Research on detection of social bots by CNetS faculty members Alessandro Flammini and Filippo Menczer, former IUNI research scientist Emilio Ferrara, and graduate students Clayton Davis, Onur Varol, and Prashant Shiralkar was featured on the covers of the two top computing venues: the June issue of Computer (flagship magazine of the IEEE Computer Society) and the July issue of Communications of the ACM (flagship publication of the ACM). Continue reading Social bot research featured on CACM, IEEE Computer covers
Congratulations to CASCI alumnus Dr. Ahmed Abdeen Hamed who was recognized by FastCompany magazine, among the most creative people in the world, in 2016, for his research publication entitled: Twitter K-H networks in action: Advancing biomedical literature for drug search.Dr. Hamed completed his Computer Science MS degree at Indiana University in May 2005 and joined our Complex Networks & Systems track of the PhD in Informatics in the Fall of 2008. For personal reasons, he finished his PhD at the University of Vermont, but started his research in biomedical text mining with the CASCI group.
Did more people see #thedress as blue and black or white and gold? How many Twitter users wanted pop star Katy Perry to take the #icebucketchallenge? The power to explore online social media movements — from the pop cultural to the political — with the same algorithmic sophistication as top experts in the field is now available to journalists, researchers and members of the public from a free, user-friendly online software suite released today by scientists at Indiana University. The Web-based tools, called the Observatory on Social Media, or “OSoMe” (pronounced “awesome”), provide anyone with an Internet connection the power to analyze online trends, memes and other online bursts of viral activity. An academic pre-print paper on the tools is available in the open-access journal PeerJ.
“This software and data mark a major goal in our work on Internet memes and trends over the past six years,” said Filippo Menczer, director of the Center for Complex Networks and Systems Research and a professor in the IU School of Informatics and Computing. “We are beginning to learn how information spreads in social networks, what causes a meme to go viral and what factors affect the long-term survival of misinformation online. The observatory provides an easy way to access these insights from a large, multi-year dataset.” Read more.
Congratulations to Clayton Davis, who won the best presenter prize at WWW 2016 Developers Day! Clayton presented BotOrNot: A system to evaluate social bots, a paper coauthored with Onur Varol, Emilio Ferrara, Alessandro Flammini and Filippo Menczer, that describes our latest API developments with the BotOrNot system.
In an interview aired on the ABC (Australian) evening news program “The World” on April 4, 2016, Filippo Menczer discussed with host Beverley O’Connor how information and misinformation spread throughout the Internet and the roles of network structure and social bubbles in determining meme virality. Video here.
Update: On March 21st, 2016 the paper described below (PMC4720984) was highlighted by Russ Altman from Stanford University in his yearly review as one of 30 important papers of the year in translational bioinformatics.
Using complex networks analysis and social media mining, CNETS researchers from the CASCI team have found that Instagram, a growing social media platform among teens, can be used “to uncover drug-drug interactions (DDI) and adverse drug reactions (ADR).” The work shows that this popular social media service is “a very powerful source of data with great promise in the public-health domain”. The study, “Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines,” supported by an R01 grant from the National Institutes of Health as well as a gift from Persistent Inc., was recently published and presented at the Pacific Symposium on Biocomputing (PSB 2016), in Hawaii. (PubMed, arXiv). The results are based on almost 7.000 user timelines associated with depression drugs which combined have 5+ million posts.
Big success for CNetS researchers at the Conference on Complex Systems (CCS’15)! Here are the accepted talks from our center:
- Computational fact checking from knowledge networks by Giovanni Luca Ciampaglia, Prashant Shiralkar, Johan Bollen, Luis M Rocha, Filippo Menczer and Alessandro Flammini
- Control of complex networks requires structure and dynamics by Alexander Gates and Luis M. Rocha
- Darwin’s Semantic Voyage by Jaimie Murdock, Simon DeDeo, and Colin Allen
- Defining and Identifying Sleeping Beauties in Science by Qing Ke, Emilio Ferrara, Filippo Radicchi and Alessandro Flammini
- Detecting conflict in social unrest using Instagram* by Rion Brattig Correia, Kwan Nok Chan and Luis M. Rocha
- Detecting Campaigns in Social Media by Onur Varol, Emilio Ferrara, Filippo Menczer and Alessandro Flammini
- Discourse Polarization in the US Congress by Rion Brattig Correia, Kwan Nok Chan and Luis M. Rocha
- Eigenmood Twitter Analysis: measuring collective mood variation by Ian B. Wood, Joana Gonçalves-Sá, Johan Bollen and Luis M. Rocha
- Evolution of Online User Behavior During a Social Upheaval by Onur Varol, Emilio Ferrara, Christine Ogan, Filippo Menczer and Alessandro Flammini
- How human perception of the urban environment influences the abandonment process by Stefani Crabstree, Simon DeDeo
- Information theoretic structures of the French Revolution by Alexander Barron, Simon DeDeo, and Rebecca Spang
- Measuring Emotional Contagion in Online Social Networks by Zeyao Yang, Emilio Ferrara
- Modularity and the Spread of Perturbations in Complex Dynamical Systems* by Artemy Kolchinsky, Alexander J. Gates and Luis M. Rocha
- On Predictability of Rare Events Leveraging Social Media by Lei Le, Emilio Ferrara and Alessandro Flammini
- Optimal network modularity for information diffusion by Azadeh Nematzadeh, Emilio Ferrara, Alessandro Flammini and Yong-Yeol Ahn
- Redundancy and control in complex networks by Luis M. Rocha
- The Rise of Social Bots in Online Social Networks by Emilio Ferrara, Onur Varol, Prashant Shiralkar, Clayton Davis, Filippo Menczer and Alessandro Flammini
Simon DeDeo will also deliver one of the plenary talks. *Denotes papers “starred”, or designated as especially worthwhile by the CCS15 program committee.
The aim of this project is to characterize, study and model various sources of bias that emerge from the complex network structure of the Web, social media, and search engines. Some of the questions we’re currently exploring concern how social, cognitive, and algorithmic biases lead to the emergence of information overload and online echo chambers that make us more vulnerable to abuse and manipulation.
Social and cognitive biases
Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. In the paper Measuring Online Social Bubbles we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information. A release about this work got some press coverage and an extended version of this paper is in preparation.
We also find that the combination of social media mechanisms and cognitive biases such as limited attention and information overload may explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy. We develop a stylized model of an online social network, where individual agents prefer quality information, but have behavioral limitations in managing a heavy flow of information. The model predicts that in realistic conditions, low-quality information is just as likely to go viral, providing an interpretation for the high volume of misinformation we observe online.
Contributing to the writing of history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia, and arguably one of the primary sources of knowledge on the Web. In our paper First Women, Second Sex: Gender Bias in Wikipedia we study gender bias in Wikipedia in terms of how women and men are characterized in their biographies. To do so, we analyze biographical content in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in social theory and discuss their implications for Wikipedia policy. This work was covered in Wikimedia Research Newsletter. An extended journal version titled Women through the glass ceiling: gender asymmetries in Wikipedia also shows that women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect.
The feedback loops between users searching information, users creating content, and the ranking algorithms of search engines that mediate between them, lead to surprising results. We are studying how all these systems and communities influence and feed on each other in a dynamic information ecology, and how these interactions affect their evolution and their impact on the global processes of information discovery, retrieval, and utilization.
For example, studying the relationship between Web traffic and PageRank, we have shown that given the heterogeneity of topical interests expressed by search queries, search engines mitigate the popularity bias generated by the rich-get-richer structure of the Web graph. These results, dispelling the feared Googlearchy affect, have been published in Proc. Natl. Acad. Sci. USA, presented at the WAW 2006 keynote (slides), and generated some media attention. You can see some movies demonstrating the finding. The result also inspired a robust rank-based model of scale-free network growth, published in Phys. Rev. Lett. (press release).
Most recently we have identified the conditions in which popularity may be a viable proxy for quality content by studying a simple model of cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the critical trade-off between quality and popularity. There is a regime of intermediate exploration cost where an optimal balance exists, such that choosing what is popular actually promotes high-quality items to the top. Outside of these limits, however, popularity bias is more likely to hinder quality.
We also studied sources of bias that stem from legal, political, or economic factors. The CENSEARCHIP tool visualizes the differences between results obtained from different search engines, or different country versions of a search engine. This tool, based on a technique described in this paper in First Monday, generated a lot of reactions in the media and the blogosphere (press release).
Mark Meiss was supported by the Advanced Network Management Laboratory, one of the Pervasive Technology Labs established at Indiana University with funding from the Lilly Endowment.
Santo Fortunato was supported by a Volkswagen Foundation grant.
Diego Fregolente was supported by the J.S. McDonnell Foundation.
This research was also supported in part by the National Science Foundation under awards 0348940, 0513650, and 0705676.
Opinions, findings, conclusions, recommendations or points of view of this group are those of the authors and do not necessarily represent the official position of the National Science Foundation, the Volkswagen Foundation, the McDonnell Foundation, or Indiana University.