Table 1. Complete list of the screened documents.
| ID | Document Reference |
Title |
|---|---|---|
| 1 | [52] | Awareness toward COVID-19 precautions among different levels of dental students in King Saud university, Riyadh, Saudi Arabia |
| 2 | [53] | Examining algorithmic biases in YouTube’s recommendations of vaccine videos |
| 3 | [54] | Impact of public sentiments on the transmission of COVID-19 across a geographical gradient |
| 4 | [55] | Arabic rumor detection: A comparative study |
| 5 | [56] | Are people incidentally exposed to news on social media? A comparative analysis |
| 6 | [57] | Social media-based COVID-19 sentiment classification model using Bi-LSTM |
| 7 | [58] | COVID-19, a tale of two pandemics: Novel coronavirus and fake news messaging |
| 8 | [59] | Fentanyl panic goes viral: The spread of misinformation about overdose risk from casual contact with fentanyl in mainstream and social media |
| 9 | [60] | Precision Global Health ‐ The case of Ebola: A scoping review |
| 10 | [61] | Social Media, Science, and Attack Discourse: How Twitter Discussions of Climate Change Use Sarcasm and Incivility |
| 11 | [62] | 2019-nCoV, fake news, and racism |
| 12 | [63] | Digital work engagement among Italian neurologists |
| 13 | [64] | Quantifying the drivers behind collective attention in information ecosystems |
| 14 | [65] | The Politicization of Ivermectin Tweets during the COVID-19 Pandemic |
| 15 | [66] | COVID-19 in South Carolina: Experiences Using Facebook as a Self-Organizing Tool for Grassroots Advocacy, Education, and Social Support |
| 16 | [67] | Self-medication and the ‘infodemic’ during mandatory preventive isolation due to the COVID-19 pandemic |
| 17 | [68] | How essential is kratom availability and use during COVID-19? Use pattern analysis based on survey and social media data |
| 18 | [69] | OCR post-correction for detecting adversarial text images |
| 19 | [70] | INDOBERT FOR INDONESIAN FAKE NEWS DETECTION |
| 20 | [71] | An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders |
| 21 | [72] | A systematic literature review on spam content detection and classification |
| 22 | [73] | How do Canadian public health agencies respond to the COVID-19 emergency using social media: A protocol for a case study using content and sentiment analysis |
| 23 | [74] | A retrospective analysis of social media posts pertaining to COVID-19 vaccination side effects |
| 24 | [75] | Quality of Bladder Cancer Information on YouTube[Formula presented] |
| 25 | [76] | A Relationship-Centered and Culturally Informed Approach to Studying Misinformation on COVID-19 |
| 26 | [77] | It Takes a Village to Combat a Fake News Army: Wikipedia’s Community and Policies for Information Literacy |
| 27 | [78] | Identifying cross-platform user relationships in 2020 U.S. election fraud and protest discussions |
| 28 | [79] | Social research 2.0: Virtual snowball sampling method using Facebook |
| 29 | [80] | Realfood and Cancer: Analysis of the Reliability and Quality of YouTube Content |
| 30 | [81] | A comprehensive Benchmark for fake news detection |
| 31 | [82] | A scoping review of COVID-19 online mis/disinformation in Black communities |
| 32 | [83] | Improving the Communication and Credibility of Government Media in Response to Public Health Emergencies: Analysis of Tweets From the WeChat Official Accounts of 10 Chinese Health Commissioners |
| 33 | [84] | Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithm |
| 34 | [85] | Fake Sentence Detection Based on Transfer Learning: Applying to Korean COVID‐19 Fake News |
| 35 | [86] | Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence |
| 36 | [87] | Connectivity Between Russian Information Sources and Extremist Communities Across Social Media Platforms |
| 37 | [88] | Nigeria EndSARS Protest: False Information Mitigation Hybrid Model |
| 38 | [89] | Arabic Language Modeling Based on Supervised Machine Learning |
| 39 | [90] | When Does an Individual Accept Misinformation? An Extended Investigation Through Cognitive Modeling |
| 40 | [91] | COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis |
| 41 | [92] | Perceived Vaccine Efficacy, Willingness to Pay for COVID-19 Vaccine and Associated Determinants among Foreign Migrants in China |
| 42 | [93] | Misinformation about the Human Gut Microbiome in YouTube Videos: Cross-sectional Study |
| 43 | [94] | A Social Network Analysis of Tweets Related to Mandatory COVID-19 Vaccination in Poland |
| 44 | [95] | MeVer NetworkX: Network Analysis and Visualization for Tracing Disinformation |
| 45 | [96] | Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter |
| 46 | [97] | ‘Blurred boundaries’: When nurses and midwives give anti-vaccination advice on Facebook |
| 47 | [98] | PM Me the Truth? The Conditional Effectiveness of Fact-Checks Across Social Media Sites |
| 48 | [99] | Xenophobic Bullying and COVID-19: An Exploration Using Big Data and Qualitative Analysis |
| 49 | [100] | BreadTube Rising: How Modern Creators Use Cultural Formats to Spread Countercultural Ideology |
| 50 | [101] | Dynamic Light Weight Recommendation System for Social Networking Analysis Using a Hybrid LSTM-SVM Classifier Algorithm |
| 51 | [102] | An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19 |
| 52 | [103] | Understanding Public Perceptions of Per- and Polyfluoroalkyl Substances: Infodemiology Study of Social Media |
| 53 | [104] | Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter |
| 54 | [105] | Public Policy Measures to Increase Anti-SARS-CoV-2 Vaccination Rate in Russia |
| 55 | [106] | Contextualizing Engagement With Health Information on Facebook: Using the Social Media Content and Context Elicitation Method |
| 56 | [107] | The Challenge of Debunking Health Misinformation in Dynamic Social Media Conversations: Online Randomized Study of Public Masking During COVID-19 |
| 57 | [108] | People lie, actions Don’t! Modeling infodemic proliferation predictors among social media users |
| 58 | [109] | CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT |
| 59 | [110] | Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation |
| 60 | [111] | Receiving COVID-19 Messages on Social Media to the People of Semarang City |
| 61 | [112] | Impact of COVID-19 on HIV Prevention Access: A Multi-platform Social Media Infodemiology Study |
| 62 | [113] | Monkeypox Vaccine Acceptance among Ghanaians: A Call for Action |
| 63 | [114] | Conspiracy Beliefs, Misinformation, Social Media Platforms, and Protest Participation |
| 64 | [115] | State vs. anti-vaxxers: Analysis of Covid-19 echo chambers in Serbia |
| 65 | [116] | Fake News Detection Techniques on Social Media: A Survey |
| 66 | [117] | Inclusive Study of Fake News Detection for COVID-19 with New Dataset using Supervised Learning Algorithms |
| 67 | [118] | On Politics and Pandemic: How Do Chilean Media Talk about Disinformation and Fake News in Their Social Networks? |
| 68 | [119] | COMMENT: Narrative-based misinformation in India about protection against Covid-19: Not just another "moo-point" |
| 69 | [120] | Narratives of Anti‐Vaccination Movements in the German and Brazilian Twittersphere: A Grounded Theory Approach |
| 70 | [121] | Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks |
| 71 | [122] | Looking for cystoscopy on YouTube: Are videos a reliable information tool for internet users? |
| 72 | [123] | A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects |
| 73 | [124] | The Impact of the COVID-19 “Infodemic” on Well-Being: A Cross-Sectional Study |
| 74 | [125] | Medical and Health-Related Misinformation on Social Media: Bibliometric Study of the Scientific Literature |
| 75 | [126] | Dynamics of social corrections to peers sharing COVID-19 misinformation on WhatsApp in Brazil |
| 76 | [127] | A hierarchical network-oriented analysis of user participation in misinformation spread on WhatsApp |
| 77 | [128] | Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach |
| 78 | [129] | Factors Influencing the Accessibility and Reliability of Health Information in the Face of the COVID-19 Outbreak—A Study in Rural China |
| 79 | [130] | The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation |
| 80 | [131] | Tracking Private WhatsApp Discourse about COVID-19 in Singapore: Longitudinal Infodemiology Study |
| 81 | [132] | The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends |
| 82 | [133] | What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision |
| 83 | [134] | Public perception of SARS-CoV-2 vaccinations on social media: Questionnaire and sentiment analysis |
| 84 | [135] | Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization |
| 85 | [136] | Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter |
| 86 | [137] | Covid-19 vaccine hesitancy on social media: Building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies |
| 87 | [138] | News media stories about cancer on Facebook: How does story framing influence response framing, tone and attributions of responsibility? |
| 88 | [139] | Credibility of scientific information on social media: Variation by platform, genre and presence of formal credibility cues |
| 89 | [140] | Health Misinformation on Social Media and its Impact on COVID-19 Vaccine Inoculation in Jordan |
| 90 | [141] | Infodemia–an analysis of fake news in polish news portals and traditional media during the coronavirus pandemic |
| 91 | [142] | Feasibility of utilizing social media to promote hpv self‐collected sampling among medically underserved women in a rural southern city in the united states (U.s.) |
| 92 | [143] | A retrospective analysis of the covid-19 infodemic in Saudi Arabia |
| 93 | [144] | Machine learning in detecting covid-19 misinformation on twitter |
| 94 | [145] | The Response of Governments and Public Health Agencies to COVID-19 Pandemics on Social Media: A Multi-Country Analysis of Twitter Discourse |
| 95 | [146] | Human Papillomavirus Vaccination and Social Media: Results in a Trial With Mothers of Daughters Aged 14–17 |
| 96 | [147] | Social media monitoring of the COVID-19 pandemic and influenza epidemic with adaptation for informal language in Arabic twitter data: Qualitative study |
| 97 | [148] | An infodemiology and infoveillance study on covid-19: Analysis of twitter and google trends |
| 98 | [149] | COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis |
| 99 | [150] | A survey of Big Data dimensions vs Social Networks analysis |
| 100 | [151] | Plandemic Revisited: A Product of Planned Disinformation Amplifying the COVID-19 “infodemic” |
| 101 | [152] | Marginalizing the Mainstream: How Social Media Privilege Political Information |
| 102 | [153] | QATAR’S COMMUNICATION STRATEGY AND THE RESOLUTION OF THE DIPLOMATIC CONFLICT IN THE GULF |
| 103 | [154] | Towards a critical understanding of social networks for the feminist movement: Twitter and the women’s strike |
| 104 | [155] | YouTube as a source of information on gout: a quality analysis |
| 105 | [156] | Social Media, Cognitive Reflection, and Conspiracy Beliefs |
| 106 | [157] | Using machine learning to compare provaccine and antivaccine discourse among the public on social media: Algorithm development study |
| 107 | [158] | A social bot in support of crisis communication: 10-years of @LastQuake experience on Twitter |
| 108 | [159] | Determinants of individuals’ belief in fake news: A scoping review determinants of belief in fake news |
| 109 | [160] | Lack of trust, conspiracy beliefs, and social media use predict COVID-19 vaccine hesitancy |
| 110 | [161] | Health information seeking behaviors on social media during the covid-19 pandemic among american social networking site users: Survey study |
| 111 | [162] | Semi-automatic generation of multilingual datasets for stance detection in Twitter |
| 112 | [163] | Social media content of idiopathic pulmonary fibrosis groups and pages on facebook: Cross-sectional analysis |
| 113 | [164] | Collecting a large scale dataset for classifying fake news tweets usingweak supervision |
| 114 | [165] | Youtube videos and informed decision-making about covid-19 vaccination: Successive sampling study |
| 115 | [166] | The commonly utilized natural products during the COVID-19 pandemic in Saudi Arabia: A cross-sectional online survey |
| 116 | [167] | A behavioural analysis of credulous Twitter users |
| 117 | [73] | How do Canadian public health agencies respond to the COVID-19 emergency using social media: A protocol for a case study using content and sentiment analysis |
| 118 | [168] | The negative role of social media during the COVID-19 outbreak |
| 119 | [169] | Twitter’s Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets |
| 120 | [58] | COVID-19, a tale of two pandemics: Novel coronavirus and fake news messaging |
| 121 | [170] | Concerns discussed on chinese and french social media during the COVID-19 lockdown:comparative infodemiology study based on topic modeling |
| 122 | [171] | Social media and medical education in the context of the COVID-19 pandemic: Scoping review |
| 123 | [172] | Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks |
| 124 | [173] | Detecting fake news on Facebook: The role of emotional intelligence |
| 125 | [174] | Information disorders during the COVID-19 infodemic: The case of Italian Facebook |
| 126 | [175] | Conspiracy vs science: A large-scale analysis of online discussion cascades |
| 127 | [176] | Will the World Ever Be the Same After COVID-19? Two Lessons from the First Global Crisis of a Digital Age |
| 128 | [177] | Using tweets to understand how COVID-19–Related health beliefs are affected in the age of social media: Twitter data analysis study |
| 129 | [178] | General audience engagement with antismoking public health messages across multiple social media sites: Comparative analysis |
| 130 | [179] | An analysis of YouTube videos as educational resources for dental practitioners to prevent the spread of COVID-19 |
| 131 | [180] | Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches |
| 132 | [181] | Visual analytics of twitter and social media dataflows: A casestudy of COVID-19 rumors |
| 133 | [182] | Examining embedded apparatuses of AI in Facebook and TikTok |
| 134 | [183] | Prevalence and perception among saudi arabian population about resharing of information on social media regarding natural remedies as protective measures against covid-19 |
| 135 | [184] | Level of acceptance of news stories on social media platforms among youth in Nigeria |
| 136 | [185] | Disinformation, vaccines, and covid-19. Analysis of the infodemic and the digital conversation on twitter [Desinformación, vacunas y covid-19. Análisis de la infodemia y la conversación digital en twitter] |
| 137 | [186] | Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study |
| 138 | [187] | Youtube as a source of information on epidural steroid injection |
| 139 | [188] | An exploratory study of social media users’ engagement with COVID-19 vaccine-related content |
| 140 | [189] | Online influencers: Healthy food or fake news |
| 141 | [190] | Sentimental Analysis on Health-Related Information with Improving Model Performance using Machine Learning |
| 142 | [191] | Digital civic participation and misinformation during the 2020 taiwanese presidential election |
| 143 | [192] | Challenging post-communication: Beyond focus on a ‘few bad apples’ to multi-level public communication reform |
| 144 | [193] | Knowledge about COVID-19 in Brazil: Cross-sectional web-based study |
| 145 | [194] | “Down the rabbit hole” of vaccine misinformation on youtube: Network exposure study |
| 146 | [195] | Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection |
| 147 | [196] | Social Media Use by Young People Living in Conflict-Affected Regions of Myanmar |
| 148 | [197] | Two-Path Deep Semisupervised Learning for Timely Fake News Detection |
| 149 | [198] | Deep learning for misinformation detection on online social networks: a survey and new perspectives |
| 150 | [199] | FauxWard: a graph neural network approach to fauxtography detection using social media comments |
| 151 | [200] | Internet users engage more with phatic posts than with health misinformation on Facebook |
| 152 | [201] | SENTIMENTAL ANALYSIS OF COVID-19 TWITTER DATA USING DEEP LEARNING AND MACHINE LEARNING MODELS [ANÁLISIS DE SENTIMIENTO DE LOS DATOS DE TWITTER DE COVID-19 UTILIZANDO MODELOS DE APRENDIZAJE PROFUNDO Y APRENDIZAJE MÁQUINA] |
| 153 | [202] | Partisan public health: how does political ideology influence support for COVID-19 related misinformation? |
| 154 | [203] | COVID-19 and the “Film Your Hospital” conspiracy theory: Social network analysis of Twitter data |
| 155 | [204] | Fake news and aggregated credibility: Conceptualizing a co-creative medium for evaluation of sources online |
| 156 | [205] | COVID-19 Information on YouTube: Analysis of Quality and Reliability of Videos in Eleven Widely Spoken Languages across Africa |
