19 pandemic has caused hate speech on online social networks to become a growing issue in recent years, affecting millions. Our work aims to improve automatic hate speech detection to prevent escalation to hate crimes. The first c hallenge i n h ate s peech r esearch i s t hat e xisting datasets suffer from quite severe class imbalances. The second challenge is the sparsity of information in textual data. The third challenge is the difficulty i n b alancing t he t radeoff b etween utilizing semantic similarity and noisy network language. To combat these challenges, we establish a framework for automatic short text data augmentation by using a semi-supervised hybrid of Substitution Based Augmentation and Dynamic Query Expansion (DQE), which we refer to as SubDQE, to extract more data points from a specific c lass f rom T witter. W e a lso p ropose the HateNet model, which has two main components, a Graph Convolutional Network and a Weighted Drop-Edge. First, we propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings to provide new insights into how ideas are connected. Second, we propose a weighted Drop-Edge based stochastic regularization technique, which removes edges randomly based on weighted probabilities assigned by the semantic similarities between Tweets. Using 3 different SubDQE-augmented datasets, we compare our HateNet model using eight different tweet embedding methods, six other baseline classification models, and seven other baseline data augmentation techniques previously used in the realm of hate speech detection. Our results show that our proposed HateNet model matches or exceeds the performance of the baseline models, as indicated by the accuracy and F1 score. © 2022 IEEE.

THE NARRATIVES OF HATE USED BY ROMANIAN PARTIES LABELLED AS FAR-RIGHT. A COMPARATIVE NARRATIVE ANALYSIS

This article presents a qualitative comparative analysis of the primary hate narratives employed by three political parties: the Iron Guard Party propaganda, the Greater Romania Party (PRM), and the Alliance for the Union of Romanians (AUR). The study focuses on the following variables: 'foreigners,' 'freemasons,' 'Jews,' 'protection of Faith and Nation,' and 'rotten political elites.' The analysis is based on official propaganda materials of each party, including patriotic songs, leaflets, newspapers, programmatic documents, and speeches of the leader for the Iron Guard. In the case of the PRM, the analysis includes the party's official program, ideology, poems, and pamphlets by the leader (Corneliu Vadim Tudor), speeches, interviews, press articles, and extracts from party journals Romania Mare (Greater Romania) and Tricolorul. The AUR's official website, political program and ideology, Facebook posts, pages, press interviews, articles, and speeches of its leaders constitute the object of analysis. The narratives extracted were analyzed using the ATLAS.ti software, revealing striking resemblances among the hate narratives employed by the parties.

Corporate social responsibility and public diplomacy as formulas to reduce hate speech on social media in the fake news era

PurposeAnalyse the presence of hate speech in society, placing special emphasis on social media. In this sense, the authors strive to build a formula to moderate this type of content, in which platforms and public institutions cooperate, from the fields of corporate social responsibility and public diplomacy, respectively.Design/methodology/approachTo this aim, it is important to focus efforts on the creation of counter-narratives;the establishment of content moderation guidelines, which are not necessarily imposed by unilateral legislation;the promotion of suitable scenarios for the involvement of civil society;transparency on the part of social media companies;and supranational cooperation that is as transnational as possible. To exemplify the implementation of initiatives against hate speech, two cases are analysed that are paradigmatic for assuming two effective approaches to the formula indicated by the authors.FindingsThe authors analyse, in the case of the European Union, its "Code of conduct to counteract illegal online hate speech”, which included the involvement of different social media companies. And in the case of Canada, the authors discuss the implementation of the bill to include a definition of hate speech and the establishment of specific sanctions for this in the Canadian Human Rights Act and the Canadian Penal Code.Originality/valueThe case of the European Union was a way of seeking consensus with social media companies without legislation, while the case of Canada involved greater legislative and penalisation. Two ways of seeking the same goal: curbing hate speech.

Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques

Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users' textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language.

The negative health effects of anti-Asian stigma in the U.S. during COVID-19

This study provided a systemic review of the content of 50 behavioral and social science studies investigating enactment and outcomes of anti-Asian stigma related to coronavirus disease (COVID-19) published in the final quarter of 2020 and during 2021. Based on a systematic search of several databases in December of 2021, 500 studies describing the impact of COVID-related stigma on Asian Americans were identified. From this group, 50 studies meeting the inclusion criteria were analyzed focusing on health and social consequences of stigma. The studies were described by five stigma themes: the enactment of stigma, health consequences of stigma, stigma in the social media, Asian American stigma in education, and policy and political consequences of anti-Asian stigma. The studies appeared in a wide range of scholarly journals using several methodologies. While some studies exclusively focused on health impacts of stigma, all considered how Asian Americans have been scapegoated for COVID-19. Spread of blame and digital stigma on the social media has been particularly damaging to psychological well-being. Discussion of these studies provided an informative systemic overview for how scholars from various disciplines have investigated the antecedents and possible mechanisms leading to anti-Asian hate. This study serves as a baseline for other scholars who want to build on this body of research in future studies as Omicron and other potential future variants of COVID unfold. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Racism and resilience of pandemic proportions: Online harassment of asian americans during covid-19

ABSTRACT This study explores perceptions of online racial hate speech directed at Asian Americans in the United States during the COVID-19 pandemic. We examined how individuals' enactment of resilience communication in response to that threat affected their self-reported estimates of personal health. Using a nationally representative survey (n = 1767) that oversampled Asian Americans (n = 455), we found that Asian Americans perceived the problem of online hate speech to be more severe than members of non-targeted groups. Analysis revealed a mediated pathway through which heightened perceptions of online racial hate speech were positively associated with individuals' enactment of specific resilience processes tied to identity affirmation, which was linked to positive gains in psychological health. Results contribute to resilience theory in the context of racism and the observed relationships between resilience communication and health. We discuss how individuals in minoritized communities and allies might use resilience to combat the synergistic stressors of the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)