Number Of Reposts Research Articles

Social Media Marketing Strategies for Electronic Cigarettes: Content Analysis of Chinese Weibo Accounts.

E-cigarettes have gained popularity among teenagers due to extensive marketing strategies on social media platforms. This widespread promotion is a risk factor, as it fosters more positive attitudes toward e-cigarette use among teenagers and increases the perception that using e-cigarettes is normal. Therefore, the marketing of e-cigarettes on social media is a serious global health concern, and its strategies and impact should be clearly identified. This study examined how e-cigarette companies popularize their products via Weibo and identified the specific strategies influencing the effectiveness of their marketing. In phase 1, we conducted a search on Qcc.com and identified 32 e-cigarette brands with active Weibo accounts between October 1 and December 31, 2020, along with 863 Weibo posts. The data were investigated through content analysis. The codebook was developed into four categories: (1) product and features, (2) sales and promotions, (3) social contact and interaction, and (4) restrictions and warnings. To further understand the factors influencing e-cigarette brand marketing, we conducted a multiple linear regression analysis. Marketing tactics by e-cigarette companies on Chinese social media were documented, including emphasizing attractive product features, using trendy characters, implicit promotions, downplaying health concerns, and engaging with Weibo users in various ways. Out of 863 posts, 449 (52%) mentioned product characteristics. In 313 (36.3%) posts, visible figures were used to attract attention. Product promotion was absent in 762 (88.3%) posts, and purchase channels were not mentioned in 790 (98.3%) posts. Social interaction-related posts received attention (n=548, 63.5%), particularly those featuring hashtag content (n=538, 62.3%). Most posts did not include claims for restrictions on teenagers' purchases or use (n=687, 79.6%) or information on health warnings (n=839, 97.2%). Multiple linear regression analysis identified marketing strategies that effectively increase the exposure of e-cigarette posts on Weibo. Posts including engagement via posts encouraging reposts, comments, and likes (P<.001) and engagement topics related to e-cigarette brands were positively correlated with the number of reposts (P=.009). Posts highlighting nonmonetary incentives (P=.004), posts with age restriction statements (P<.001), engaging via stories and idea collection (P<.001), and engagement topics related to products (P<.001) and current affairs (P=.002) had a positive effect on the number of comments. Engagement topics related to brands (P<.001) or interactive sweepstakes (P<.001) had a positive effect on the number of likes. E-cigarette posts on Weibo that focus on product features and social interaction attract public attention, especially from teenagers. Stricter regulations and monitoring should be adopted to restrict the social media marketing of e-cigarettes.

In Punishment We Trust: Analyzing Characteristics and Credibility of Rumor-Debunking Messages on Chinese Social Media

Research on fact-checking journalism has predominantly centered on Western countries, often overlooking how distinct political media systems in non-Western countries might influence its practices and effectiveness. This article addresses this gap by focusing on government-led fact-checking in China, referred to as “rumor-debunking,” and we argue that compared to the Western mode of fact-checking, rumor debunking differs significantly with its unique motives, targets, and outcomes within the context of an authoritarian regime. Employing content analysis (Study 1) and a conjoint experiment (Study 2), we empirically examined the characteristics and credibility perceptions of rumor-debunking messages on Chinese social media Weibo during the COVID-19 pandemic. The findings of Study 1 reveal that rumor-debunking efforts in China were primarily driven by government-affiliated accounts and mainly targeting ordinary citizens. Study 2 shows that message specificity mattered in that detailing the rumors being debunked, providing evidence for the debunking, and including punitive actions against rumormongers significantly enhanced perceived credibility among the Chinese public. Additionally, we found messages that received higher social endorsement—reflected by a greater number of reposts, comments, and likes—boosted credibility. However, the presence of “recommended comments,” which indicate that the account holders curated the user comments under their debunking messages, did not affect credibility. The social and political implications of rumor-debunking in authoritarian regimes were also discussed.

How does information competition affect new product diffusion? Insights from computational experiments

This study conducts extensive computational experiments to analyze the effects of two factors governing information competition on diffusion outcomes and compares the performance of a decision rule (DR)-inspired seeding strategy with several traditional seeding strategies. The results show that as the probability of posting original information increases, the net present value (NPV) of diffusion shows an inverted U-shaped trend, as does the ratio of the NPV of the seeding strategy to that of no seeding strategy (NPVR). Second, as the average number of reposts per consumer increases, the NPV and NPVR for seeding strategies increase, but the growth rate decreases. Finally, DR strategies outperform traditional strategies. Based on these insights, we offer suggestions for social marketing campaigns that focus on selecting an appropriate information environment.

Examining regional variations in effective posts on X (Twitter) during the COVID-19 pandemic

Social media platforms play a significant role in disseminating information. The effectiveness of posts published on such platforms is often measured by the extent to which those posts are reshared — something that can be shaped by the regional social and cultural context. This study examines regional variations in effective posts (formerly tweets) published on X (formerly known as Twitter) during the initial phase of the COVID-19 pandemic, focusing on California’s two largest cities: Los Angeles and San Diego. By analysing a large dataset comprising the top 5,000 most reposted (formerly retweeted) posts from users in both cities, gathered between 17th March and 11th April, 2020, the research aims to identify key posters and content characteristics associated with higher repost rates. Employing techniques such as topic modelling and sentiment analysis, this study transforms unstructured text data into quantitative metrics in order to explore the relationship between content metrics and the number of reposts. The findings highlight the significant influence of local figures in shaping user engagement with shared posts spanning diverse topics and sentiments. These insights have significant implications for marketers, emphasising the importance of understanding local influencers, post topics and sentiment in geo-tagged posts. In regions with diverse populations, businesses can develop strategies to collaborate with local influencers and actively involve community leaders from various cultural backgrounds. By doing so, they facilitate the dissemination of trustworthy information and enhance engagement with culturally diverse audiences. Ultimately, this approach enables marketers to transform regional disparities into strategic advantages, fostering more effective communication and engagement on social media.

#SaveTheChildren: A pilot study of a social media movement co-opted by conspiracy theorists

In a preliminary analysis of 121,984 posts from X (formerly known as Twitter) containing the hashtag #SaveTheChildren, we found that conspiratorial posts received more engagement than authentic hashtag activism between January 2022 and March 2023. Conspiratorial posts received twice the number of reposts as non-conspiratorial content. Our initial findings of a forthcoming larger multi-platform study suggest that the way that users strategically exploit the #SaveTheChildren hashtag may have implications for the visibility of legitimate social movements on the X platform. Future work should consider other social media platforms to determine if the visibility of legitimate social movements is decreasing more broadly, particularly in the context of 2024 being the largest election year in history.

Analysis of Influencing Factors of Public Crowdfunding Based on Social Media- -Take “Water Drop Fund” as An Example

With the popularity of mobile phone clients and the rapid development of Internet, social media has infiltrated into people’s daily lives. In this context, China’s public crowdfunding has developed and grown, forming a unique operating mode under the impact of the times. Many public crowdfunding platforms based on social media such as Weibo and WeChat have emerged. “Water Drop Fund” is a typical new public welfare under the combination of social media and public welfare. By studying the “Water Drop Fund” platform, we can discover the epitome of this new form of public welfare under the context of the new era. This article takes “Water Drop Fund” as an example, collects relevant information about applicants on the public welfare platform, and uses regression analysis to describe the different influencing factors in this crowdfunding project. It summarizes the relationship between various factors and the crowdfunding rate, and further discovers that the target amount, the number of confirmations, and the number of reposts are the key factors affecting this new type of public welfare crowdfunding. Suggestions for the future development of the public welfare crowdfunding system are made based on these key factors.

ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ СОЦІАЛЬНИХ МЕРЕЖ НА ОСНОВІ НЕЙРОМЕРЕЖЕВОГО ПІДХОДУ

The article presents a predictive model for analyzing the state and behavior of social network objects in dynamics and for building predictions of user behavior. This opens up opportunities for choosing the most promising promotion of objects in social networks, the best placement of advertising, the possibility of creating independent projects. The article proposes an approach to the formalization of the processes of growth/decrease in the popularity of content under the influence of external factors within the framework of a mathematical predictive model based on a recurrent neural network. The purpose of the model is mathematical processing and analysis of stochastic information flows, graphical presentation of forecasts of music trends, changes in the popularity of bloggers, video content, and more. The developed model can be used to predict the growth of reposts in social networks. The list of external and internal variables that affect the behavior of the output data at each moment of time is described. The behavior of time series is studied on the basis of input data obtained from social networks (nomenclature of indicators, number of reposts of researched objects, simulation period). A feature of the model is the ability to work with Big Data arrays, which in most cases have incomplete or "noisy" information. The dependence between input and output is proposed to be specified using a system of constraints and external coefficients that change over time. Control parameters are predictors of the model. Features of building a predictive model are shown, a list of the main limitations of the mathematical model is described. The result of the implementation of the tasks set in the research is a dynamic predictive model that provides an opportunity to create predictions based on such indicators as reposts on social networks, music trends, and the popularity of bloggers. It is proposed to use a recurrent artificial network with a dynamic neuron to predict reposts on selected research objects. The neuron processes input signals and calculates a predictive indicator of the number of reposts. The learning process of the neural network during the simulation period of one month is demonstrated. To reveal the prospects of further development of the model in order to improve the accuracy of forecasts of socio-economic processes.

The Influence of the Debunker's Identity and Emotional Expression on the Sharing Behavior of Debunking Information.

Owing to the proliferation of rumors on social media, it is necessary to disseminate debunking information to minimize the harm caused by them. Using content analysis, sentiment analysis, and regression analysis, this study examined the mediating role of follower count in the relationship between the debunker’s identity and sharing behavior, and it explored the relationship between the text sentiment of debunking information and sharing behavior based on data on the spread of three rumors that circulated extensively on social media. Using an ordinary account as a reference, we found that the mediating or suppression effect (i.e., when direct and indirect effects are significant and opposite) of follower count in the relationship between debunker’s identity (celebrity, media, or government) and sharing behavior was significant. The three test identities (celebrity, media, and government) had more followers than the ordinary account, which resulted in a significant positive effect on the number of reposts. The debunker’s identity did not have a positive effect on the sharing of debunking information when controlling for mediating variables. Debunking information with emotional overtones (positive or negative) was shared more widely compared with information with neutral emotions, and the dominant emotional polarity was different in the three different rumors. These findings can contribute to the generation of debunking information content, which can aid in the development of effective communication strategies and improvement in the efficiency of crisis management.

ФОРМУВАННЯ ЛОЯЛЬНОСТІ КЛІЄНТІВ ІЗ ВИКОРИСТАННЯМ ІНСТРУМЕНТІВ SMM НА ПРИКЛАДІ СТУДІЇ КРАСИ LIMA BEAUTY STUDIO

One of the critical components that build the competitive advantages of the enterprise is consumer loyalty. Consumer loyalty plays a strategic role in ensuring the competitive advantages of the enterprise, is an indicator of the ability to carry out innovative transformations not only in the field of production but also in the field of service. The effectiveness of the measures taken depends to a large extent on the chosen tools and methods of encouraging consumer commitment. Current trends in marketing are aimed at forming long-term relationships with market participants. The article considers the possibility of using Internet marketing tools in social networks on the example of a service company, examines the effectiveness of the chosen communication channel in terms of forming a loyal target audience. The publication examines the features of the social network Instagram as a communication platform for positioning and promoting goods and services, forming a personal brand and increasing customer loyalty. Particular attention is paid to the role of visual content, the advantages of nonverbal presentation of information, the specifics of interaction with the verbal component of SMM-text are considered. The object of the study was the social networks of the beauty studio "Lima Beauty Studio", which is successfully developing and offers a variety of content for your Instagram account. Features of effective SMM-text, forms and functions of the most common types of content, which helps to ensure customer loyalty, are highlighted. The effectiveness of the use of marketing tools in social networks is studied. It has been found that active interaction with the target audience and the use of influencer marketing tools promotes the beauty studio on the Internet, namely, customer loyalty and recognition among a large number of existing beauty studios. The survey shows that indicators of loyalty can be considered the presence of positive feedback, the number of subscribers, the number of likes and comments, the frequency of publications and the number of reposts, and the use of emojis and hashtags in creating content helps build trust, visibility and, consequently, effective interaction with the brand.

