The New Hampshire, Vol. 108, No. 23 (Apr. 4, 2019)

An independent student produced newspaper from the University of New Hampshire

Perceptions of Female Sexual Pathology: The Role of Racial Biases in Clinical Decision Making

Diversity issues such as multicultural competence and sexual health competence have received increased but separate academic attention in recent years. Although empirical support has been found for the presence of racial biases in the diagnoses of mental health disorders, there is no evidence to date regarding the role of racial biases in the diagnoses of female sexual pathology. In the present study, 101 pre-doctoral psychology interns across the United States assessed the symptom severity of a fictional client via online vignettes in which client race was experimentally manipulated. Participants did not report significantly different symptom severity ratings between the vignettes featuring a White client and the vignettes featuring a Black client. Future research should examine service-provider competence among more diverse samples, as well as pedagogical practices within psychology training programs that may be implicated in these results

Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201