Social Media Activity Research Articles

Organizational communication for social change on social media: NPOs’ social media strategies based on their perception of three stakeholder networks in collective and connective action

Abstract Social media transform and complicate nonprofit organizations’ (NPOs) traditional communication to engage and lead stakeholders for collective action. Stakeholders can self-organize for connective action on social media and form stakeholder networks of varied potential and structures that NPOs may leverage for collective goals. Facing such networks, NPOs may communicate in diverse ways to accommodate and take advantage of the diverse stakeholder networks. Through two surveys conducted with 375 (Study 1) and 74 (Study 2) NPOs, this study suggests that NPOs diversify their communication to leverage different potential from each stakeholder network by crafting different types of social media activities based on their perception of different networks. This study develops a comprehensive theoretical understanding of NPOs’ transformed and complex communication strategies for stakeholder engagement in driving social change, opening up new avenues for organizational communication and collective and connective action scholarship.

Pricing dynamics and herding behaviour of NFTs

AbstractThis paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a “superstar effect”, with a few artists dominating sales, and herding behaviour within the NFT market.

Employee Branding Thru Social Media

Developing a distinct Employer brand sets an Employer apart from its rivals and other companies competing for the same talent pool. Most businesses now utilize internet media significantly in the field of brand development. Branding is a marketing term that is applied to organizations, which are seen as goods with distinct characteristics. Jobseekers collect information from organizations’ online platforms as well as informal feedback from other stakeholders. The investigation includes not only their online presence and how they are portrayed, but also the degree of engagement of their activities in various social media platforms (e.g. Face book, YouTube, LinkedIn, Twitter). Although the research that mostly focused on The analysis includes not only their online presence and how these businesses are presented, but also the level of interactivity of their activities in various social media (e.g., Facebook, YouTube, LinkedIn, and Twitter), as well as a description of the Measurement Model Assessment, social media factor with all items displayed. Fit Indices for SMM Model (CFI)

Predicting social media users' indirect aggression through pre-trained models.

Indirect aggression has become a prevalent phenomenon that erodes the social media environment. Due to the expense and the difficulty in determining objectively what constitutes indirect aggression, the traditional self-reporting questionnaire is hard to be employed in the current cyber area. In this study, we present a model for predicting indirect aggression online based on pre-trained models. Building on Weibo users' social media activities, we constructed basic, dynamic, and content features and classified indirect aggression into three subtypes: social exclusion, malicious humour, and guilt induction. We then built the prediction model by combining it with large-scale pre-trained models. The empirical evidence shows that this prediction model (ERNIE) outperforms the pre-trained models and predicts indirect aggression online much better than the models without extra pre-trained information. This study offers a practical model to predict users' indirect aggression. Furthermore, this work contributes to a better understanding of indirect aggression behaviors and can support social media platforms' organization and management.

Analyzing COVID-19 Sentiments on Twitter: An Effective Machine Learning Approach

The COVID-19 pandemic has brought about a surge in online discussions and social media activity, making it crucial to analyze public sentiment towards the virus and related topics. This thesis focuses on Sentiment Analysis of COVID-19 data on Twitter, employing Multinomial Logistic Regression as the primary classification algorithm. This research explores Sentiment Analysis of COVID-19 data on Twitter using Multinomial Logistic Regression. It constructs a tweet dataset reflecting various sentiments—positive, negative, and neutral. The data undergoes preprocessing, and a Sentiment Analysis model is built, with 70% of data for training and 30% for testing. The model uses Count-Vectorizer, Tf-idf for feature extraction, and Multinomial Logistic Regression to classify tweets. The study achieves state-of-the-art results with a high accuracy of 95.14%, demonstrating the effectiveness of this approach. The results offer valuable insights into public sentiment during crises, aiding in decision-making and communication strategies.

