An Empirical Evaluation Of Social Influence Metrics

Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the perfor- mance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neigh- borhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade mea- sures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.Comment: 8 pages, 5 figure

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success

The expansion of the Internet and social media have triggered a differentiation of the word-of-mouth (WOM) concept, with consumer communication about brands and products now taking place in various settings and forms. Two important digital WOM types are microblogs and consumer reviews. To clarify their differential roles for product success, this study offers a theoretical framework of the influence of these two types of WOM, drawing from consumer information search theory and diffusion theory. The tests of the proposed framework use a longitudinal data set of video game sales and weekly information gathered from microblogs (i.e., over 13 million tweets from Twitter) and consumer reviews (i.e., more than 17,000 Amazon consumer reviews). Analyzing a system of equations provides evidence that the influence of microblogs and consumer reviews on new product success changes over time. Prior to launch, the volumes of microblogs and consumer reviews, together with advertising, represent primary sales drivers. After launch, the volume of microblogs is initially influential, then loses impact, whereas the impact of the volume of consumer reviews continues to grow. The valence of consumer reviews gains significance only near the end of the observation period, but the valence of microblogging is never influential

Literature review - Twitter as A Tool of Market Intelligence for Businesses: Sentiment analysis approach

Purpose As an emerging technology, sentiment analysis of Twitter has aroused interest in the field of business research. The thesis has three primary objectives. The first objective is to identify how businesses could utilize sentiment analysis of Twitter in their market intelligence functions. The second is to determine how sentiment analysis of Twitter compares to more traditional methods of market intelligence. Thirdly, this thesis aspires to bring technology-oriented discipline easier to approach for business researchers. Methodology The research method of this thesis is a literature review. The thesis revises prior published and peer-reviewed articles with a focus on sentiment analysis of Twitter and its applications to market intelligence. Findings There are three significant findings in this thesis. 1. Companies have utilized sentiment analysis for various purposes of market intelligence with encouraging results. 2. Sentiment analysis of Twitter has a variety of similarities with traditional market intelligence methods. In the future, it will be an auspicious technique for market intelligence as its accuracy is improved, and companies utilize it more frequently for practical purposes. 3. Even though Twitter sentiment analysis has raised plenty of interest, there is no clear research field within the business, and more specifically, market intelligence related literature. Future research For future research, this thesis provides a review of the possibilities and uses of Twitter sentiment analysis in the context of market intelligence. Its focus is to support especially business research. Reviewed literature illustrates that there are a large number of research avenues to be addressed in the future. The first objective for future research is to implement a more precise research field of business research. The second objective is to conduct more comparative studies between Twitter sentiment analysis and qualitative business research methods. Another intriguing research topic is Twitter sentiment analysis in the context of Finnish companies.Tutkielman tiivistelmätiedoissa näkyvä hyväksymisvuosi on 2019.The year of approval showing in the abstract of the thesis is 2019

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI