The Observatory on Social Media (OSoMe, pronounced awe•some) is a joint project of the Network Science Institute (IUNI), the Center for Complex Networks and Systems Research (CNetS) at the School of Informatics, Computing, and Engineering, and the Media School at Indiana University. The OSoMe mission:
Transform the study of coupled media and technology networks that drive the diffusion of information. Offer access to data and tools to investigate the diffusion of (mis)information, uncover the vulnerabilities of the media ecosystem, and develop methods for increasing the resilience of citizens and democratic systems to manipulation. Train a generation of media professionals, enabling them to employ computational skills for fulfilling the traditional watchdog function of journalism. Our ambition is to position future reporters to uncover newsworthy information that is otherwise invisible to public scrutiny and empower citizens to navigate their way to informed participatory behavior.
Core OSoMe activities
Employ social media data and analytics resources to empower researchers, journalists, and citizens to understand information diffusion, detect misinformation, and evaluate the trustworthiness of new influentials. These resources include a 100+ billion tweet collection and several public data, visualization, machine learning, and literacy tools to study information pollution and manipulation.
Conduct research to understand the cognitive, social, network, and algorithmic factors that make the media landscape vulnerable to manipulation, and design interventions and incentives to mitigate these vulnerabilities. Our research will help news consumers detect coordinated manipulation campaigns and determine the trustworthiness of information and sources, in the absence of reliable intermediaries. The interdisciplinary nature of these efforts is illustrated by collaborations among computer scientists, journalists, physicists, cognitive scientists, political scientists, and sociologists.
Develop new training in support of the journalistic mission, serving the information needs of a democracy in the era of social media. These educational effortswill include new master and certificate programs in data journalism.
Our research and tools have major societal impact. We were among the first research groups to show how social media can be abused to manipulate public opinion. For example, our work uncovered early evidence of systematic, orchestrated, and widely spread misinformation campaigns based on fake news, astroturf (fake grassroots movements), and social bots. We developed the first widely used bot detection system (BotOrNot, later renamed Botometer). Our work on social bots was featured on the covers of the two top computing publications: IEEE Computer and Communications of the ACM.
Our tools have been used to uncover the roots of political misinformation in investigative pieces on the White Helmets and the pizzagate conspiracy. They have been leveraged in studies of misinformation spreading mechanisms (e.g., studies by Pew and in Science), and disinformation campaigns (e.g., voter suppression bots).
In terms of public policy, our reseach on social bots has been quoted during the Senate Intelligence Committee hearing with Facebook, Google and Twitter. It is also cited in a bill on bot disclosure introduced in the US Congress by Sen. Feinstein, and in a similar California state law enacted in 2019.
Using computational simulations of agent-based models in conjunction with large scale social media analytics, we have made significant contributions to the understanding of the factors that facilitate the viral spread of information and misinformation in social media: network structure, competition for finite attention, and manipulation by inauthentic accounts such as bots and trolls.
Recognition includes a best paper award at the Web Science Conference, a best poster award at the Conference on Complex Systems, and a best presentation award at the World Wide Web Conference. Our findings are disseminated through prestigious journals including Science (cover), Nature Communications, CACM, Computer, Nature Physics, Neuron, PRL, Nature Scientific Reports; and top international conferences including KDD, WWW, and ICWSM.
Research from this project receives worldwide coverage in hundreds of articles in popular media, including Wall Street Journal, New York Times, Washington Post, Rolling Stone, USA Today, CNN, BBC, NPR, The Economist, Newsweek, The Atlantic, Politico, New Scientist, Wired, Science, and Nature.
Our work was featured in episodes of the popular TV shows The Good Wife (deception by social bots) and Homeland (diffusion networks with manipulation by bots).
Our software and data are used in courses on network science and social media. The project has resulted in several open-source software libraries, including Hoaxy and BotSlayer, a mobile app (Fakey), and two patents. IU is licensing our Botometer software. Our visualization software won the WICI Data Challenge from the University of Waterloo.
We train undergraduate students from underrepresented minorities in STEM, as well as many graduate and postdoctoral students. Several former students are now employees at Facebook, Google, Amazon, and LinkedIn.