Emotional Digital Labor Among Young People Within the Context of Lumpencybertariat

Türkiye ranks among the countries with the highest rates of social media usage. Especially among the young population, the prevalence of social media usage reaches the highest rates. This article focuses on the formation of hate within the cultural production processes on social media among young population. The monopolization of social media platforms fosters an economic logic where hateful interactions gain exchange value within algorithmic distribution systems. The online activities of social media users are at risk of being drawn into a “discursive spiral of hate”. Algorithms direct social media activities towards emotional escalation and transform online interactions into the “emotional digital labor”. Thus, the digital cultural production is tended to be dominated by the “lumpencybertariat”. This lumpenization process poses a direct risk to young social media users. This article aims to outline the conceptual framework of the position of lumpencybertariat as the producer of emotional digital labor in the digital capitalism. The possibility of “emotional sustainability” as a potential tool to counter the risk of lumpenization in digital cultural production is discussed.

“We’re Not Lab Rats”: Analysis of Netizens’ Response to BCG Vaccine-Test Proposal Against COVID-19 in Africa

In April 2020, two French scientists proposed Africa as the site for testing a BCG tuberculosis vaccine for treating COVID-19. This sparked social media outrage with some describing the gaffe as a hangover of colonial mentality. This study examined netizens’ interpretation of the faux pas and how the social media activism that followed it shaped the narrative about Africa’s identity. The study found that netizens interpreted the discourse as racism against Africa, colonialism, and a breach of medical ethics. These created an avenue for social media activism by a new generation of African netizens to assert their independence, protect their humanity, and promote the idea of Africa for Africans. The study recommends the need for ethics of care and racial sensitivity in public and global health discourses that have the potential to reopen scars of historical trauma in subjugated populations who may be geographically or socially affected by pandemics.

Leveraging social media data for pandemic detection and prediction

Governments and healthcare institutions are increasingly recognizing the value of leveraging social media data to address disease outbreaks. This is due to the rapid dissemination and rich content of social media data, which includes real-time reactions and calls for help from people. However, current research on which social media data can be utilized for information support, as well as the underlying reasons why social media data can be utilized for information support, remains limited. This study aims to make up for the aforementioned limitation by investigating which social media information is more likely to reflect the severity of an outbreak through empirical and prediction models, while also elucidating why social media data has the ability to reflect pandemic through content analysis. The COVID-19 outbreak was utilized as a case example in this study because it has the advantage of enhancing the universality of results and promoting the validation of the model with multiple waves of data. The empirical model results indicate that social media activity from public users is more likely to reflect the ground truth during pandemic. In particular, it was found that negative sentiment expressed in blog posts by public users during pandemic aligns more closely with the severity of disease outbreak. Then, a prediction model was proposed to further validate these findings of the empirical model. Finally, a content analysis was conducted based on the conclusions drawn from empirical model and prediction model. The content analysis revealed that the predictive capability of social media data for pandemic originates from individual self-reporting of illness. This study provides contributions and insights into which types of information can be used for pandemic monitoring and forecasting. The findings of our research have significant implications for governments and healthcare institutions in leveraging social media data for pandemic monitoring and forecasting.

Schoolfeeds: A study of principals’ governance of school social media pages and students’ data, privacy and treatment

It is common for contemporary schools to have a presence on social media for the purpose of connecting with the school community and often the wider public. Drawing on the perspectives of five Australian non-government principals, the study explored why schools engaged with social media and the approaches used to manage the school's social media activities. We found that the main perceived value was in marketing the school to prospective families, which required active curation of posts and comments to ensure a positive image was presented and maintained. We further found that principals consider social media platforms to be neutral mechanisms for communication, not dissimilar to previous forms of communication long used by schools to engage with their communities. When viewed through a data justice lens, we argue that these views result in unintended consequences by presenting an image of the school that does not represent the diversity of its students and activities and by perpetuating the exploitative practices of commercial platforms that commodify students’ data. There are also missed opportunities for schools to better promote their educational goals and activate students as agents in representing themselves online and developing their data literacy. We propose that by adopting the term ‘schoolfeeds’ we can promote a more critical engagement with school social media that can lead to better policy and practice.

