Reinforcing attitudes in a gatewatching news era: individual-level antecedents to sharing fact-checks on social media

Despite the prevalence of fact-checking, little is known about who posts fact-checks online. Based upon a content analysis of Facebook and Twitter digital trace data and a linked online survey (N = 783), this study reveals that sharing fact-checks in political conversations on social media is linked to age, ideology, and political behaviors. Moreover, an individual’s need for orientation (NFO) is an even stronger predictor of sharing a fact-check than ideological intensity or relevance, alone, and also influences the type of fact-check format (with or without a rating scale) that is shared. Finally, participants generally shared fact-checks to reinforce their existing attitudes. Consequently, concerns over the effects of fact-checking should move beyond a limited-effects approach (e.g., changing attitudes) to also include reinforcing accurate beliefs.Accepted manuscrip

Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović

Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. a–c Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546 kb

Using Social Media to Promote STEM Education: Matching College Students with Role Models

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac