scholarly journals What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

2020 ◽  
Vol 12 (4) ◽  
pp. 1345 ◽  
Author(s):  
Youzhu Li ◽  
Xianghui Gao ◽  
Mingying Du ◽  
Rui He ◽  
Shanshan Yang ◽  
...  
Keyword(s):  
Social Network ◽  
Genetically Modified ◽  
The Body ◽  
Network Services ◽  
Genetically Modified Food ◽  
Genetic Traits ◽  
Gm Food ◽  
Social Network Services ◽  
The Government ◽  
Information Characteristics

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

Correcting Misperceptions About Genetically Modified Food on Social Media: Examining the Impact of Experts, Social Media Heuristics, and the Gateway Belief Model

2020 ◽  
pp. 107554702098137
Author(s):  
Leticia Bode ◽  
Emily K. Vraga ◽  
Melissa Tully
Keyword(s):  
Social Media ◽  
Food Safety ◽  
Genetically Modified ◽  
Human Consumption ◽  
Genetically Modified Food ◽  
The Public ◽  
Scientific Consensus ◽  
Gm Food ◽  
Belief Model ◽  
The Impact

We experimentally test whether expert organizations on social media can correct misperceptions of the scientific consensus on the safety of genetically modified (GM) food for human consumption, as well as what role social media cues, in the form of “likes,” play in that process. We find expert organizations highlighting scientific consensus on GM food safety reduces consensus misperceptions among the public, leading to lower GM misperceptions and boosting related consumption behaviors in line with the gateway belief model. Expert organizations’ credibility may increase as a result of correction, but popularity cues do not seem to affect misperceptions or credibility.

Debunking Misinformation About Genetically Modified Food Safety on Social Media: Can Heuristic Cues Mitigate Biased Assimilation?

2021 ◽  
pp. 107554702110220
Author(s):  
Yuan Wang
Keyword(s):  
Social Media ◽  
Food Safety ◽  
Experimental Design ◽  
Genetically Modified ◽  
Genetically Modified Food ◽  
Gm Food ◽  
Social Endorsement ◽  
Biased Assimilation ◽  
Source Cues

Focusing on debunking misinformation about genetically modified (GM) food safety in a social media context, this study examines whether source cues and social endorsement cues interact with individuals’ preexisting beliefs about GM food safety in influencing misinformation correction effectiveness. Using an experimental design, this study finds that providing corrective messages can effectively counteract the influence of misinformation, especially when the message is from an expert source and receives high social endorsements. Participants evaluate misinformation and corrective messages in a biased way that confirms their preexisting beliefs about GM food safety. However, their initial misperceptions can be reduced when receiving corrective messages.

Face hallucination and recognition in social network services

2014 ◽  
Vol 71 (6) ◽  
pp. 2035-2049 ◽  
Author(s):  
Feng Jiang ◽  
Seungmin Rho ◽  
Bo-Wei Chen ◽  
Xiaodan Du ◽  
Debin Zhao
Keyword(s):  
Social Network ◽  
Network Services ◽  
Face Hallucination ◽  
Social Network Services
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