Adverse weather amplifies social media activity and worsens online sentiment: Evidence from over 10 billion social media posts

Adverse weather amplifies social media activity and worsens online sentiment: Evidence from over 10 billion social media posts

Does ostracism/rejection impact self-disclosures? Examining the appeal of perceived social affordances after social threat

Abstract Two studies examine how experiencing a social need threat (ostracism and rejection) impacts subsequent preferences for self-disclosure to various digital audiences. Findings consider how contextual/situational factors like need threats may impact the appeal of two established perceived social affordances of media: personalization and privacy/visibility. Participants took part in a (bogus) social media activity to elicit feelings of inclusion/ostracization/rejection and then were asked about sharing their media preferences with various potential audiences. Results show that social need threats have no significant impact on privacy preferences but do affect preferences for sharing with some audiences and not others. Notably, ostracized and rejected participants show different patterns of preferences, suggesting these forms of social need threat may have distinct impacts on future self-disclosures. Implications for online relationship development and community building are considered in the discussion.

An Investigation into Social Media Utilisation in Small and Medium-Sized Enterprises and its Effect on Business Performance: A Case Study of Kabwata Township, Lusaka

This study investigated the impact of social media on Small and Medium-Sized Enterprises (SMEs) in Kabwata Township, focusing on the types of social media used, its impact on brand visibility, customer acquisition, and the influence of social media marketing strategies on sales revenue and profitability. A mixed methods approach was employed, integrating quantitative surveys and qualitative interviews to provide a comprehensive understanding of SMEs' social media practices. The study targeted SMEs actively using social media in Kabwata. 100% response rate was achieved from a sample of 133 SMEs for quantitative data and additional insights from 20 SMEs for qualitative data. The results indicated that platforms like Facebook, Instagram, X (formerly known as Twitter), and LinkedIn significantly enhanced brand visibility and customer engagement. However, the choice of platform alone did not predict success; strategic implementation was crucial. Significant correlations and regression coefficients demonstrated the positive impact of social media marketing on SME success, though with a modest effect on sales revenue. Recommendations emphasized aligning social media activities with business objectives to maximize benefits.

Identification of Expected Growth in Crypto Currency

Identifying the expected growth in cryptocurrency involves analyzing a combination of market trends, technological advancements, regulatory developments, and economic indicators. Historical performance and adoption rates of major cryptocurrencies provide insight into market trends, while innovations in blockchain technology, such as Ethereum 2.0 and Layer 2 solutions, along with the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), highlight significant technological advancements. Regulatory developments, including supportive legislation and the involvement of institutional investors through financial products like Bitcoin ETFs, play a crucial role in shaping market confidence and investment. Economic indicators, such as inflation, monetary policies, and global events, also influence interest in cryptocurrencies as alternative assets. Investor sentiment, driven by public perception, media coverage, and social media activity, impacts market dynamics. Additionally, research from financial analysts, market research firms, and academic studies, along with corporate partnerships and the integration of crypto solutions with traditional systems, contribute to growth predictions. Monitoring market capitalization and trading volumes further helps gauge market interest and liquidity. By considering these multifaceted factors, a more comprehensive understanding of the potential growth in the cryptocurrency market can be achieved.

Social Media Activities and Affective Well-being in the Daily Life of Emerging Adults

Social Media Activities and Affective Well-being in the Daily Life of Emerging Adults

Social Media Activation Strategy pada Produk Olahan Kelapa Kopyor “Kosudu”

Coconut is a prospective plantation commodity. Many coconuts are further processed into various processed products, one of which is frozen coconut kopyor. This research aims to determine the implementation of brand activation and determine which social media activation strategy to be implemented in "Kosudu" frozen kopyor coconut. This research was designed with a case study approach in the Unit Usaha Kopyor Bogor. The informants and respondents were chosen through purposive sampling with criteria. The informants were 7 employees who provided information regarding “Kosudu”, while the respondents were 10 consumers who purchased the product. The analysis used in this research is SWOT and QSPM analysis. The research results show the implementation of the "Kosudu" social media activation, there are 8 activities in social media. The brand activation strategy for the Kopyor Bogor Business Unit is (1) Video advertisement (2) User Generated Content (3) Live dan post feed (4) Podcast.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

How Crises Shape Interest in Elected Officials of Color: Social Media Activity, Race and Responsiveness to Members of Congress on Twitter

