Upcoming events

Finding Fake News

Giovanni Luca Ciampaglia
Indiana University Network Science Institute
SWS Colloquium
26 Jun 2017, 10:30 am - 12:00 pm
Saarbrücken building E1 5, room 029
Two-thirds of all American adults access the news through social media. But social networks and social media recommendations lead to information bubbles and personalization and recommendations by maximizing the click-through rate lead to ideological polarization. Consequently rumors false news conspiracy theories and now even fake news sites are an increasingly worrisome phenomena. While media organizations (Snopes.com PolitiFact FactCheck.org et al.) have stepped up their efforts to verify news political scientists tell us that fact-checking efforts may be ineffective or even counterproductive. To address some of these challenges researchers at Indiana University are working on an open platform for the automatic tracking of both online fake news and fact-checking on social media. The goal of the platform named Hoaxy is to reconstruct the diffusion networks induced by hoaxes and their corrections as they are shared online and spread from person to person.

Your Photos Expose Your Social Circles - Social Relation Recognition from 5 Social Domains

Qianru Sun
Joint Lecture Series
05 Jul 2017, 12:15 pm - 1:15 pm
Saarbrücken building E1 5, room 002
Social relations are the foundation of human daily life. Developing techniques to analyze such relations in visual data such as photos bears great potential to build machines that better understand people at a social level. Additionally through better understanding about such hidden information in exposed photos we would like to inform people about potential privacy risks. Social domain-based theory from social psychology is a great starting point to systematically approach social relation recognition. The theory provides a coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations in each domain. Our work provides the first photo dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations and contributes the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performances we have some findings of interpretable features that are in accordance with the predictions from social psychology literature. Our work mainly contributes to interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.