Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms.
Ontology highlight
ABSTRACT:
SUBMITTER: Velasquez N
PROVIDER: S-EPMC8206165 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In this article, we present the findings of a comprehensive longitudinal social network analysis conducted on Twitter across four consecutive election campaigns in Spain, spanning from 2015 to 2019. Our focus is on the discernible trend of increasing partisan and ideological homogeneity within interpersonal exchanges on this social media platform, alongside high levels of networking efficiency measured through average retweeting. This diachronic study allows us to observe how dynamics of party competition might contribute to perpetuating and strengthening network ideological and partisan homophily, creating 'epistemic bubbles' in Twitter, yet showing a greater resistance to transforming them into 'partisan echo-chambers.' Specifically, our analysis reveals that the rise of a new radical right-wing party (RRP), Vox, has heightened ideological homogeneity among users across the entire ideological spectrum. However, this process has not been uniform. While users aligned with mainstream political parties consistently share content that reinforces in-party affinity, resulting in highly efficient 'epistemic bubbles,' the emergence of the RRP has given rise to a distinct group of users associated with the most extreme partisan positions, characterized by a notable proportion of out-partisan hostility content, which has fostered the creation of low-efficient 'partisan echo-chambers.'
| S-EPMC11231175 | biostudies-literature
Project description:Hate speech on social media threatens the mental health of its victims and poses severe safety risks to modern societies. Yet, the mechanisms underlying its proliferation, though critical, have remained largely unresolved. In this work, we hypothesize that moralized language predicts the proliferation of hate speech on social media. To test this hypothesis, we collected three datasets consisting of N = 691,234 social media posts and ∼35.5 million corresponding replies from Twitter that have been authored by societal leaders across three domains (politics, news media, and activism). Subsequently, we used textual analysis and machine learning to analyze whether moralized language carried in source tweets is linked to differences in the prevalence of hate speech in the corresponding replies. Across all three datasets, we consistently observed that higher frequencies of moral and moral-emotional words predict a higher likelihood of receiving hate speech. On average, each additional moral word was associated with between 10.76% and 16.48% higher odds of receiving hate speech. Likewise, each additional moral-emotional word increased the odds of receiving hate speech by between 9.35 and 20.63%. Furthermore, moralized language was a robust out-of-sample predictor of hate speech. These results shed new light on the antecedents of hate speech and may help to inform measures to curb its spread on social media.
| S-EPMC9837664 | biostudies-literature
Project description:BackgroundOnline advertisements on social media platforms are an important tool for engaging relevant populations in public health research. However, little is known about what platforms and ad characteristics are most effective in engaging high-priority HIV populations, including racial/ethnic and sexual minority individuals.MethodsData from this study were drawn from advertising campaigns conducted on popular websites and social media platforms that recruited for several nationwide randomized controlled trials of various HIV prevention and testing strategies among sexual minority men (SMM) from December 2019 until March 2022. Descriptive statistics and LASSO regression models were used to determine which platforms and ad characteristics were associated with significantly higher odds of engagement.ResultsAds on Google search, Facebook, and Instagram yielded the most cost-effective engagement, while gay-oriented dating platforms and TrafficJunky yielded the highest percentage of users who appeared to meet basic eligibility criteria. The highest percentages of Black users were screened through ads on Jack'd, TrafficJunky, and Google search; for Hispanic/Latino users, Google search, Grindr, Facebook, and Instagram. Analyzing ad characteristics, we found ads that used suggestive content, animation, and included study or institution logos were associated with greater engagement. Ads that emphasized convenience of the research (e.g. mentioned participating "from home") and that depicted people of similar races/ethnicities were also associated with greater engagement among Black and Hispanic/Latino sexual minority men.ConclusionsWe found that advertisements on mainstream social media sites are most cost effective. Although gay-oriented dating platforms were much more effective at reaching the target population, they were considerably more expensive. We also identified ad characteristics that were particularly effective in engaging users. These results could inform the design of online public health outreach campaigns for similar populations to improve their engagement and reach. Findings also demonstrated the value of conducting focused research on the effectiveness of various online marketing strategies.
| S-EPMC10668575 | biostudies-literature
Project description:ObjectiveTo pilot public health interventions at women potentially interested in maternity care via campaigns on social media (Twitter), social networks (Facebook), and online search engines (Google Search).Data sources/study settingPrimary data from Twitter, Facebook, and Google Search on users of these platforms in Los Angeles between March and July 2014.Study designObservational study measuring the responses of targeted users of Twitter, Facebook, and Google Search exposed to our sponsored messages soliciting them to start an engagement process by clicking through to a study website containing information on maternity care quality information for the Los Angeles market.Principal findingsCampaigns reached a little more than 140,000 consumers each day across the three platforms, with a little more than 400 engagements each day. Facebook and Google search had broader reach, better engagement rates, and lower costs than Twitter. Costs to reach 1,000 targeted users were approximately in the same range as less well-targeted radio and TV advertisements, while initial engagements-a user clicking through an advertisement-cost less than $1 each.ConclusionsOur results suggest that commercially available online advertising platforms in wide use by other industries could play a role in targeted public health interventions.
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Project description:Social media platforms have become the most prominent medium for spreading hate speech, primarily through hateful textual content. An extensive dataset containing emoticons, emojis, hashtags, slang, and contractions is required to detect hate speech on social media based on current trends. Therefore, our dataset is curated from various sources like Kaggle, GitHub, and other websites. This dataset contains hate speech sentences in English and is confined into two classes, one representing hateful content and the other representing non-hateful content. It has 451,709 sentences in total. 371,452 of these are hate speech, and 80,250 are non-hate speech. An augmented balanced dataset with 726,120 samples is also generated to create a custom vocabulary of 145,046 words. The total number of contractions considered in the dataset is 6403. The total number of bad words usually used in hateful content is 377. The text in each sentence of the final dataset, which is utilized for training and cross-validation, is limited to 180 words. The generated contractions dataset can be used for any projects in the area of NLP for data preprocessing. The augmented dataset can help to reduce the number of out-of-vocabulary words, and the hate speech dataset can be used as a classifier to detect hate or no hate on social media platforms.
| S-EPMC9807815 | biostudies-literature
Project description:We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.
| S-EPMC9960309 | biostudies-literature
Project description:Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.
| S-EPMC9876356 | biostudies-literature
Project description:Online social networking (OSN) has deeply penetrated university campuses, influencing multiple aspects of student life. Standing from a pedagogical perspective, this study investigates how university students' OSN engagement affect their learning outcomes. Drawn upon social learning theory, this study proposes that OSN engagement help university students' establishing the self-efficacy belief, achieving social acceptance and acculturation with environment, and these attributions further lead them to attain positive learning outcomes which are shaped by self-esteem development, satisfaction with university life, and the grade point average (GPA) based performance. Results from a survey accompanied by focus group discussions support these embedded self-conceptualization and social learning in OSN. This study contributes to the extant research on OSN for learning by highlighting the role of OSN for the whole person development, especially the students' self-conceptulization and psychological well-beings. The revealed mediating mechanisms also adds values to social learning theory and imply the design foci of e-learning activities and applications.
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Project description:Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.
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Project description:Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.
| S-EPMC7451563 | biostudies-literature