Acknowledgements
Table III.20: Pearson’s product-moment correlation between various factors (with P-value <0.0001 for each test).
['[BOLD] Factors', '[BOLD] Corr.']
Ch4S3SS4SSSx3 Ch4S4SS1SSSx2 -Ch5S3SS1SSSx4 Ch5S3SS1SSSx7 -Ch5S3SS2SSSx3 Ch5S3SS2SSSx6 Ch5S3SS3SSSx4 Ch5S3SS3SSSx5 Ch5S4SS2SSSx2 -Ch5S4SS3SSSx3 Language: Topics: Topics are an important indicator for 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 postings. 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 posting. 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 community, we can observe moderate signals of interaction between various factors that characterize users, postings, and sources. Our CCRF model brings all these features together to build a strong signal for credibility analysis.