Leveraging Joint Interactions for Credibility Analysis in News Communities
Table 10: Pearson’s product-moment correlation between various factors (with P-value <0.0001 for each test).
['[BOLD] Factors', '[BOLD] Corr.']
Hypothesis Testing: We test various hypotheses under the influence of the feature groups using explicit labels, and ratings available in the NewsTrust community. Language: Topics: Topics are an important indicator for news credibility. We measured the influence of the Politics tag on other topics by their co-occurrence frequency in the explicit tag sets over all the news articles. We found significant influence of Politics on all topics, with an average measure of association of 54% to any topic, and 62% for the overall news article. The community gets polarized due to different perspectives on topical aspects of news. In general, we find that community disagreement for different viewpoints are as follows: Right (0.80) > Left(0.78) > Center(0.65) > Neutral (0.63). Users: User engagement features are strong indicators of expertise. Interactions: A negative sign of correlation indicates decrease in disagreement or MSE with increase in expertise. In a news community, we can observe moderate signals of interaction between various factors that characterize users, articles, and sources. Our CCRF model brings all these features together to build a strong signal for news credibility.