| 157 | [206] | COVID-19: Retransmission of official communications in an emerging pandemic |
| 158 | [207] | Insights from twitter conversations on lupus and reproductive health: Protocol for a content analysis |
| 159 | [208] | Temporal and location variations, and link categories for the dissemination of COVID-19-related information on twitter during the SARS-CoV-2 outbreak in Europe: Infoveillance study |
| 160 | [209] | Inflaming public debate: a methodology to determine origin and characteristics of hate speech about sexual and gender diversity on Twitter |
| 161 | [210] | How to fight an infodemic: The four pillars of infodemic management |
| 162 | [211] | Genesis of an emergency public drug information website by the French Society of Pharmacology and Therapeutics during the COVID-19 pandemic |
| 163 | [212] | YouTube as a source of information on COVID-19: A pandemic of misinformation? |
| 164 | [213] | The impact of social media on panic during the COVID-19 pandemic in iraqi kurdistan: Online questionnaire study |
| 165 | [214] | COVID-19 and the 5G conspiracy theory: Social network analysis of twitter data |
| 166 | [215] | From disinformation to fact-checking: How Ibero-American fact-checkers on Twitter combat fake news |
| 167 | [216] | Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set |
| 168 | [217] | Mining physicians’ opinions on social media to obtain insights into COVID-19: Mixed methods analysis |
| 169 | [218] | A new application of social impact in social media for overcoming fake news in health |
| 170 | [219] | Islamophobic hate speech on social networks. An analysis of attitudes to Islamophobia on Twitter [El discurso de odio islamófobo en las redes sociales. Un análisis de las actitudes ante la islamofobia en Twitter] |
| 171 | [220] | Information management in healthcare and environment: Towards an automatic system for fake news detection |
| 172 | [221] | Vaccine-related advertising in the Facebook Ad Archive |
| 173 | [222] | Ontology Meter for Twitter Fake Accounts Detection |
| 174 | [223] | Social media and fake news in the post-truth era: The manipulation of politics in the election process |
| 175 | [224] | An analysis of fake narratives on social media during 2019 Indonesian presidential election |
| 176 | [225] | Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based Approach |
| 177 | [226] | Fake News Detection Using Machine Learning Ensemble Methods |
| 178 | [227] | Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication |
| 179 | [228] | Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter |
| 180 | [229] | The visual vaccine debate on twitter: A social network analysis |
| 181 | [230] | "Tell us what’s going on": Exploring the information needs of pregnant and postpartum women in Australia during the pandemic with ’Tweets’, ’Threads’, and women’s views |
| 182 | [231] | Paying SPECIAL consideration to the digital sharing of information during the COVID-19 pandemic and beyond |
| 183 | [232] | Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit |
| 184 | [233] | Fake news analysis modeling using quote retweet |
| 185 | [234] | Automatically appraising the credibility of vaccine-related web pages shared on social media: A twitter surveillance study |
| 186 | [235] | Citizen journalism and public participation in the Era of New Media in Indonesia: From street to tweet |
| 187 | [236] | Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter [Desinformación y vacunas en redes: Comportamiento de los bulos en Twitter] |
| 188 | [237] | Fiji’s coup culture: Rediscovering a voice at the ballot box |
| 189 | [238] | Polarization and fake news: Early warning of potential misinformation targets |
| 190 | [239] | Fake news and dental education |
| 191 | [240] | A corpus of debunked and verified user-generated videos |
| 192 | [241] | Comparison study between the UAE, the UK, and India in Dealing with whatsapp fake news |
| 193 | [242] | Constitution, democracy, regulation of the internet and electoral fake news in the Brazilian elections [Constituição, democracia, regulação da internet e fake news nas eleições brasileiras] |
