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2022 ◽  
Vol 1 (3) ◽  
pp. 1-4
Author(s):  
Farha Yashmin Rohman ◽  

Pandemic like COVID-19 has triggered disruptions in personal and collective lives globally. It is not only a pandemic, but also an Infodemic of misinformation about the virus which raises demand for reliable and trustworthy information. With the advent of social media creation and consumption of news have been changing among the young generation. Student leaders have taken on additional work and assumed new responsibilities by volunteering in their communities and creating awareness among the public about the accuracy of information and measures to be taken against the deadly virus. This study explores the use of Facebook handles by the student leaders of two universities in Guwahati in creating awareness about the health-related messages regarding Covid-19 and its vaccination. The researcher will use critical discourse analysis to evaluate the use of social networking sites by the students’ leaders. To understand the usage by the leaders, Facebook pages of the leaders would be followed and studied backed with unstructured interviews with the leaders to understand the purpose of and pattern of using the social media handles.


Author(s):  
Shaha Al-Otaibi ◽  
Nourah Altwoijry ◽  
Alanoud Alqahtani ◽  
Latifah Aldheem ◽  
Mohrah Alqhatani ◽  
...  

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.


Author(s):  
Warih Maharani ◽  
Veronikha Effendy

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>


2022 ◽  
Vol 217 ◽  
pp. 106018
Author(s):  
Vanessa Teles da Mota ◽  
Catherine Pickering ◽  
Alienor Chauvenet

Body Image ◽  
2022 ◽  
Vol 40 ◽  
pp. 200-206
Author(s):  
Keisha C. Gobin ◽  
Sarah E. McComb ◽  
Jennifer S. Mills

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