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Human-Computer Interaction and the Web
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Helen Ashman, Declan Dagger, Tim Brailsford, James Goulding, Declan O’Sullivan, Jan-Felix Schmakeit, Vincent Wade
Many social network providers are moving toward this by lowering the barriers to interaction through “social plug-ins” or functional widgets that can be placed on any website to leverage some of the functionality of the source network (e.g., Twitter @anywhere and Facebook Like Button applications). Although their primary purpose is to drive more traffic to those networks, the spread of social interaction affordances across the web is gaining real traction (more than 1 million websites integrate with the Facebook platform*). Movements like the Federated Social Web† are addressing the technical and architectural challenges of a Social Web infrastructure by focusing on technologies to support and promote online identity and security, exchangeable and configurable profiles, definition, declaration and management of relationships, sharing of media and activity streams, messaging, indexing and searching, functional interoperability, and data portability. These enabling technologies will inevitably lead to a series of complex design challenges when building applications to leverage the capabilities and affordances of the Social Web.
Reliable Ad Hoc Smartphone Application Creation for End Users
Published in Georgios Kambourakis, Asaf Shabtai, Constantinos Kolias, Dimitrios Damopoulos, Intrusion Detection and Prevention for Mobile Ecosystems, 2017
Adwait Nadkarni, Akash Verma, Vasant Tendulkar, William Enck
As described previously, adversaries and misbehaving corporations generally gain access to personal user data and cookies through cross-site attacks. The Facebook “like” button described previously is a great example. That said, prior work has built models to prevent leakage of private data through cross-site attacks. For example, Bauer et al. [35] provide an information flow control model that tracks the flow of sensitive information in the Chromium web browser and prevents leakage of sensitive information. Yet, such systems are unavailable to users unless ported into the web browser (e.g., Chrome for mobile).
Mining analysis of customer perceived value of online customisation experience under social commerce
Published in Enterprise Information Systems, 2021
Xiangzhi Bu, Zhoucheng Huang, Quanwu Zhao
In order to promote communication among customers and form a social atmosphere, JD.com allows customers to comment under other customer reviews, and it also provides a ‘like’ button under customer reviews, which other customers can click. The more customers click the ‘like’ button, the higher the ranking of the related review. Sumner, Ruge-Jones, and Alcorn (2018) pointed out that ‘like’ button can be conceptualised as an important social cue that allows users to communicate and convey emotion to other online users. Users pressing the ‘like’ button generally hope to achieve relational facilitation, self-presentation, and metacommunication. On social media, most users are more willing to respond to other people’s content, rather than generating original content (Hampton et al. 2012), which makes the ‘like’ button an important communication tool for most online users. We believe it is meaningful to conduct text mining in customer reviews in the platform for studying CPV and social media in the Chinese context using this secondary consumer feedback (i.e., reviews of reviews).
Classifying user connections through social media avatars and users social activities: a case study in identifying sellers on social media
Published in Enterprise Information Systems, 2020
Yu Mao, Yifan Zhu, Yiping Liu, Qika Lin, Hao Lu, Fuquan Zhang
• The average number of retweets, likes and comments of one user: In spam detection, a common way to measure whether or not a user is real is to see if the tweets they send receive enough interaction with other users (Xu, Sun, and Javaid 2016). Therefore, the three common forms of microbloggingretweet, like and commentare valid indicators. Previous studies have indicated that the retweet has a potential relationship to the number of fans, the number of followers and the overall amount of tweets. The ‘Like’ button is considered to be a measure of user engagement because other users may participate in an evaluation of the post (Gerlitz and Helmond 2013). We adopted these factors as a means to identify user interactions. User categories related to ACG generally had higher values (especially the upper quartile) with regard to these indicators of interactions, suggesting a user’s reliance and active presence on social networks.