Understanding the power of opinion leaders’ influence on the diffusion process of popular mobile games: Travel Frog on Sina Weibo

Wang, Zongshui; Liu, Haiyan; Liu, Wei; Wang, Shouyang

doi:10.1016/j.chb.2020.106354

Cited by 52 publications

(44 citation statements)

References 56 publications

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“…Further, brands use social media to promote their products [26]. In this context, where the number of followers or likes determines the influence that a person receives, image, comment, or opinion [27], and where anyone can post their opinion freely publish through different types of eWOM as reviews, recommendations, and likes, anyone can become an OL, even in contexts where they are not an expert [28].…”

Section: Opinion Leadershipmentioning

confidence: 99%

“…On the other hand, Iyengar et al [3] found that OLs have more influence as they are heavy users of a specific product. Wang et al [26] stated that OLs are the most effective source of message dissemination. In the same direction, Weimann [22] showed that compared to no OLs, the OLs spread more brand information and news for followers to make decisions.…”

Section: Social Media Recommendation Facebook Experimentsmentioning

confidence: 99%

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The Influence of Opinion Leaders’ eWOM on Online Consumer Decisions: A Study on Social Influence

Tobón

García‐Madariaga

2021

JTAER

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Opinion leaders and eWOM are becoming two of the most effective ways to launch a brand on social media by creating viral marketing. However, how much influence does an opinion leader’s eWOM (OL eWOM) have on consumer purchasing decisions? This research looks at the role of OL eWOM as well as the effect of its valence and product type on the decision to buy or not buy from a realistic experimental online store design. In total, 300 undergraduate students were randomly assigned to one of five scenarios in a 22 experimental arrangement. Results show that OL eWOM influences consumer online decisions when purchasing experience-type goods and the valence of eWOM is positive. However, if we compare the OL eWOM with a control group, then OL eWOM does not have a significant influence. This research provides novel empirical evidence for the limited influence of OL in modeling shopping behaviors in e-commerce contexts.

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Section: Opinion Leadershipmentioning

confidence: 99%

Section: Social Media Recommendation Facebook Experimentsmentioning

confidence: 99%

The Influence of Opinion Leaders’ eWOM on Online Consumer Decisions: A Study on Social Influence

Tobón

García‐Madariaga

2021

JTAER

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“…Opinion leaders often showed negative emotions and played an important role in interpersonal communication on social media. People were more likely to change their attitude when they received information from the people they admired [ 81 ]. This was the main reason why the overall types of user’s emotion regarding food safety issues tended to be negative.…”

Section: Discussionmentioning

confidence: 99%

“…Opinion leaders mostly questioned and criticized the supervision work of food safety risks, and their emotion intensity was strong. Under the influence of the emotions of opinion leaders and stimulated by their own irrational needs, ordinary user’s emotions became intense [ 81 ].…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Xiong

2021

Healthcare

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Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it affects users’ emotions have not been fully studied. Therefore, from the perspective of communication and social psychology, this study uses the content analysis method to analyze factors affecting social media users’ emotions regarding food safety issues. In total, 371 tweet samples of genetically modified food security in Sina Weibo (similar to Twitter) were encoded, measured, and analyzed. The major findings are as follows: (1) Tweet account type, tweet topic, and emotion object were all significantly related to emotion type. Tweet depth and objectivity were both positively affected by emotion type, and objectivity had a greater impact. (2) Account type, tweet topic, and emotion object were all significantly related to emotion intensity. When the depths were the same, emotion intensity became stronger with the decrease in objectivity. (3) Account type, tweet topic, emotion object, and emotion type were all significantly related to a user’s emotion communication capacity. Tweet depth, objectivity, and user’s emotion intensity were positively correlated with emotion communication capacity. Positive emotions had stronger communication capacities than negative ones, which is not consistent with previous studies. These findings help us to understand both theoretically and practically the changes and dissemination of user’s emotions in a food safety and health information environment.

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“…The information of microblogs has strong timeliness and the content of information loses fresh over time [30], which makes difficult for people to continuously pay their attention on a given microblog. To test this phenomenon, according the classification of users in reference [31], the statistics of the number of retweets for a single microblog about eight classes of users at different times are shown in Figure 2 with the time going and tends to zero after published about 7 hours in a day, which implies that microblogs have strong timeliness in different time of a day.…”

Section: B the Event Attention Degreementioning

confidence: 99%

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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Sina Weibo has significantly impact on the information diffusion processes in many realworld social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events' influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and nonredundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.

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Understanding the power of opinion leaders’ influence on the diffusion process of popular mobile games: Travel Frog on Sina Weibo

Cited by 52 publications

References 56 publications

The Influence of Opinion Leaders’ eWOM on Online Consumer Decisions: A Study on Social Influence

The Influence of Opinion Leaders’ eWOM on Online Consumer Decisions: A Study on Social Influence

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

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