ABSTRACT This study aims to understand public reactions to statements about racial discrimination made by elected officials of color on Twitter. We argue that statements about race made by elected officials of color will be overlooked in most political contexts. However, during events when race is made a salient issue, the public will look to elected officials of the afflicted group for guidance and, through this, the public will be more likely to interact with these leaders on Twitter. In this way, periods of crises create a window of opportunity when statements against racism made by elected officials of color receive greater public attention. To assess this hypothesis, we use the case study of Asian American members of Congress and their discussion of anti-Asian racism on Twitter. We examine the pattern of Twitter interactions to their posts in 2021. We find that Asian American members of Congress receive more social media attention (likes and retweets) relative to other members of Congress when condemning anti-Asian violence, but only in the immediate wake of the March 2021 mass shooting of Asian Americans in Atlanta, Georgia. This study offers important new insight into when elected officials of color can draw public attention to the communities that they descriptively represent.

Dynamics between Bitcoin Market Trends and Social Media Activity

Dynamics between Bitcoin Market Trends and Social Media Activity

Analysis of Sarcasm Language Use on Instagram Social Media (Case Study of Students from the Faculty of Social Sciences UINSU)

This article aims to examine the dynamics of sarcasm language use by students of the Faculty of Social Sciences at the State Islamic University of North Sumatra (UINSU) in their interactions on Instagram social media. The urgency of this research arises from the observation that sarcasm, although often used as a communication tool, frequently leads to misinterpretations and interpersonal conflicts in the digital context that lacks non-verbal cues. This study adopts a qualitative case study approach with a constructivist paradigm, using thematic analysis techniques to interpret data obtained through in-depth interviews and observation of social media activities. Data validity testing was conducted through source triangulation to ensure the reliability of the findings. The research results indicate that students have significantly varied understandings of sarcasm, which is often misinterpreted as serious communication, leading to undesired emotional reactions. In the segment "Social Cognition: Student Poll on Sarcastic Comments on Instagram," it was found that although students could identify sarcasm, emotional reactions to sarcasm varied, indicating a need for higher digital literacy to effectively manage and process information in social interactions. The discussion in this article emphasizes that sarcasm on social media, while being a dynamic expression tool, requires deep contextual understanding to avoid misinterpretation. Therefore, enhancing media literacy is crucial to help students understand communication nuances and reduce the risk of misunderstandings in digital interactions. This study highlights the importance of effective digital literacy education, which not only enhances students' communication skills but also prepares them to navigate the complex and often ambiguous digital ecosystem.

New graduate nurses’ readiness for practice, transition and integration into the workplace: A longitudinal study with mixed methods research

AimTo examine the readiness of new graduate nurses for practice and evaluate factors influencing their transition into the workplace. BackgroundThis is new research in Bahrain. Understanding readiness for practice is important during a time when there is a national initiative to promote nursing as a career among the indigenous Bahraini population. The worldwide nursing shortages and aging workforce require new and innovative recruitment and retention measures. There is change, increased demand and complexity, in nursing and healthcare needs with accompanying healthcare specialization. In such a health care environment, the transitioning and readiness of newly qualified nurses is an increasing challenge. DesignA longitudinal design, with a triangulated, mixed methods, research approach was used. MethodsThe study population consisted of 50 newly graduated nurses. Data were collected over one year through self-administered questionnaires, reflective diaries and focus groups. Quantitative data were analysed using SPSS and qualitative data using thematic analysis with adherence to GRAMMS reporting guidelines. ResultsIn Bahrain, there are actions to recruit young Arabic students to nursing including schools and community presentations by trained nurse ambassadors, social media activities and career exhibitions, however there is a lack of objective information on the perspective of cohorts of nurses who have successfully completed graduate nursing registration programmes and transitioned into practice. International literature suggests that the transition of newly graduated nurses can be stressful and challenging. On entry to practice, the respondents positively perceived their characteristics and attributes; however, their confidence was affected by challenges in the workplace. The transitioning of the new graduate nurses was affected by the availability of support and leadership, induction programmes and the new complex work environment. ConclusionUnderstanding work readiness and the emotional needs of new graduate nurses, accompanied by improved support, will enhance the transition of newly qualified nurses into the workplace and reduce attrition. Study RegistrationKHUH/Research/No.279/2019