Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior and Evaluating It to Soccer Game TV Programs

Abstract

This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using 49 soccer game TV programs were conducted.

How Many Tweets Describe the Topics on TV Programs: An Investigation on the Relation between Twitter and Mass Media

“…There have been several studies focusing on the relationship between Twitter and TV media. Hayama [9] proposed a novel method to detect highlight scenes from a TV program using the classification of Twitter user behavior. Hayama conducted experiments on forty-nine football games and confirmed the model effectiveness.…”

PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

“…The annotation was focused on personal names frequently appearing in these tweets and the keywords co-occurring with them. Hayama [18] used tweets classified according to users' behavior to improve the detection of TV Program highlight scenes. For him, Twitter users behave in different ways (e.g., conversing or sharing information), and this behavior can affect the correct detection.…”

Análisis de redes sociales de la producción científica sobre programación televisiva

“…Esto se relacionaría con el que las investigaciones sobre redes sociales atraen más a los investigadores más jóvenes que pueden prescindir de los estudios previos y del background que exigen las investigaciones sobre televisión. A esto se sumaría su mejor preparación para los análisis cuantitativos y la minería de datos que exigen los análisis de redes sociales e internet (Hayama, 2018). Eso sin contar con que la propia televisión pueda considerarse integrada en la web, y no tanto como un medio independiente (Bondad-Brown, Rice, & Pearce, 2012).…”