Quantifying Influence in Social Networks and News Media

Abstract

Massive numbers of users of social networks share various types of information such as opinions, news, and ideas in real time. As a new form of social network, Twitter is a particularly useful information source. Studying influence can help us better understand the role of social networks. The popularity of social networks like Twitter is primarily measured by the number of followers. The number of followers in Twitter and the number of users exposed to news media are important factors in measuring influence. We chose Twitter and the New York Times as representative media to analyze the influence and present an empirical analysis of these datasets. When the correlation between the number of followers in Twitter and the number of users exposed to the New York Times is computed, the result is moderately high. The correlation between the number of users exposed to the New York Times and the number of sections including the users on it, was found to be very high. We measure the normalized influence score using our proposed expression based on the two correlation coefficients.

Document Classification Model Using Web Documents for Balancing Training Corpus Size per Category

“…Most digital documents are frequently updated, and writers disseminate information and present their ideas on various topics [2]. Unlike news articles written by well-educated journalists and standardized according to official style guides, most digital documents, such as weblogs and twitter tweets, tend to contain colloquial sentences and slang that misleads the classifier, because anybody can publish anything [3][4][5][6]. Considering this cumbersomeness of document classification, researchers have proposed the following approaches.…”