Assessing the Credibility of Cyber Adversaries

Online communications are ever increasing, and we are constantly faced with the challenge of whether online information is credible or not. Being able to assess the credibility of others was once the work solely of intelligence agencies. In the current times of disinformation and misinformation, understanding what we are reading and to who we are paying attention to is essential for us to make considered, informed, and accurate decisions, and it has become everyone’s business. This paper employs a literature review to examine the empirical evidence across online credibility, trust, deception, and fraud detection in an effort to consolidate this information to understand adversary online credibility – how do we know with whom we are conversing is who they say they are? Based on this review, we propose a model that includes examining information as well as user and interaction characteristics to best inform an assessment of online credibility. Limitations and future opportunities are highlighted

Dutch Journalism in the Digital Age

With an ever-growing supply of online sources, information to produce news stories seems to be one mouse click away. But in what way do Dutch journalists actually use computer-aided research tools? This article provides an inventory of the ways journalists use digital (re)sources and explores the differences between experts and novices. We applied a combined methodological approach by conducting an ethnographic study as well as a survey. Results show that Dutch journalists use relatively few digital tools to find online information. However, journalists who can be considered experts in the field of information retrieval use a wider range of search engines and techniques, arrive quicker at the angle to their story, and are better at finding information related to this angle. This allows them to spend more time on writing their news story. Novices are more dependent on the information provided by others

Recommender System Based on Expert and Item Category

The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique   2) the experts from the frequency of rating technique and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creatingThe objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating

Two Essays on Consumer-Generated Reviews: Reviewer Expertise and Mobile Reviews

Over the past few decades, the internet has risen to prominence, enabling consumers to not only quickly access large amounts of information, but also openly share content (e.g., blogs, videos, reviews) with a substantially large number of fellow consumers. Given the vast presence of consumers in the online space, it has become increasingly critical for marketers to better understand the way consumers share, and learn from, consumer-generated content, a research area known as electronic word-of-mouth. In this dissertation, I advance our understanding about the shared content generated by consumers on online review platforms. In Essay 1, I study why and how the expertise of consumers in generating reviews systematically shapes their rating evaluations and the downstream consequences this has on the aggregate valence metric. I theorize, and provide empirical evidence, that greater expertise in generating reviews leads to greater restraint from extremes in evaluations, which is driven by the number of attributes considered by reviewers. Further, I demonstrate two major consequences of this restraint-of-expertise effect. (i) Expert (vs. novice) reviewers have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) Experts systematically benefit and harm service providers with their ratings. For service providers that generally provide mediocre (excellent) experiences, experts assign significantly higher (lower) ratings than novices. Building on my investigation of expert reviewers, in Essay 2, I investigate the differential effects of generating reviews on mobile devices for expert and novice reviewers. I argue, based on Schema Theory, that expert and novice reviewers adopt different “strategies” in generating mobile reviews. Because of their review-writing experience, experts develop a review-writing schema, and compared to novices, place greater emphasis on the consistency of various review aspects, including emotionality of language and attribute coverage in their mobile reviews. Accordingly, although mobile (vs. desktop) reviews are shorter for both experts and novices, I show that experts (novice) generate mobile reviews that contain a slight (large) increase in emotional language and are more (less) attribute dense. Drawing on these findings, I advance managerial strategies for review platforms and service providers, and provide avenues for future research

Social Epistemology as a New Paradigm for Journalism and Media Studies

Journalism and media studies lack robust theoretical concepts for studying journalistic knowledge ‎generation. More specifically, conceptual challenges attend the emergence of big data and ‎algorithmic sources of journalistic knowledge. A family of frameworks apt to this challenge is ‎provided by “social epistemology”: a young philosophical field which regards society’s participation ‎in knowledge generation as inevitable. Social epistemology offers the best of both worlds for ‎journalists and media scholars: a thorough familiarity with biases and failures of obtaining ‎knowledge, and a strong orientation toward best practices in the realm of knowledge-acquisition ‎and truth-seeking. This paper articulates the lessons of social epistemology for two central nodes of ‎knowledge-acquisition in contemporary journalism: human-mediated knowledge and technology-‎mediated knowledge.

Harvesting Wisdom on Social Media for Business Decision Making

The proliferation of social media provides significant opportunities for organizations to obtain wisdom of the crowds (WOC)-type data for decision making. However, critical challenges associated with collecting such data exist. For example, the openness of social media tends to increase the possibility of social influence, which may diminish group diversity, one of the conditions of WOC. In this research-in-progress paper, a new social media data analytics framework is proposed. It is equipped with well-designed mechanisms (e.g., using different discussion processes to overcome social influence issues and boost social learning) to generate data and employs state-of-the-art big data technologies, e.g., Amazon EMR, for data processing and storage. Design science research methodology is used to develop the framework. This paper contributes to the WOC and social media adoption literature by providing a practical approach for organizations to effectively generate WOC-type data from social media to support their decision making

Human-in-the-Loop Learning From Crowdsourcing and Social Media

Computational social studies using public social media data have become more and more popular because of the large amount of user-generated data available. The richness of social media data, coupled with noise and subjectivity, raise significant challenges for computationally studying social issues in a feasible and scalable manner. Machine learning problems are, as a result, often subjective or ambiguous when humans are involved. That is, humans solving the same problems might come to legitimate but completely different conclusions, based on their personal experiences and beliefs. When building supervised learning models, particularly when using crowdsourced training data, multiple annotations per data item are usually reduced to a single label representing ground truth. This inevitably hides a rich source of diversity and subjectivity of opinions about the labels. Label distribution learning associates for each data item a probability distribution over the labels for that item, thus it can preserve diversities of opinions, beliefs, etc. that conventional learning hides or ignores. We propose a humans-in-the-loop learning framework to model and study large volumes of unlabeled subjective social media data with less human effort. We study various annotation tasks given to crowdsourced annotators and methods for aggregating their contributions in a manner that preserves subjectivity and disagreement. We introduce a strategy for learning label distributions with only five-to-ten labels per item by aggregating human-annotated labels over multiple, semantically related data items. We conduct experiments using our learning framework on data related to two subjective social issues (work and employment, and suicide prevention) that touch many people worldwide. Our methods can be applied to a broad variety of problems, particularly social problems. Our experimental results suggest that specific label aggregation methods can help provide reliable representative semantics at the population level

Leveraging Mixed Expertise in Crowdsourcing.

Crowdsourcing systems promise to leverage the "wisdom of crowds" to help solve many kinds of problems that are difficult to solve using only computers. Although a crowd of people inherently represents a diversity of skill levels, knowledge, and opinions, crowdsourcing system designers typically view this diversity as noise and effectively cancel it out by aggregating responses. However, we believe that by embracing crowd workers' diverse expertise levels, system designers can better leverage that knowledge to increase the wisdom of crowds. In this thesis, we propose solutions to a limitation of current crowdsourcing approaches: not accounting for a range of expertise levels in the crowd. The current body of work in crowdsourcing does not systematically examine this, suggesting that researchers may not believe the benefits of using mixed expertise warrants the complexities of supporting it. This thesis presents two systems, Escalier and Kurator, to show that leveraging mixed expertise is a worthwhile endeavor because it materially benefits system performance, at scale, for various types of problems. We also demonstrate an effective technique, called expertise layering, to incorporate mixed expertise into crowdsourcing systems. Finally, we show that leveraging mixed expertise enables researchers to use crowdsourcing to address new types of problems.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133307/1/afdavid_1.pd