Browsing by Subject "Social media"
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- ItemInstagram vs. Reality: Conceptions of Authenticity Among Instagrams Micro-Influencers(2019) Connors, Noah E.; McKeever, MatthewSocial media micro-influencer marketing is a booming, multi-billion-dollar industry, and Instagram is, far and away, the platform of choice for marketers. Central to narratives advanced in popular media and advertising industry publications is the notion that influencers are effective at advertising products because they are considered ‘authentic’ in the eyes of their respective audiences. This thesis attempt to interrogate the concept of authenticity among Instagram influencers themselves, and asks: What is considered authentic among the micro-=influencers of Instagram, and what is its significance? Drawing upon previous research and interviews with five Instagram micro-influencers, I argue that authenticity is a fraught performance. I then suggest detail three ‘balancing acts’ central to micro-influencers’ strategies of performing authenticity: 1) a balancing act between back-stage practices and front-stage performance, in a dramaturgical sense; 2) a balancing act between practices of emulation and practices of distinction; and 3) a balancing act between relatability and marketability.
- ItemSocial Recommender systems: Improving recommendations through personalization(2011) Surti, Tanvi; Lindell, StevenThe vision for Web 3.0 (popularly referred to as the Semantic Web) is the ability to create meaning out of a deluge of qualitative data. This paper explores a very specific instance of the Semantic Web – Social Recommender Systems. This paper discusses the possibility of converting social crowdsourced data into quantitative information and using this information to power social recommendations. Over the course of this paper, we discuss five important recommender algorithms. This paper first outlines the importance of recommendations and elaborates the different types of recommendation algorithms used widely. We then discuss the potential to further personalize these recommendations by trying to identify user ‘taste’ by capitalizing on the social data available about the user. Next, we discuss the availability and applicability of data from social networks and how this data may be processed into quantitative input. This data is then used as input for two different social algorithms and their merits are discussed. And lastly this paper covers the topic of creating hybrid systems out of a wide range of recommendation algorithms so as to create social systems which are able to give diverse and personalized recommendations.