Enterprising Citizens: Digital Self‐Help Gurus in Postliberalization India

This study evaluates different antecedents affecting information sharing via multiple social media platforms on a large scale. In doing so, this research compares the effects of information sharing behavior factors via Facebook and WeChat adoption. The respondents were international students studying in two Chinese universities and are the frequent users of both social media platforms. At first, quantity data has been collected through an online survey. Second, in-depth interviews were conducted to get qualitative data. To test our model and hypotheses multigroup analysis and content analysis were used. All exogenous variables such as perceived usefulness (PU), perceived ease of use (PEOU), Technological innovation (INNO) and information sharing attitude (ATT) have a positive effect on information sharing behavior (BEH). In addition, the results indicated no difference effects of PU and ATT via multiple social media platforms (WeChat and Facebook). However the effect of PEOU on information sharing attitude varied via social media platforms (WeChat and Facebook). The author found positive and stronger relationship between PEOU and ATT which is significant for Facebook only. On the other hand, the positive relationship between INNO and ATT is significant and stronger for WeChat only. We found the least research about information sharing behavior via multiple social media platforms. Besides, this study describes the patterns of information sharing on Facebook and WeChat, simultaneously.

Fake news has become a significant challenge on online social platforms, increasing uncertainty and unwanted tension in society. The negative impact of fake news on political processes, public health, and social harmony underscores the urgency of developing more effective detection systems. Existing methods for fake news detection often focus solely on one platform, potentially missing important clues that arise from multiple platforms. Another important consideration is that the domain of fake news changes rapidly, making cross-domain analysis more difficult than in-domain analysis. To address both of these limitations, our method takes evidence from multiple social media platforms, enhances our cross-domain analysis, and improves overall detection accuracy. Our method employs the Dempster–Shafer combination rule for aggregating probabilities for comments being fake from two different social media platforms. Instead of directly using the comments as features, our approach improves fake news detection by examining the relationships and calculating correlations among comments from different platforms. This provides a more comprehensive view of how fake news spreads and how users respond to it. Most importantly, our study reveals that true news is typically rich in content, while fake news tends to generate a vast thread of comments. Therefore, we propose a combined method that merges content- and comment-based approaches, allowing our model to identify fake news with greater accuracy and showing an overall improvement of 7% over previous methods.

Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among U.S. young adults

In this paper, we examine how multiple social media platforms are being used for formal and informal learning by examining data from two connectivist MOOCs (or cMOOCs). Our overarching goal is to develop and evaluate methods for learning analytics to detect and study collaborative learning processes. For this paper, we focus on how to link multiple online identities of learners and their contributions across several social media platforms in order to study their learning behaviours in open online environments. Many challenges were found in collection, processing, and analyzing the data; results are presented here to provide others with insight into such issues for examining data across multiple open media platforms.

In 2016, the online cause #Mission22 went viral on social media. Established to raise awareness about high suicide rates among US military veterans, the campaign involves users posting a video of themselves doing 22 push-ups for 22 days, and on some platforms, to donate and recruit others to do the same. Based on a ‘big data’ analysis of Twitter data (over 225,883 unique tweets) during the height of the campaign, this article uses #Mission22 as a site in which to analyse how people depict, self-represent and self-tell as moral subjects using social media campaigns. In addition to spotlighting how such movements are mobilised to portray moral selves in particular ways, the analysis focuses on how a specific online cause like #Mission22 becomes popularly supported from a plethora of possible causes and how this selection and support is shaped by online networks. We speculate that part of the reason why Mission22 went ‘viral’ in the highly competitive attention economies of social media environments was related to visual depictions of affective bodily, fitness and moral practices.

