Visual Analysis of Weblog Content

In recent years, one of the advances of the World Wide Web is social media and one of the fastest growing aspects of social media is the blogosphere. Blogs make content creation easy and are highly accessible through web pages and syndication. With their growing influence, a need has arisen to be able to monitor the opinions and insight revealed within their content. In this paper we describe a technical approach for analyzing the content of blog data using a visual analytic tool, IN-SPIRE, developed by Pacific Northwest National Laboratory. We highlight the capabilities of this tool that are particularly useful for information gathering from blog data

The structure and perception of budgerigar (Melopsittacus undulatus) warble songs

The warble song of male budgerigars (Melopsittacus undulatus) is an extraordinarily complex, multi-syllabic, learned vocalization that is produced continuously in streams lasting from a few seconds to a few minutes without obvious repetition of particular patterns. As a follow-up of the warble analysis of Farabaugh et al. (1992), an automatic categorization program based on neural networks was developed and used to efficiently and reliably classify more than 25,000 warble elements from 4 budgerigars. The relative proportion of the resultant seven basic acoustic groups and one compound group is similar across individuals. Budgerigars showed higher discriminability of warble elements drawn from different acoustic categories and lower discriminability of warble elements drawn from the same category psychophysically, suggesting that they form seven perceptual categories corresponding to those established acoustically. Budgerigars also perceive individual voice characteristics in addition to the acoustic measures delineating categories. Acoustic analyses of long sequences of natural warble revealed that the elements were not randomly arranged and that warble has at least a 5th-order Markovian structure. Perceptual experiments provided convergent evidence that budgerigars are able to master a novel sequence between 4 and 7 elements in length. Through gradual training with chunking (about 5 elements), birds are able to master sequences up to 50 elements. The ability of budgerigars to detect inserted targets taken in a long, running background of natural warble sequences appears to be species-specific and related to the acoustic structure of warble sounds

A Temporal Frequent Itemset-Based Clustering Approach For Discovering Event Episodes From News Sequence

When performing environmental scanning, organizations typically deal with a numerous of events and topics about their core business, relevant technique standards, competitors, and market, where each event or topic to monitor or track generally is associated with many news documents. To reduce information overload and information fatigues when monitoring or tracking such events, it is essential to develop an effective event episode discovery mechanism for organizing all news documents pertaining to an event of interest. In this study, we propose the time-adjoining frequent itemset-based event-episode discovery (TAFIED) technique. Based on the frequent itemset-based hierarchical clustering (FIHC) approach, our proposed TAFIED further considers the temporal characteristic of news articles, including the burst, novelty, and temporal proximity of features in an event episode, when discovering event episodes from the sequence of news articles pertaining to a specific event. Using the traditional feature-based HAC, HAC with a time-decaying function (HAC+TD), and FIHC techniques as performance benchmarks, our empirical evaluation results suggest that the proposed TAFIED technique outperforms all evaluation benchmarks in cluster recall and cluster precision

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media

92nd Annual Meeting of the Virginia Academy of Science: Proceedings

Full proceedings of the 92nd Annual Meeting of the Virginia Academy of Science, May 13-15, 2014, Virginia Commonwealth University, Richmond, Virgini