User Activity Classification and Domain-Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Designing IDE Interventions to Promote Social Interaction and Improved Programming Outcomes in Early Computing Courses

As in other STEM disciplines, early computing courses tend to stress individual assignments and discourage collaboration. This can lead to negative learning experiences that compel some students to give up. According to social learning theory, one way to improve students’ learning experiences is to help them form and participate actively in vibrant social learning communities. Building on social learning theory, we have designed a set of software interventions (scaffolds and prompts) that leverage automatically collected learning process data to promote increased social interactions and better learning outcomes in individual programming assignments, which are a key component of early undergraduate computing courses. In an empirical study, we found that students’ interaction with the interventions was correlated with increased social activity, improved attitudes toward peer learning, more closely coupled social networks, and higher performance on programming assignments. Our work contributes a theoretically motivated technological design for social programming interventions; an understanding of computing students’ willingness to interact with the interventions; and insights into how students’ interactions with the interventions are associated with their social behaviors, attitudes, connectedness with others in the class, and their course outcomes.

User’s Activity Classification And Domain Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Knowledge Level about Management of Social Media Use during the Covid-19 Pandemic

Introduction: Pandemic COVID-19 has led people to a new norm of spending most of their time at home. Regular direct physical social interactions become less common and replaced by interacting using social media. Method: This is study is a descriptive survey, describing society’s knowledge on the management of social media usage in COVID-19 Pandemic. 666 samples were gathered who met the inclusion and exclusion criteria. Google Form was spread amongst webinar participants, processed and distributed into tables, including average score based on age groups. Results: Majority of the participants (69.5%) achieved a score between 5-6 out of 7 questions that were given. Whilst, 0 participants received scores between 0 to 1. Results achieved by all age groups are almost similar, with age 36-40 appearing on top. Conclusion: Knowledge regarding social media usage management does not appear to be affected by the person’s age. This is because social media has been used by people of all ages, hence have almost similar knowledge regarding its usage.

Mindreading and Social Cognition

The cognitive ability to think about other people's psychological states is known as `mindreading'. This Element critiques assumptions that have been formative in shaping philosophical theories of mindreading: that mindreading is ubiquitous, underpinning the vast majority of our social interactions; and that its primary goal is to provide predictions and explanations of other people's behaviour. It begins with an overview of key positions and empirical literature in the debate. It then introduces and motivates the pluralist turn in this literature, which challenges the core assumptions of the traditional views. The second part of the Element uses case studies to further motivate the pluralist framework, and to advocate the pluralist approach as the best way to progress our understanding of social cognitive phenomena.

Willingness to communicate in relation to language use among Pakistani undergraduates: A sociocultural perspective

This study aimed to investigate English as a second language (ESL) undergraduates’ sociocultural perspective of willingness to communicate (WTC) in English inside the classroom in relation to language use outside the classroom. The participants were 440 ESL undergraduates selected through the cluster sampling method from eight universities in Khyber Pakhtunkhwa Province in Pakistan. The data were collected through questionnaires on WTC in English inside the classroom and language use outside the classroom. The findings revealed that the participants’ level of WTC in English was high for most social interactions within the classroom, such as in groups, during activities, with the same gender, and when given preparation time in groups. The findings for language use showed that a mixture of languages, such as Pashto and Urdu, was predominantly used in the family, neighbourhood and friendship, religion, education, and transaction domains. In contrast, English was primarily used in the mass media and social media domains. Moreover, the findings revealed that WTC in English inside the classroom was positively correlated with social media, mass media, transaction and education domains but negatively correlated with the family domain.

KONDISI SOSIAL EKONOMI PARA PELAKU USAHA MIKRO, KECIL DAN MENENGAH (UMKM) PADA MASA PANDEMI COVID-19 DI KELURAHAN HELVETIA TIMUR

Based on the results of the research conducted, even though the Covid pandemic condition which caused a decrease in the income of Micro, Small and Medium Enterprises players, they were still able to survive and were still sufficient to meet their needs. The education of the children of UMKM actors is fulfilled up to the informal sector. Adequate living conditions because it is already a permanent home. All MSME actors and their family members are registered in the BPJS program, as well as their employees are registered in the BPJS manpower program. Social interactions with family are harmonious, as well as with fellow business actors. Apart from the ability of MSMEs to maintain their economy during the COVID-19 pandemic, the authors suggest that the government be able to provide assistance that can be distributed evenly so that new and old MSMEs can compete in the future

Making Community Beliefs and Capacities Visible Through Care-mongering During COVID-19

The COVID-19 global pandemic brought forth wide-ranging, unanticipated changes in human interaction, as communities rushed to slow the spread of the coronavirus. In response, local geographic community members created grassroots care-mongering groups on social media to facilitate acts of kindness, otherwise known as care-mongering. In this paper, we are interested in understanding the types of care-mongering that take place and how such care-mongering might contribute to community collective efficacy (CCE) and community resilience during a long-haul global pandemic. We conducted a content analysis of a care-mongering group on Facebook to understand how local community members innovated and developed care-mongering practices online. We observed three facets of care-mongering: showing appreciation for helpers, coming up with ways of supporting one another's needs, and continuing social interactions online and present design recommendations for further augmenting care-mongering practices for local disaster relief in online groups.