Using self-categorization theory to uncover the framing of the 2015 Rugby World Cup: a cross-cultural comparison of three nations’ newspapers

Research into the framing of sporting events has been extensively studied to uncover newspaper bias in the coverage of global sporting events. Through discourse, the media attempt to capture, build, and maintain audiences for the duration of sporting events through the use of multiple narratives and/or storylines. Little research has looked at the ways in which the same event is reported across different nations, and media representations of the Rugby World Cup have rarely featured in discussions of the framing of sport events. The present study highlights the different ways in which rugby union is portrayed across the three leading Southern Hemisphere nations in the sport. It also shows the prominence of nationalistic discourse across those nations and importance of self-categorizations in newspaper narratives.</jats:p

Presidential scandals and party loyalty

This paper takes advantage of the unique aspects of Trump’s Presidency to design and implement a survey-experiment testing various categories of scandals. Although the findings are limited to the current Presidency, the paper contributes to the literature through its categorization of Trump’s scandals, and its application of those categories in an experimental design. The results indicate no significance for any type of scandal; raising questions regarding polarization in the country, and media outlets’ extensive coverage of such scandals. Negative partisanship is also examined here as a potential explanation for the high levels of party loyalty seen in the Republican Party – although the results in that area are similarly insignificant. Further research should be done to draw out precise movements among true independents and understand how positive and negative partisanship interact with one another in generating party loyalty

The Development of In-Group Favoritism: Between Social Reality and Group Identity

This study examined how social reality restricts children’s tendency for in-group favoritism in group evaluations. Children were faced with social reality considerations and with group identity concerns. Using short stories, in this experimental study, conducted among 3 age groups (6-, 8-, and 10-year-olds), the authors examined the trait attribution effects of reality constraints on eye-color differences and national group differences. The results show that the trait attributions of all age groups were restricted by the acceptance of socially defined reality. In addition, when the information about reality was not considered accurate, only the youngest children showed positive in-group favoritism. It is argued that these findings are useful in trying to reconcile some of the divergent and contrasting findings in the developmental literature on children’s intergroup perceptions and evaluations.

The Development of In-Group Favoritism: Between Social Reality and Group Identity

This study examined how social reality restricts children’s tendency for in-group favoritism in group evaluations. Children were faced with social reality considerations and with group identity concerns. Using short stories, in this experimental study, conducted among 3 age groups (6-, 8-, and 10-year-olds), the authors examined the trait attribution effects of reality constraints on eye-color differences and national group differences. The results show that the trait attributions of all age groups were restricted by the acceptance of socially defined reality. In addition, when the information about reality was not considered accurate, only the youngest children showed positive in-group favoritism. It is argued that these findings are useful in trying to reconcile some of the divergent and contrasting findings in the developmental literature on children’s intergroup perceptions and evaluations.

A Route Confidence Evaluation Method for Reliable Hierarchical Text Categorization

Hierarchical Text Categorization (HTC) is becoming increasingly important with the rapidly growing amount of text data available in the World Wide Web. Among the different strategies proposed to cope with HTC, the Local Classifier per Node (LCN) approach attains good performance by mirroring the underlying class hierarchy while enforcing a top-down strategy in the testing step. However, the problem of embedding hierarchical information (parent-child relationship) to improve the performance of HTC systems still remains open. A confidence evaluation method for a selected route in the hierarchy is proposed to evaluate the reliability of the final candidate labels in an HTC system. In order to take into account the information embedded in the hierarchy, weight factors are used to take into account the importance of each level. An acceptance/rejection strategy in the top-down decision making process is proposed, which improves the overall categorization accuracy by rejecting a few percentage of samples, i.e., those with low reliability score. Experimental results on the Reuters benchmark dataset (RCV1- v2) confirm the effectiveness of the proposed method, compared to other state-of-the art HTC methods

Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

Topic Detection and Tracking in Personal Search History

This thesis describes a system for tracking and detecting topics in personal search history. In particular, we developed a time tracking tool that helps users in analyzing their time and discovering their activity patterns. The system allows a user to specify interesting topics to monitor with a keyword description. The system would then keep track of the log and the time spent on each document and produce a time graph to show how much time has been spent on each topic to be monitored. The system can also detect new topics and potentially recommend relevant information about them to the user. This work has been integrated with the UCAIR Toolbar, a client side agent. Considering limited resources on the client side, we designed an e????cient incremental algorithm for topic tracking and detection. Various unsupervised learning approaches have been considered to improve the accuracy in categorizing the user log into appropriate categories. Experiments show that our tool is effective in categorizing the documents into existing categories and detecting the new useful catgeories. Moreover, the quality of categorization improves over time as more and more log is available