I am a PostDoc at the Center for Network Science at the Central European University. I received my PhD from the College of Computer & Information Science at Northeastern University advised by Alan Mislove and David Lazer.
Broadly, my work investigates a variety of content serving websites such as Search Engines, Online Stores, Job Search Sites or Freelance Marketplaces. In this quickly changing online ecosystem companies track users' every move and feed the collected data into big data algorithms in order to match them with the most interesting, most relevant content. Since these algorithms learn on human data they are likely to pick up on social biases and unintentionally reinforce them. In my PhD work I created a methodology called Algorithmic Auditing which tries to uncover the potential negative impacts of large online systems. Examples of such audits include examining the "Filter Bubble effect" on Google Search, online price discrimination or detecting inequalities in online labor markets.
For my detailed resume please see my CV.
And here is my PhD thesis (I promise it is easy to read!)
Measuring Bias in Online Labor Markets
Labor economy has been through a lot of structural changes in the past years. People use various online services to find employment, advertise freelance services, collaborate on projects, outsource work, etc. These online sites offer innovative mechanisms for organizing employment or hiring processes and may alter many of the social forces known to cause social inequality in traditional labor markets. While policies in the traditional labor economy protecting people in the labor market have been developed over hundreds of years, we are at the early stages of this process in the online context. Paradoxically, while meaningful policy making requires a good understanding of the mechanisms that create or reinforce inequalities, without regulations reinforcing audits or some form of transparency, it is very difficult to learn about these systems.
In my work I investigate the mechanism that emerge in this new ecosystem and their potential for creating or reinforcing gender and racial biases. I am especially interested in the impact tools that differentiate new online services from traditional labor markets, e.g. public social feedback or the use of big data algorithms in search and recommendation. Quantifying bias is a challenge in itself; obtaining data, defining the right baselines to compare against, and developing tools for detecting inequalities. Beyond finding inequalities, my work places emphasis on determining the real world effect of these differences and exploring possible mitigation strategies. These questions however lead to even more methodological challenges; how do we disentangle effects of algorithms, self-presentation, network-processes in the underlying social network, and review systems?
To answer the above question I combine a variety of methods, including online data collection and empirical analysis, online and field experiments, and survey base data collection.
Fact-Cheking interventions on Online Social Networks
The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and Factcheck.org, are a strategy users can employ to refute false claims made by their peers. We use data from Online Social Networks such as Twitter to find these conversations and to examine the contexts and consequences of fact-checking interventions. Our preliminary results suggest that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends.
Political Fact-Checking on Twitter: When Do Corrections Have an Effect?
In the journal of Political CommunicationFair Sharing for Sharing Economy Platforms
In Proceedings of the FATREC Workshop on Responsible Recommendation (colocated with RecSys 2017), Como, Italy, August 2017.Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community
Proceedings of the 11th International AAAI Conference on Weblogs and Social Media (ICWSM'17) Montreal, May, 2017.Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr
Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), Portland, OR, February, 2017.Location, Location, Location: The Impact of Geolocation on Web Search Personalization
Proceedings of the 15th ACM Internet Measurement Conference (IMC'15), Tokyo, Japan, October 2015.Measuring Price Discrimination and Steering on E-commerce Web Sites
Proceedings of the 14th ACM/USENIX Internet Measurement Conference (IMC'14), Vancouver, Canada, November 2014.Get Back! You Don't Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations
Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM'14), Ann Arbor, MI, June 2014Measuring Personalization of Web Search
Proceedings of the 22nd International World Wide Web Conference (WWW'13), Rio de Janeiro, Brazil, May 2013.Tweetin' in the Rain: Exploring societal-scale effects of weather on mood (Poster Paper)
Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM'12), Dublin, Ireland, June 2012.
Posters and presentations
Power of Digital Research Roundtable
International Communication Association Conference, Fukuoka, Japan, 2016
Bias in Online Freelance Marketplaces
In 2nd Annual International Conference on Computational Social Science, Evanston, IL, June, 2016.Get Back! You Don't Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations
Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM'14), Ann Arbor, MI, June 2014Behavioral Responses to Fact-Checking Interventions in Online Social Networks
Sunbelt 2014, St. Petersburg FL, Feb. 2014Mitigating Sybil attacks on content rating systemsSOSP 2011Measuring and predicting sentiment on Twitter
Discrimination in Online Freelance Markets
- Researchers find racial, gender bias in online freelance marketplaces News@Northeastern,
- Data shows race and sex bias is costly for gig economy workers at startups like TaskRabbit Mic,
- Studies Show Racial and Gender Discrimination Throughout the Gig Economy
Is the Gig Economy Rigged?
MIT Tech Review,
Do companies charge online shoppers different prices? [Video Interview/ph
CBS Evening News
The Boston Globe
The Wall Street Journal
ABC News (Broadcast on Good Morning America)
The Washington Post
WBUR Radio Boston
The Huffington Post
Fast Company Exist
Atlanta Business Chronicle
The Saturday Post,
Oct 17, 2015
Nov 7, 2013