Assessing Public Awareness of Social Justice Documentary Films based on News Coverage versus Social Media

Abstract

The comprehensive measurement of the impact that information products have on individuals, groups and society is of practical relevance to many actors, including philanthropic funding organizations. In this paper we focus on assessing one dimension of impact, namely public awareness, which we conceptualize as the amount and substance of attention that information products gain from the press and social media. We are looking at a type of products that philanthropic organizations fund, namely social justice documentaries. Using topic modeling as a text summarization technique, we find that films from certain domains, such as "Politics and Government" and "Environment and Nature," attract more attention than productions on others, such as "Gender and Ethnicity". We also observe that film-related public discourse on social media (Facebook and non-expert reviews) has a higher overlap with the content of a film than press coverage of films does. This is partially due to the fact that social media users focus more on the topics of a production whereas the press pays strong attention to cinematographic and related features.

Estimating the Rating of the Reviews Based on the Text

“…The results showed that SVM classifier and the combination of all features are the best predictor of impact in reviews. In another work, the amount of alignment of social media (Facebook and reviews), news articles and the transcript of the film were studied [8]. It was found that social media are more aligned with the main subject of the films.…”

Analytical framework for evaluating digital diplomacy using network analysis and topic modeling: Comparing South Korea and Japan

“…In addition to the semantic network analysis, we employed topic modeling using LDA (latent Dirichlet allocation) to examine the public perceptions and attitudes expressed in the comments. Based on natural language processing, topic modeling is a text summarization technique that identifies salient themes contained in the data (Diesner, Rezapour, & Jiang, 2016). We used the ConText tool.…”