This paper explores uses and gratifications of a content community on a social network service -a music video sharing group on Facebook. In a two-stage study, 20 users first generated words or phrases to describe how they used the group, and what they enjoyed about their use. These phrases were coded into 34 questionnaire items that were then completed by 57 new participants. Factor analysis on this data revealed four gratifications: contribution; discovery; social interaction and entertainment. These factors are interpreted and discussed, leading to design implications and guidelines aimed at informing the design of future online services that combine media sharing with social interaction to create online systems based on a rich and meaningful object-centered sociality.
In the digital era, we witness the increasing use of artificial intelligence (AI) to solve problems, while improving productivity and efficiency. Yet, inevitably costs are involved with delegating power to algorithmically based systems, some of whose workings are opaque and unobservable and thus termed the “black box”. Central to understanding the “black box” is to acknowledge that the algorithm is not mendaciously undertaking this action; it is simply using the recombination afforded to scaled computable machine learning algorithms. But an algorithm with arbitrary precision can easily reconstruct those characteristics and make life-changing decisions, particularly in financial services (credit scoring, risk assessment, etc.), and it could be difficult to reconstruct, if this was done in a fair manner reflecting the values of society. If we permit AI to make life-changing decisions, what are the opportunity costs, data trade-offs, and implications for social, economic, technical, legal, and environmental systems? We find that over 160 ethical AI principles exist, advocating organisations to act responsibly to avoid causing digital societal harms. This maelstrom of guidance, none of which is compulsory, serves to confuse, as opposed to guide. We need to think carefully about how we implement these algorithms, the delegation of decisions and data usage, in the absence of human oversight and AI governance. The paper seeks to harmonise and align approaches, illustrating the opportunities and threats of AI, while raising awareness of Corporate Digital Responsibility (CDR) as a potential collaborative mechanism to demystify governance complexity and to establish an equitable digital society.
Individuals share a diversity of content on social media for a variety of reasons. Research has often described and explained disclosure via the application of a subjective cost-benefit analysis framed around reciprocity, suggesting that people communicate selfishly motivated by the expectation of receiving something in return. This paper investigates the moderating effects of tie strength and interpersonal trust on the relationship between expected reciprocity and intensity of communication between two social media connections. A Facebook application presented participants with a random set of their friends and asked them to rate their friendships in terms of these values. Overall, 90 participants rated 1728 friendships, while the application collected 11 activity variables depicting the actual communication that has taken place in each pair of connections. A principal component analysis was used to distinguish between text-and photograph-related communication, and two moderated multiple regressions were conducted to establish the moderating effects. The results show significant moderating effects of tie strength and trust on the communication around photographs, but not around text. This study contributes to communication research by explicating the ways that tie strength and trust affect patterns of communication on social media. Implications for social media researchers and designers are discussed.
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