| 194 | [243] | Recycling old strategies and devices: What remains, an art project addressing disinformation campaigns (Re)using strategies to delay industry regulation [What remains, un proyecto artístico que trata sobre las campañas de desinformación (Re)utilizando estrategias para retrasar la regulación industrial] |
| 195 | [244] | Reading between the lines and the numbers: An analysis of the first NetzDG reports |
| 196 | [245] | After the ‘APIcalypse’: social media platforms and their fight against critical scholarly research |
| 197 | [246] | Health-Related Disaster Communication and Social Media: Mixed-Method Systematic Review |
| 198 | [247] | Are internet videos useful sources of information during global public health emergencies? A case study of YouTube videos during the 2015–16 Zika virus pandemic |
| 199 | [248] | Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review |
| 200 | [249] | Detection and visualization of misleading content on Twitter |
| 201 | [250] | Tweet, truth and fake news: A study of BJP’s official tweeter handle |
| 202 | [251] | Social media, dietetic practice and misinformation: A triangulation research |
| 203 | [252] | Examination of YouTube videos related to synthetic cannabinoids |
| 204 | [253] | Practices and promises of Facebook for science outreach: Becoming a “Nerd of Trust” |
| 205 | [254] | Rising tides or rising stars?: Dynamics of shared attention on twitter during media events |
| 206 | [255] | Misleading health-related information promoted through video-based social media: Anorexia on youtube |
| 207 | [256] | Quality of healthcare information on YouTube: psoriatic arthritis [Qualität von Gesundheitsinformationen auf YouTube: Psoriasisarthritis] |
| 208 | [257] | YOUTUBEASASOURCE OFINFORMATIONABOUT UNPROVENDRUGSFOR COVID-19: the role of the mainstream media and recommendation algorithms in promoting misinformation [YOUTUBE COMO FUENTE DE INFORMACIÓN SOBRE MEDICAMENTOS NO PROBADOS PARA EL COVID-19: el papel de los principales medios de comunicación y los algoritmos de recomendación en la promoción de la desinformación] [YOUTUBE COMO FONTE DE INFORMAÇÃO SOBRE MEDICAMENTOS SEM EFICÁCIA COMPROVADA PARA COVID-19: o papel da imprensa tradicional e dos algoritmos de recomendação na promoção da desinformação] |
| 209 | [258] | Utilising online eye-tracking to discern the impacts of cultural backgrounds on fake and real news decision-making |
| 210 | [259] | Top 100 #PCOS influencers: Understanding who, why and how online content for PCOS is influenced |
| 211 | [260] | Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis |
| 212 | [261] | Negative COVID-19 Vaccine Information on Twitter: Content Analysis |
| 213 | [262] | Platform Effects on Public Health Communication:A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook |
| 214 | [263] | The influence of fake news on face-trait learning |
| 215 | [264] | COVID-Related Misinformation Migration to BitChute and Odysee |
| 216 | [265] | Sending News Back Home: Misinformation Lost in Transnational Social Networks |
| 217 | [266] | Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic |
| 218 | [267] | Organization and evolution of the UK far-right network on Telegram |
| 219 | [268] | Predictive modeling for suspicious content identification on Twitter |
| 220 | [269] | Detection and moderation of detrimental content on social media platforms: current status and future directions |
| 221 | [270] | Cross-platform information spread during the January 6th capitol riots |
| 222 | [271] | Combating multimodal fake news on social media: methods, datasets, and future perspective |
| 223 | [272] | In.To. COVID-19 socio-epidemiological co-causality |
| 224 | [273] | Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit |
| 225 | [274] | Investigating the Impacts of YouTube’s Content Policies on Journalism and Political Discourse |
| 226 | [275] | Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news |
| 227 | [276] | A deep dive into COVID-19-related messages on WhatsApp in Pakistan |
| 228 | [277] | It-which-must-not-be-named: COVID-19 misinformation, tactics to profit from it and to evade content moderation on YouTube |
| 229 | [278] | Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study |
| 230 | [279] | The three-step persuasion model on YouTube: A grounded theory study on persuasion in the protein supplements industry |