BackgroundThe COVID-19 pandemic has potentially had a negative impact on the mental health and well-being of individuals and families. Anxiety levels and risk factors within particular populations are poorly described.ObjectiveThis study aims to evaluate confidence, understanding, trust, concerns, and levels of anxiety during the COVID-19 pandemic in the general population and assess risk factors for increased anxiety.MethodsWe launched a cross-sectional online survey of a large Russian population between April 6 and 15, 2020, using multiple social media platforms. A set of questions targeted confidence, understanding, trust, and concerns in respondents. The State-Trait Anxiety Inventory was used to measure anxiety. Multiple linear regressions were used to model predictors of COVID-19–related anxiety.ResultsThe survey was completed by 23,756 out of 53,966 (44.0% response rate) unique visitors; of which, 21,364 were residing in 62 areas of Russia. State Anxiety Scale (S-Anxiety) scores were higher than Trait Anxiety Scale scores across all regions of Russia (median S-Anxiety score 52, IQR 44-60), exceeding published norms. Time spent following news on COVID-19 was strongly associated with an increased S-Anxiety adjusted for baseline anxiety level. One to two hours spent reading COVID-19 news was associated with a 5.46 (95% CI 5.03-5.90) point difference, 2-3 hours with a 7.06 (95% CI 6.37-7.74) point difference, and more than three hours with an 8.65 (95% CI 7.82-9.47) point difference, all compared to less than 30 minutes per day. Job loss during the pandemic was another important factor associated with higher S-Anxiety scores (3.95, 95% CI 3.31-4.58). Despite survey respondents reporting high confidence in information regarding COVID-19 as well as an understanding of health care guidance, they reported low overall trust in state and local authorities, and perception of country readiness.ConclusionsAmong Russian respondents from multiple social media platforms, there was evidence of higher levels of state anxiety associated with recent job loss and increased news consumption, as well as lower than expected trust in government agencies. These findings can help inform the development of key public health messages to help reduce anxiety and raise perceived trust in governmental response to this current national emergency. Using a similar methodology, comparative surveys are ongoing in other national populations.

Understanding what attracts users to engage with social media content (i.e. reply-to, share, favourite) is important in domains such as market analytics, advertising, and community management. To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics, using replying as our chosen engagement modality, across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison.Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.

Despite increasing awareness and research about online strategic information operations, there remain gaps in our understanding, including how information operations leverage the wider information ecosystem and take shape on and across multiple social media platforms. In this paper we use mixed methods, including digital trace ethnography, to look beyond a single social media platform to the broader information ecosystem. We aim to understand how multiple social media platforms are used, in parallel and complementary ways, to achieve the strategic goals of online information operations. We focus on a specific case study: the contested online conversation surrounding Syria Civil Defense (the White Helmets), a group of first responders that assists civilians affected by the civil war within the country. Our findings reveal a network of social media platforms from which content is produced, stored, and integrated into the Twitter conversation. We highlight specific activities that sustain the strategic narratives and attempt to influence the media agenda. And we note that underpinning these efforts is the work of resilience-building: the use of alternative (non-mainstream) platforms to counter perceived threats of 'censorship' by large, established social media platforms. We end by discussing the implications on social media platform policy.

Within a globalised digital environment characterised by increasingly diverse and dynamic social media platforms, video creators and their content production and circulation now typically operate across multiple social media platforms. Focusing on Chinese content creators and their cross-platform and cross-cultural social media practices, this paper draws on digital ethnographic research to analyse how user-generated content and creator identities are constructed across Chinese and Western social media services including YouTube, Bilibili, Douyin and RED. This article asks: how do Chinese content creators produce and circulate videos across multiple social media platforms and diverse cultures? How do these creators navigate platform architectures to present, manage and commercialise their identity given the cross-platform and transnational context? The findings suggest that Chinese creators’ cross-platform practices can be seen as a form of platform migration, in which they learn to move within and across platforms to ensure they create the optimal conditions for their content to spread and be viewed. These migratory platform practices are, however, constrained by audiences, algorithms, and advertiser expectations for creators to construct and maintain a single and consistent creator identity. These transnational creator identities include elements of both novelty and normativity in video content, such as niche or exotic performances, which serve up content for negotiating algorithmic visibility, or negotiating audiences for achieving a “cosmopolitan Chineseness”. As such, we can see that creator identities are both afforded and shaped through the globalised cultures, economies and politics of online video-sharing platforms.

In today 's time social media platforms have taken over our lives. The number of people using these platforms keeps on increasing day by day. With the increase of social media usage, the person who is using these platforms become more exposed to the negative effects of using social media. Among many negative effects, cyberbullying is one of the major negative effects of using social media. People online get bullied which affects their mental health in a negative manner. It is an incredibly difficult task to detect cyberbullying on social media platforms especially due to the slangs that people make up regularly but even so this paper suggests a working implementation of an application that detects cyberbullying across multiple social media platforms using the data provided by Twitter, Wikipedia and Formspring. This paper makes use of Deep Learning for the purpose of detecting cyberbullying.

