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2022 ◽  
Vol 16 (2) ◽  
pp. 1-26
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Giovanni Bruno ◽  
Paolo Trunfio

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.

Learning through social media platforms is a nascent pedagogy that opens up new virtual online e-instructional modalities and avenues to be explored especially in these challenging emergency times of COVID-19. This research focuses on a self-directed initiative of a math teacher who taught her students in an open virtual class via Instagram. This study explores how the main features of Instagram -inherently used as social interaction platform - were maximized for educational purposes. It also investigates the effects, be they positive or negative, on the learning-teaching process in terms of engagement and communication. For this, a mixed-method sequential exploratory design was opted for to conduct the study which surveyed 100 students across 22 different high schools who took part in the virtual open math classes. The findings highlight the different patterns of Instagram use and platform features that lend this social media website the requisite feasibility to educationalize it. Furthermore, the results reveal both the favourable and disadvantageous aspects of Instagram.

2022 ◽  
Vol 40 (3) ◽  
pp. 1-33
Xingshan Zeng ◽  
Jing Li ◽  
Lingzhi Wang ◽  
Kam-Fai Wong

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Brinda Sampat ◽  
Sahil Raj

Purpose“Fake news” or misinformation sharing using social media sites into public discourse or politics has increased dramatically, over the last few years, especially in the current COVID-19 pandemic causing concern. However, this phenomenon is inadequately researched. This study examines fake news sharing with the lens of stimulus-organism-response (SOR) theory, uses and gratification theory (UGT) and big five personality traits (BFPT) theory to understand the motivations for sharing fake news and the personality traits that do so. The stimuli in the model comprise gratifications (pass time, entertainment, socialization, information sharing and information seeking) and personality traits (agreeableness, conscientiousness, extraversion, openness and neuroticism). The feeling of authenticating or instantly sharing news is the organism leading to sharing fake news, which forms the response in the study.Design/methodology/approachThe conceptual model was tested by the data collected from a sample of 221 social media users in India. The data were analyzed with partial least squares structural equation modeling to determine the effects of UGT and personality traits on fake news sharing. The moderating role of the platform WhatsApp or Facebook was studied.Findings The results suggest that pass time, information sharing and socialization gratifications lead to instant sharing news on social media platforms. Individuals who exhibit extraversion, neuroticism and openness share news on social media platforms instantly. In contrast, agreeableness and conscientiousness personality traits lead to authentication news before sharing on the social media platform.Originality/value This study contributes to social media literature by identifying the user gratifications and personality traits that lead to sharing fake news on social media platforms. Furthermore, the study also sheds light on the moderating influence of the choice of the social media platform for fake news sharing.

2022 ◽  
pp. 146144482110685
Hyunyi Cho ◽  
Julie Cannon ◽  
Rachel Lopez ◽  
Wenbo Li

Concerns over the harmful effects of social media have directed public attention to media literacy as a potential remedy. Current conceptions of media literacy are frequently based on mass media, focusing on the analysis of common content and evaluation of the content using common values. This article initiates a new conceptual framework of social media literacy (SoMeLit). Moving away from the mass media-based assumptions of extant approaches, SoMeLit centers on the user’s self in social media that is in dynamic causation with their choices of messages and networks. The foci of analysis in SoMeLit, therefore, are one’s selections and values that influence and are influenced by the construction of one’s reality on social media; and the evolving characteristics of social media platforms that set the boundaries of one’s social media reality construction. Implications of the new components and dimensions of SoMeLit for future research, education, and action are discussed.

2022 ◽  
Vol 7 (4) ◽  
pp. 659-662
Akansha Gupta ◽  
Ritesh Kumar Chaurasiya

: In normal population and patient, the significant increase in dry eyes manifestations have been observed. Similarly, aggravated symptoms and complaints of dryness have also been observed in clinical and hospital staff by using a face mask for an extended time period. The purpose of the study was to observe the association between symptoms of dry eyes and the duration of using masks in health professionals.: An unspecified questionnaire was distributed using Google Forms through different social media platforms, asking each respondent to contribute to the survey. Data were collected from December 2021 to January 2021. Statistical analysis was performed using SPSS software. Statistical significance was considered if p-value was less than 0.05. A total of 39 responses was included for analysis in the study. There was a positive correlation between the frequency of the symptoms of dryness and the duration of using the mask. Similarly, the severity of the symptoms for dry eyes was strongly correlated with an increase in the frequency of symptoms for dry eyes.The finding reflects that the frequency of the dryness along with the severity will increase with the increase in the duration of wearing a mask. Moreover, it also suggests that cloth mask is the probable predisposing factor for the increase in the dry eye symptoms in this study.

2022 ◽  
Vol 9 ◽  
Stella K. Chong ◽  
Shahmir H. Ali ◽  
Lan N. Ðoàn ◽  
Stella S. Yi ◽  
Chau Trinh-Shevrin ◽  

Social media has been crucial for seeking and communicating COVID-19 information. However, social media has also promulgated misinformation, which is particularly concerning among Asian Americans who may rely on in-language information and utilize social media platforms to connect to Asia-based networks. There is limited literature examining social media use for COVID-19 information and the subsequent impact of misinformation on health behaviors among Asian Americans. This perspective reviews recent research, news, and gray literature to examine the dissemination of COVID-19 misinformation on social media platforms to Chinese, Korean, Vietnamese, and South Asian Americans. We discuss the linkage of COVID-19 misinformation to health behaviors, with emphasis on COVID-19 vaccine misinformation and vaccine decision-making in Asian American communities. We then discuss community- and research-driven responses to investigate misinformation during the pandemic. Lastly, we propose recommendations to mitigate misinformation and address the COVID-19 infodemic among Asian Americans.

2022 ◽  
Vol 9 ◽  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

JMIR Nursing ◽  
10.2196/35274 ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. e35274
Bhavya Yalamanchili ◽  
Lorie Donelle ◽  
Leo-Felix Jurado ◽  
Joseph Fera ◽  
Corey H Basch

Background During a time of high stress and decreased social interaction, nurses have turned to social media platforms like TikTok as an outlet for expression, entertainment, and communication. Objective The purpose of this cross-sectional content analysis study is to describe the content of videos with the hashtag #covidnurse on TikTok, which included 100 videos in the English language. Methods At the time of the study, this hashtag had 116.9 million views. Each video was coded for content-related to what nurses encountered and were feeling during the COVID-19 pandemic. Results Combined, the 100 videos sampled received 47,056,700 views; 76,856 comments; and 5,996,676 likes. There were 4 content categories that appeared in a majority (>50) of the videos: 83 showed the individual as a nurse, 72 showed the individual in professional attire, 58 mentioned/suggested stress, 55 used music, and 53 mentioned/suggested frustration. Those that mentioned stress and those that mentioned frustration received less than 50% of the total views (n=21,726,800, 46.17% and n=16,326,300, 34.69%, respectively). Although not a majority, 49 of the 100 videos mentioned the importance of nursing. These videos garnered 37.41% (n=17,606,000) of the total views, 34.82% (n=26,759) of the total comments, and 23.85% (n=1,430,213) of the total likes. So, despite nearly half of the total videos mentioning how important nurses are, these videos received less than half of the total views, comments, and likes. Conclusions Social media and increasingly video-related online messaging such as TikTok are important platforms for social networking, social support, entertainment, and education on diverse topics, including health in general and COVID-19 specifically. This presents an opportunity for future research to assess the utility of the TikTok platform for meaningful engagement and health communication on important public health issues.

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