15,983 research outputs found
Recommendation, collaboration and social search
This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology.
Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly.
We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements
Finding Truth in Cause-Related Advertising: A Lexical Analysis of Brandsâ Health, Environment, and Social Justice Communications on Twitter
Consumers increasingly desire to make purchasing decisions based on factors such as health, the environment, and social justice. In response, there has been a commensurate rise in cause-related marketing to appeal to socially-conscious consumers. However, a lack of regulation and standardization makes it difficult for consumers to assess marketing claims; this is further complicated by social media, which firms use to cultivate a personality for their brand through frequent conversational messages. Yet, little empirical research has been done to explore the relationship between cause-related marketing messages on social media and the true cause alignment of brands. In this paper, we explore this by pairing the marketing messages from the Twitter accounts of over 1,000 brands with third-party ratings of each brand with respect to health, the environment, and social justice. Specifically, we perform text regression to predict each brandâs true rating in each dimension based on the lexical content of its tweets, and find significant held-out correlation on each task, suggesting that a brandâs alignment with a social cause can be somewhat reliably signaled through its Twitter communications â though the signal is weak in many cases. To aid in the identification of brands that engage in misleading cause-related communication as well as terms that more likely indicate insincerity, we propose a procedure to rank both brands and terms by their volume of âconflictingâ communications (i.e., âgreenwashingâ). We further explore how cause-related terms are used differently by brands that are strong vs. weak in actual alignment with the cause. The results provide insight into current practices in causerelated marketing in social media, and provide a framework for identifying and monitoring misleading communications. Together, they can be used to promote transparency in causerelated marketing in social media, better enabling brands to communicate authentic valuesbased policy decisions, and consumers to make socially responsible purchase decisions
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
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