Social media connects individuals to on-line communities through a variety of platforms, which are partially funded by commercial marketing and product advertisements. A recent study reported that 92% of businesses rated social media marketing as very important. Accurately linking the identity of users across various social media platforms has several applications viz. marketing strategy, friend suggestions, multi platform user behavior, information verification etc. We propose LINKSOCIAL, a large-scale, scalable, and efficient system to link social media profiles. Unlike most previous research that focuses mostly on pair-wise linking (e.g., Facebook profiles paired to Twitter profiles), we focus on linking across multiple social media platforms. L INK S OCIAL has three steps: (1) extract features from user profiles and build a cost function, (2) use Stochastic Gradient Descent to calculate feature weights, and (3) perform pair-wise and multi-platform linking of user profiles. To reduce the cost of computation, L INK S OCIAL uses clustering to perform candidate pair selection. Our experiments show that L INK S OCIAL predicts with 92% accuracy on pair-wise and 74% on multi-platform linking of three well-known social media platforms. Data used in our approach will be available at http://vishalshar.github.io/data/.

PurposeWith the growing trend of omni-channel marketing, brands are increasingly looking to offer a seamless experience to their online fan base by connecting with them across multiple social media platforms. This paper explores the relationship between brand posts' characteristics and popularity for start-ups across four different social media platforms: Facebook, Twitter, Instagram and LinkedIn.Design/methodology/approachA total of 1,200 social media posts from 10 start-ups were subjected to content analysis. Regression analysis was employed with brand posts' popularity (likes, comments and shares/retweets) as the dependent variable.FindingsThe results reveal several nuances in brand post popularity for start-ups across Facebook, Twitter, Instagram and LinkedIn. Antecedents of the popularity measures of likes, comments and shares/retweets also fared differently.Originality/valueThe paper reports one of the earliest empirical studies to better understand how the qualities of brand posts are related to their appeal across multiple social media platforms. It advances the literature on social media marketing and offers insights to social media managers of brands, particularly start-ups, on how to offer smoother customer journeys across numerous digital touchpoints.

The ability to compose emerging topics from the data collected from multiple social media platforms can help individuals and organisations meet their business goals and improve decision-making, as such information can provide more complete and accurate information. However, existing research has mainly focused on analysing emerging topics from the posts and related data collected from a single social media platform. In this paper, we propose a framework referred to as Multi-source Social Topic Media Analysis (xSMA) framework to model, rank and semantically analyse emerging topics across various social media platforms. The implementation and evaluation of the xSMA framework using real-world datasets obtained from Twitter and Reddit are also described.

Purpose: Overwhelmed by the huge rise in the number of social media (SM) platforms, B to B firms have been increasingly using multiple social media (SM) platforms to enhance their relationships with their customers. The purpose of this study is to investigate the influence of the competitive pressure to use SM on B to B firms use of multiple SM platforms, organization and individual SM competences and on relationship sales performance. Method: An online survey is implemented to collect data from B to B firms from different industries in an emerging market, i.e. Kuwait, to produce 152 usable questionnaires. Structural equation modeling is carried out using Smart PLS 3. Findings: The main findings show that competitive pressure to use SM fully influences relationship sales performance through individual social media competence. It also influences relationship sales performance through two mediations (1) organizational SM competence, (2) on a less important level, through the use of multiple SM platforms and organizational SM competence. Additionally, both organization and individual SM competence are found to significantly influence relationship sales performance. Implications: This study uncovers the complex mechanism through which competitive pressures to use social media influence both individual and organization social media competence and their relationship with their customers. It demonstrates that the use of multiple SM platforms significantly increases relationship sales performance, but this influence is weak. Therefore, top managers must choose the right number of SM platforms and design clear SM strategies. Originality: This study sheds light on the influence of competitive pressure to use SM on B to B firms’ relationships with their customers i.e. relationship sales performance. This coercive pressure could potentially spread B to B firms’ resources over a large number of SM and lead to poor SM presence. The study also emphasizes the role of top management in choosing the optimal combination of SM platforms and developing their organization SM competence.

Purpose This study aims to explore the impact of firm-generated content (FGC) on viral marketing on multiple social media platforms, and how social ties embedded in different social media platforms affect the motives for social dissemination. Design/methodology/approach Three studies were conducted to test the model. A quasi-field experiment (Study 1) supported this main effect. Studies 2 and 3 examined the underlying mechanism and enhanced the internal and external validity of the findings. Findings The findings revealed that warmth (vs competence)-oriented FGC is consistent with the communion (vs agency) mode and elicits greater social dissemination on social media embedded with strong (vs weak) ties. Practical implications This study illustrates that FGC that matches communication modes on multiple social media platforms embedded with different social ties will trigger viral marketing and being aware of this match is crucial for policymakers. Originality/value This research sheds light on the effects of FGC on viral marketing on multiple social media platforms embedded in different social ties.