A social network as information: The effect of system generated reports of connectedness on credibility on Twitter

Social media have gained increased usage rapidly for a variety of reasons. News and information is one such reason. The current study examines how system-generated cues available in social media impact perceptions of a source’s credibility. Participants were asked to view one of six mock Twitter.com pages that varied both the number of followers and the ratio between followers and follows on the page and report their perceived source credibility. Data indicate that curvilinear effects for number of followers exist, such that having too many or too few connections results in lower judgments of expertise and trustworthiness. Having a narrow gap between the number of followers and follows also led to increased judgments of competence. Implications of these findings are discussed, along with limitations of the current study and directions for future research.     

Social metacognition and the creation of correct,new ideas: A statistical discourse analysis of online mathematics discussions

During asynchronous, online mathematics discussions, new ideas and justifications (knowledge content) and evaluations and invitations to participate (social metacognition) can influence the likelihood of a correct, new idea (CNI) in the current message. Using statistical discourse analysis, we modeled 894 messages by 183 participants on 60 high school mathematics topics on a mathematics problem solving website not connected to any class or school. Results showed that CNIs, justifications, and social metacognition (correct evaluations and questions) in recent messages increased the likelihood of a CNI in the current message. Meanwhile, more experienced participants (who had posted more messages on the website) had more CNIs, and participants who initiated topics had fewer CNIs. Applied to practice, these results suggest that teachers can facilitate students’ creation of CNIs by encouraging them to justify their ideas, evaluate one another’s ideas carefully, and ask questions during online mathematics discussions.     

Text mining for market prediction: A systematic review

The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses. That is why a number of researchers have turned their full attention to the different aspects of this problem lately. However, there is no well-rounded theoretical and technical framework for approaching the problem to the best of our knowledge. We believe the existing lack of such clarity on the topic is due to its interdisciplinary nature that involves at its core both behavioral-economic topics as well as artificial intelligence. We dive deeper into the interdisciplinary nature and contribute to the formation of a clear frame of discussion. We review the related works that are about market prediction based on online-text-mining and produce a picture of the generic components that they all have. We, furthermore, compare each system with the rest and identify their main differentiating factors. Our comparative analysis of the systems expands onto the theoretical and technical foundations behind each. This work should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance.     

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence. We present a case study which involves a set of techniques in classification tasks and we study a set of nonparametric procedures useful to analyze the behavior of a method with respect to a set of algorithms, such as the framework in which a new proposal is developed.Particularly, we discuss some basic and advanced nonparametric approaches which improve the results offered by the Friedman test in some circumstances. A set of post hoc procedures for multiple comparisons is presented together with the computation of adjusted p-values. We also perform an experimental analysis for comparing their power, with the objective of detecting the advantages and disadvantages of the statistical tests described. We found that some aspects such as the number of algorithms, number of data sets and differences in performance offered by the control method are very influential in the statistical tests studied. Our final goal is to offer a complete guideline for the use of nonparametric statistical procedures for performing multiple comparisons in experimental studies.