Manuel Gomez Rodriguez, Max Planck Institute for Software Systems

Manuel Gomez Rodriguez Photo

Title: Distilling Information Reliability and Source Trustworthiness from Digital Traces

Abstract: Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this talk, I will first argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, I will introduce a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Finally, I will elaborate on large-scale experiments on real-world data gathered from Wikipedia and Stack Overflow and show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.

Bio: Manuel Gomez Rodriguez is a tenure-track faculty at Max Planck Institute for Software Systems. Manuel develops machine learning and large-scale data mining methods for the analysis, modeling and control of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including an Outstanding Paper Award at NIPS’13 and a Best Research Paper Honorable Mention at KDD’10. Manuel holds a PhD and MS in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find more about him at

Meeyoung Cha, Korea Advanced Institute of Science and Technology

Title: Rumor Detection in Social Media

Abstract: Social news platforms are an ideal place for spreading rumors and automatically debunking rumors has become a crucial problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand rumor propagation processes. In detecting rumors in the wild, time becomes a critical ​factor. ​This talk will present how the significance of features change​s​ ​over time and which features allow for early rumor detection.

Bio: Meeyoung Cha is an associate professor at Graduate School of Culture Technology in KAIST. Her research interests are in the analysis of large-scale online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. She worked at the Facebook Data Science Team as a Visiting Professor.

Krishna Gummadi, Max Planck Institute for Software Systems

Title: Demographic Biases in Trending Topic Recommendations and their Implications

Abstract: Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the con- tent an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site’s overall population.
In this talk, I will describe we our attempt to quantify and explore the demographic biases in the crowdsourced recommendations, using extensive data collected from Twitter. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service ‘Who-Makes-Trends’ at makes-trends.

Bio:  Krishna Gummadi is a tenured faculty member and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. Krishna’s research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with (i) assessing the credibility of information shared by anonymous online crowds, (ii) understanding and controlling privacy risks for users sharing data on online forums, (iii) understanding, predicting and influencing human behaviors on social media sites (e.g., viral information diffusion), and (iv) enhancing fairness and transparency of machine (data-driven) decision making in social computing systems. Krishna’s work on online social networks, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers and award (best) papers at NIPS ML & Law Symposium, ACM’s COSN, ACM/Usenix’s SOUPS, AAAI’s ICWSM, Usenix’s OSDI, ACM’s SIGCOMM IMC, and SPIE’s MMCN conferences. He has also co-chaired AAAI’s ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.