A comparative analysis for spatio-temporal spreading patterns of emergency news

Understanding the propagation characteristics of online emergency news communication is of great importance to guiding emergency management and supporting the dissemination of vital information. However, existing methods are limited to the analysis of the dissemination of online information pertaining to a specific disaster event. To study the quantification of the general spreading patterns and unique dynamic evolution of emergency-related information, we build a systematic, comprehensive evaluation framework and apply it to 81 million reposts from Sina Weibo, Chinese largest online microblogging platform, and perform a comparative analysis with four other types of online information (political, social, techs, and entertainment news). We find that the spreading of emergency news generally exhibits a shorter life cycle, a shorter active period, and fewer fluctuations in the aftermath of the peak than other types of news, while propagation is limited to a few steps from the source. Furthermore, compared with other types of news, fewer users tend to repost the same piece of news multiple times, while user influence (which depends on the number of fans) has the least impact on the number of reposts for news of emergencies. These comparative results provide insights that will be useful in the context of disaster relief, emergency management, and other communication path prediction applications.

Effect of anger, anxiety, and sadness on the propagation scale of social media posts after natural disasters

Social media is widely used for sharing disaster-related information following natural disasters. Drawing on negativity bias theory, integrated crisis mapping model, and arousal theory, this study characterized the emotional responses of the public and tested the way emotional factors and influential users (with high numbers of followers and activeness) affect the number of reposts. Results indicated that after unpredictable earthquakes, the public showed negative responses, and negativity bias theory manifested especially when the posts came from influential users. During a typhoon or earthquake, the number of reposts grew as the number of anger-related words in posts increased. Anxiety- and typhoon-related posts from users with high numbers of followers negatively affected the number of reposts, whereas sadness-related posts had contrasting effects. These findings can help emergency managers formulate proper emotional response strategies after various natural calamities and help researchers test the abovementioned theories or models using real-word data from social media.

On the Algorithms of Identifying Opinion Leaders in Social Networks

The present paper is dedicated to the algorithms of opinion leaders’ identification in social networks on the basis of social network theory and machine learning methods. The main applications of such algorithms for social network analysis are discussed, examples of existing opinion leaders search algorithms and their advantages and disadvantages are given. A modified algorithm for finding the most influential social network nodes, which takes into account not only the position of the node in the network, but also signs of its activity, such as the number of posts, the number of comments and the number of reposts is proposed. The suggested algorithm is more accurate than the algorithms, which are based only on the position of the node in the network. Limitations of using the algorithms as well as possible problems with machine learning application in the field of social network analysis are described.

Empirical Analysis of Relationship-based User Reposting Behavior on Microblog Network

Microblogs have become a significant online social service for information propagation. Compared with other SNS, a microblog makes use of a flexible unidirectional subscription structure to encourage users to get information from others. In this paper, the authors performed relationship-based large-scale statistics and analyzed long-term reposting behavior. The total number of reposts from a certain followee with a bidirectional or unidirectional relationship is power-law, as well as a reposting interval time. Statistics suggest the curve of bidirectional friends decays slower with a smaller slope, which implies the interactions between bidirectional friends are more stable and intensive. Moreover, reposts from bidirectional friends take approximate 55 percentages, although bidirectional relationships comprise only 33 percent of relationships. Furthermore, the authors investigated the impact of following relationships and found that users focus more on social influence and activity level for unidirectional friends; however, for bidirectional friends, interactions and closeness are more crucial.

