Flagging

Abstract: Unmotivated test takers using rapid guessing in item responses can affect validity studies and teacher and institution performance evaluation negatively, making it critical to identify these test takers. The authors propose a new nonparametric method for finding response-time thresholds for flagging item responses that result from rapid-guessing behavior. Using data from a low-stakes assessment of college-level academic skills as an illustration, the authors evaluate and compare model fit and score estimation based on data sets cleaned by both new and existing methods. Flagging rapid-guessing responses is found to generally improve model fit, item parameter estimation, and score estimation, as in the literature. This new method, based on both response time and response accuracy, shows promise in detecting rapid guessing and in improving efficiency of the flagging process when built into data analysis.

Abstract: The study investigates the flagging behavior as specific type of bystander intervention against uncivil user comments in comments sections on news sites. Two experimental studies examine the effects of intervention information, characteristics of response comments, and the type of victim attacked in a comment on flagging behavior, that is on reporting a comment to professional moderators. Our results indicate that intervention information is a promising strategy to motivate flagging. Flagging is based on responsibility attribution to professional moderators but not on self-responsibility perception. Type of victim and characteristics of other users’ posted responses to preceding comments (public disagreement and politeness) shape deviance perceptions of the situation and influence flagging behavior.

Abstract: The rapid development of different social media and content-sharing platforms has been largely exploited to spread misinformation and fake news that make people believing in harmful stories, which allow to influence public opinion, and could cause panic and chaos among population. Thus, fake news detection has become an important research topic, aiming at flagging a specific content as fake or legitimate. The fake news detection solutions can be divided into three main categories: content-based, social context-based, and knowledge-based approaches. In this paper, we propose a novel hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: (1) linguistic features (i.e., title, number of words, reading ease, lexical diversity,and sentiment), and (2) a novel set of knowledge-based features, called fact-verification features that comprise three types of information namely, (i) reputation of the website where the news is published, (ii) coverage, i.e., number of sources that published the news, and (iii) fact-check, i.e., opinion of well-known fact-checking websites about the news, i.e., true or false. The proposed system only employs eight features, which is less than most of the state-of-the-art approaches. Also, the evaluation results on a fake news dataset show that the proposed system employing both types of features can reach an accuracy of 94.4%, which is better compared to that obtained from separately employing linguistic features (i.e., accuracy=89.4% ) and fact-verification features (i.e., accuracy=81.2%).

Abstract: The present invention comprises a system, method and software for automatically flagging one or more channels broadcast over a distribution network as a favorite channel that comprises monitoring input commands by a user through the use of an input device to detect a command from the user to tune a channel. An identifier for the channel is recorded and an indicator of the number of times that the channel has been tuned is incremented. The identifiers with the top indicators are selected for inclusion within the list of automatic favorite channels.