A survey on trends of cross-media topic evolution map

• Published in: Knowledge-based systems Vol. 124; pp. 164 - 175
• Main Authors: Zhou, Houkui, Yu, Huimin, Hu, Roland, Hu, Junguo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.05.2017; Elsevier Science Ltd
• Subjects: Cross-media; Digital media; Evolution; Information overload; Probabilistic generative model; Probability; Social networks; Topic evolution; Topic map; Trends; Topic evolution; Topic map; Probabilistic generative model; Cross-media
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2017.03.009

Rapid advancements in internet and social media technologies have made “information overload” a rampant and widespread problem. Complex subjects, histories, or issues break down into branches, side stories, and intertwining narratives; a “topic evolution map” can assist in joining together and clarifying these disparate parts of an unfamiliar territory. This paper reviews the extant research on topic evolution map based on text and cross-media corpora over the past decade. We first define a series of necessary terms, then go on to describe the traditional topic evolution map per 1) topic evolution over time, based on the probabilistic generative model, and 2) topic evolution from a non-probabilistic perspective. Next, we discuss the current state of research on topic evolution map based on the cross-media corpus, including some open questions and possible future research directions. The main contribution of this review is in its construction of an evolution map that can be used to visualize and integrate the extant studies on topic modeling – specifically in regards to cross-media research.