Chapters authored
Time-Series Analysis of Video Comments on Social Media
In this study, we propose a method to detect unfair rating cheat caused by multiple comment postings focusing on time-series analysis of the number of comments. We defined the videos that obtained a lot of comments by unfair cheat as ‘unfair video’ and defined the videos which obtained without unfair cheat as ‘popular video’. Specifically, our proposed method focused on the difference of chronological distributions of the comments between the popular videos and the unfair videos. As the evaluation result, our proposed method could obtain higher accuracy than that of the baseline method.
Part of the book: Sociolinguistics
Analysis of Information Spreading by Social Media Based on Emotion and Empathy
The number of social media users has increased exponentially in recent times, and various types of social media platforms are being introduced. While social media has become a convenient communication tool, its use has caused various social problems. Some users who cannot imagine the emotions their posts may induce in readers cause what is termed as “the flaming phenomenon.” In some cases, users intentionally repeat strong remarks for self-advertisement. To identify the cause of this phenomenon, it is necessary to analyze the posted contents or the personalities of the users who cause the flaming. However, it is difficult to reach a generalized conclusion because each case varies depending on the circumstances and individual. In this chapter, we study the phenomenon of information spreading via communication on social media by conducting a detailed analysis of replies and number of retweets in Japanese, and we reveal the relation between the feedback on such posts and the emotions or empathy they result in.
Part of the book: Off and Online Journalism and Corruption
Mining Numbers in Text: A Survey
Both words and numerals are tokens found in almost all documents but they have different properties. However, relatively little attention has been paid in numerals found in texts and many systems treated the numbers found in the document in ad-hoc ways, such as regarded them as mere strings in the same way as words, normalized them to zeros, or simply ignored them. Recent growth of natural language processing (NLP) research areas has change this situations and more and more attentions have been paid to the numeracy in documents. In this survey, we provide a quick overview of the history and recent advances of the research of mining such relations between numerals and words found in text data.
Part of the book: Information Systems