The Predictive Power of Content and Temporal Features of Posts in Information Dissemination in Microblogging

Reposting and commenting are the key mechanisms for information diffusion in microblogging. This study investigated the effects of content- and temporal-related characteristics of microposts on the number of reposts and comments received. Using a case study approach, data were collected from the microblog of a corporate in the hospitality industry. For the content-related factors, the results suggest that the use of multimedia features (including emoticons and pictures) and originally written posts had positive effects on reposts and comments. Different thematic topics also generated varying numbers of reposts and comments. For the temporal-related factors, the hour of the day in which a post was posted apparently affected the number of reposts and comments received.

Exploring factors influencing Chinese user’s perceived credibility of health and safety information on Weibo

The spread of non-credible health and safety information on microblog sites may lead to serious consequences when people use such sites as the basis for critical decisions. This study investigated the factors influencing Chinese microblog users’ perception of the credibility of health and safety information. Credibility cues related to the source (source credentials), the message (claim extremity and claim type), and the distribution in personal networks (type of comments, source of comments, and the number of reposts of a message) were examined. Three experiments were conducted on a mocked up Weibo system with 80 participants. The results show that objective claims with low extremity increased perceived information credibility when the participants were highly involved with the issue and had enough prior knowledge. When the participants had insufficient prior knowledge, the source credentials positively influenced the information’s credibility. Negative comments from personal networks decreased perceived credibility significantly, and this effect was slightly more pronounced when the comments came from close friends. For credible information, a large number of reposts added to the credibility, whereas for less credible information, a large number of reposts may induce greater skepticism and decrease perceived credibility.

Micro-blog hot topics detection method based on user role orientation

To solve the low extraction efficiency for extracting hot topics in huge amounts of micro-blog data, a new topics detection method based on user role orientation was proposed. Firstly, some noise data of parts of users were filtered out by user role orientation. Secondly, the feature weight was calculated by the Term Frequency-Inverse Document Frequency( TFIDF) function combined with semantic similarity to reduce the error caused by semantic expression. Then, the improved Single-Pass clustering algorithm was used to extract the topics of micro-blog. Lastly, the heat evaluation of micro-blog topics was made according to the number of reposts and comments, thus the hot topics were found. The results show that the average missing rate and false detection rate respectively decrease by 12. 09% and 2. 37%, and further indicate the topic detection accuracy rate is effectively improved and the method is feasible.

Reality Check for the Chinese Microblog Space: A Random Sampling Approach

Chinese microblogs have drawn global attention to this online application’s potential impact on the country’s social and political environment. However, representative and reliable statistics on Chinese microbloggers are limited. Using a random sampling approach, this study collected Chinese microblog data from the service provider, analyzing the profile and the pattern of usage for 29,998 microblog accounts. From our analysis, 57.4% (95% CI 56.9%,58.0%) of the accounts’ timelines were empty. Among the 12,774 non-zero statuses samples, 86.9% (95% CI 86.2%,87.4%) did not make original post in a 7-day study period. By contrast, 0.51% (95% CI 0.4%,0.65%) wrote twenty or more original posts and 0.45% (95% CI 0.35%,0.60%) reposted more than 40 unique messages within the 7-day period. A small group of microbloggers created a majority of contents and drew other users’ attention. About 4.8% (95% CI 4.4%,5.2%) of the 12,774 users contributed more than 80% (95% CI,78.6%,80.3%) of the original posts and about 4.8% (95% CI 4.5%,5.2%) managed to create posts that were reposted or received comments at least once. Moreover, a regression analysis revealed that volume of followers is a key determinant of creating original microblog posts, reposting messages, being reposted, and receiving comments. Volume of friends is found to be linked only with the number of reposts. Gender differences and regional disparities in using microblogs in China are also observed.

Meme ranking to maximize posts virality in microblogging platforms

Microblogging is a modern communication paradigm in which users post bits of information, or “memes” as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a meme, it become visible to the user community. When a user finds a meme of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough the social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.