Tree decomposition based anomalous connected subgraph scanning for detecting and forecasting events in attributed social media networks

2020 ◽  
Vol 407 ◽  
pp. 83-93
Author(s):  
Minglai Shao ◽  
Peiyuan Sun ◽  
Jianxin Li ◽  
Qiben Yan ◽  
Zhirui Feng
Infoman s ◽  
2018 ◽  
Vol 12 (2) ◽  
pp. 115-124
Author(s):  
Yopi Hidayatul Akbar ◽  
Muhammad Agreindra Helmiawan

Social media is one of the information media that is currently widely used by several companies and personally to convey information, with the presence of social media companies no longer need to spread offers through print media, they can use information technology tools in this case social media to submit offers the products they sell to users globally through social media. This social media marketing technique is the process of reaching visits by internet users to certain sites or public attention through social media sites. Marketing activities using social media are usually centered on the efforts of a company to create content that attracts attention, thus encouraging readers to share the content through their social media networks. The application of the QMS method is certainly not only submitted through search engine webmasters, but also on a website keywords must be applied that relate to the contents of the website content, because with the keyword it will automatically attract visitors to the university website based on keyword phrases that they type in the search engine. With Search Media Marketing Technique (SMM) is one of the techniques that must be applied in conducting sales promotions, especially in car dealers in Bandung, it is considered important because each product requires price, feature and convenience socialization through social media so that sales traffic can increase. Each dealer should be able to apply the techniques of Social Media Marketing (SMM) well so that car sales can reach the expected target and provide profits for sales as car sellers in the field.


MedienJournal ◽  
2020 ◽  
Vol 44 (1) ◽  
pp. 41-54
Author(s):  
Isabell Koinig

The youth constitutes the largest user base of social media networks. While this generation has grown up in a digitally immersed environment, they are still not immune to the dangers the online space bears. Hence, maintaining their privacy is paramount. The present article presents a theoretical contribution, that is based on a review of relevant articles. It sets out to investigate the importance adolescents attribute to online privacy, which is likely to influence their willingness to disclose data. In line with a “new privacy paradox”, information disclosure is seen as unavoidable, given the centrality of social networks to adolescents’ lives. This goes hand in hand with individual privacy management. As individuals often lack knowledge as to how to protect their privacy, it is essential to educate the youth about their possibilities, equipping them with agency and self-responsibilization. This corresponds with a teen-centric approach to privacy as proposed by the TOSS framework.


MedienJournal ◽  
2020 ◽  
Vol 44 (1) ◽  
pp. 41-54
Author(s):  
Isabell Koinig

The youth constitutes the largest user base of social media networks. While this generation has grown up in a digitally immersed environment, they are still not immune to the dangers the online space bears. Hence, maintaining their privacy is paramount. The present article presents a theoretical contribution, that is based on a review of relevant articles. It sets out to investigate the importance adolescents attribute to online privacy, which is likely to influence their willingness to disclose data. In line with a “new privacy paradox”, information disclosure is seen as unavoidable, given the centrality of social networks to adolescents’ lives. This goes hand in hand with individual privacy management. As individuals often lack knowledge as to how to protect their privacy, it is essential to educate the youth about their possibilities, equipping them with agency and self-responsibilization. This corresponds with a teen-centric approach to privacy as proposed by the TOSS framework.


MIS Quarterly ◽  
2014 ◽  
Vol 38 (1) ◽  
pp. 274-304 ◽  
Author(s):  
Gerald C. Kane ◽  
◽  
Maryam Alavi ◽  
Giuseppe (Joe) Labianca ◽  
Stephen P. Borgatti ◽  
...  

2018 ◽  
Author(s):  
Annice Kim ◽  
Robert Chew ◽  
Michael Wenger ◽  
Margaret Cress ◽  
Thomas Bukowski ◽  
...  

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.


Author(s):  
Kathrin Eismann

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fu Jie Tey ◽  
Tin-Yu Wu ◽  
Chiao-Ling Lin ◽  
Jiann-Liang Chen

AbstractRecent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.


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