| 231 | [280] | Examining the Twitter Discourse on Dementia During Alzheimer’s Awareness Month in Canada: Infodemiology Study |
| 232 | [281] | Rapid Sharing of Islamophobic Hate on Facebook: The Case of the Tablighi Jamaat Controversy |
| 233 | [282] | Social Media and the Influence of Fake News on Global Health Interventions: Implications for a Study on Dengue in Brazil |
| 234 | [283] | Spanish Facebook Posts as an Indicator of COVID-19 Vaccine Hesitancy in Texas |
| 235 | [284] | Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study |
| 236 | [285] | Empowering Health Care Workers on Social Media to Bolster Trust in Science and Vaccination During the Pandemic: Making IMPACT Using a Place-Based Approach |
| 237 | [286] | Exploring Motivations for COVID-19 Vaccination among Black Young Adults in 3 Southern US States: Cross-sectional Study |
| 238 | [287] | Development of Principles for Health-Related Information on Social Media: Delphi Study |
| 239 | [288] | The Influence of Fake News on Social Media: Analysis and Verification of Web Content during the COVID-19 Pandemic by Advanced Machine Learning Methods and Natural Language Processing |
| 240 | [289] | Habermasian analysis of reports on Presidential tweets influencing politics in the USA |
| 241 | [290] | A unified approach of detecting misleading images via tracing its instances on web and analyzing its past context for the verification of multimedia content |
| 242 | [291] | “It’s true! I saw it on WhatsApp”: Social Media, Covid-19, and Political-Ideological Orientation in Brazil |
| 243 | [292] | Use of digital media for family planning information by women and their social networks in Kenya: A qualitative study in peri-urban Nairobi |
| 244 | [293] | Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube |
| 245 | [294] | Bots’ Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter |
| 246 | [295] | Misinformation About COVID-19 Vaccines on Social Media: Rapid Review |
| 247 | [296] | Fear, Stigma and Othering: The Impact of COVID-19 Rumours on Returnee Migrants and Muslim Populations of Nepal |
| 248 | [297] | Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat |
| 249 | [298] | The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic |
| 250 | [299] | Twelve tips to make successful medical infographics |
| 251 | [300] | TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets |
| 252 | [301] | Cognitive and affective responses to political disinformation in Facebook |
| 253 | [302] | Experience: Managing misinformation in social media-insights for policymakers from Twitter analytics |
| 254 | [303] | Hepatitis E vaccine in China: Public health professional perspectives on vaccine promotion and strategies for control |
| 255 | [304] | “Fake Elections”? Cyber Propaganda, Disinformation and the 2017 General Elections in Kenya |
| 256 | [305] | ‘Fake News’ in urology: evaluating the accuracy of articles shared on social media in genitourinary malignancies |
| 257 | [306] | “I will kill myself”–The series of posts in Facebook and unnoticed departure of a life |
| 258 | [307] | Ethiopia’s Hate Speech Predicament: Seeking Antidotes Beyond a Legislative Response |
| 259 | [308] | The Paradox of Participation Versus Misinformation: Social Media, Political Engagement, and the Spread of Misinformation |
| 260 | [309] | ‘Techlash’, responsible innovation, and the self-regulatory organization |
| 261 | [310] | YouTube videos as a source of misinformation on idiopathic pulmonary fibrosis |
| 262 | [311] | Dissemination of Misinformative and Biased Information about Prostate Cancer on YouTube |
| 263 | [312] | Hyperacusis and social media trends |
| 264 | [313] | Media education with the monetization of YouTube: The loss of truth as an exchange value [Educación mediática frente a la monetización en YouTube: la pérdida de la verdad como valor de cambio] |
| 265 | [314] | All i Have Learned, i Have Learned from Google: Why Today’s Facial Rejuvenation Patients are Prone to Misinformation, and the Steps We can take to Contend with Unreliable Information |
| 266 | [315] | Digital diplomacy: Emotion and identity in the public realm |
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