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Wang, Xinyi (2017) Privacy Concerns of Big Data in Social Network Industry. [Dissertation (University of Nottingham only)]

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Abstract

In the big data era, whether in the work, study or daily life, people are increasingly inseparable from the Internet. Among the various network sites, social networks are the most representative instance of Big Data due to its huge amounts of users and data generated. For example, the world’s largest social network platform Facebook has over 2000 million active users in 2017, and its users share 2.5 billion items per day. Big Data is valuable, and its substantial value can help companies better understand their customers, optimise their processes or make more business opportunities. Thus, companies prefer to collect and analyse their users’ data to utilise the value of the data. However, the Big Data value mining process is usually being seen as the threat of personal privacy, especially in social network platforms with a large amount of personal data. In order to understand people’s attitude about data collection by social network platforms and how people manage their privacy, this dissertation will use the Communication Privacy Management theory as the theoretical framework, and 15 respondents will be selected in this study to conduct 10 to 15 minutes face-to-face semi-structured interviews. As a result, this dissertation finds that most people have privacy awareness, and they are considerably tolerant of their data being collected by social networks. However, the users’ tolerance is because they trust the platforms they use. Once the trust is lost and their privacy is violated, people will strongly resist their sensitive data be abused, and they may do some radical behaviours to maintain their privacy rights. Thus, it is strongly recommended that social network platforms to seriously consider these user behaviours and do not abuse users’ data, in order to have harmonious interactions.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	WANG, Xinyi
Date Deposited:	10 Apr 2018 15:11
Last Modified:	24 Apr 2018 15:28
URI:	https://eprints.nottingham.ac.uk/id/eprint/46108

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