Daniel Miller (University College London)
Daniel Miller is a Professor of Anthropology at University College London. He is a Fellow of the British Academy. He founded the first degree in Digital Anthropology at UCL. He has authored/edited 37 books including 'The Internet: An Ethnographic Approach' (with D. Slater 2001), 'The Cell Phone' (2006), 'Tales From Facebook' (2001), and 'Digital Anthropology' (with Heather Horst, 2012). Migration and New Media (with Mirca Madianou, 2012). Webcam (with J Sinanan 2014). Social Media in an English Village (2016) and How the World Changed Social Media (with 8 others UCL Press 2016). He also has three books with Suhrkamp: Der Trost der Dinge, Das Wilde Netzwork and Weihnachten - Das Globale Fest. Recently a play based on his work premiered in Stuttgart. Currently he holds a five-year (2012-2017) Advanced Grant from the European Research Council to investigate global social media.
This talk reports on research by nine anthropologists who simultaneously carried out a collaborative 15 months ethnography on the use and consequences of social media in fieldsites ranging from the Syria-Turkey border, an IT complex in south India to both a factory and a rural town in China, a squatters settlement in Brazil, a mining town Chile, an English village and small towns in south Italy and Trinidad.
The focus will be on two issues: our definition of social media as `scalable sociality’, in contrast to prior media with its duality of the private and the public, and secondly the impact of a shift to visual communication. These have consequences for a broad range of issues such as enhanced conservatism, and both enhanced and reduced individualism, inequality and privacy. The paper will briefly discuss our theoretical structures that underlie this project such as the `theory of attainment’, and `polymedia’.
Finally a mention will be made of the plans for the dissemination of the research results through eleven Open Access volumes and multilingual popular media such as YouTube, a MOOC, and a website, and the potential this represents for the future of research dissemination.
[slides as PDF]
Andrew Tomkins (Google Research)
Andrew joined Google Research in 2009, where he serves as an engineering director working on geo data analysis and machine learning. His earlier research focused on measurement, modelling, and analysis of content, communities, and users on the World Wide Web. Prior to joining Google, he spent four years at Yahoo! serving as chief scientist of search, and eight years at IBM's Almaden Research Center, where he served as chief scientist on the WebFountain project. Andrew has authored over 100 technical papers and 60 issued patents. He received Bachelors degrees in Mathematics and Computer Science from MIT, and a PhD in CS from Carnegie Mellon University.
LARGE-SCALE ANALYSIS OF DYNAMICS OF CHOICE AMONG DISCRETE ALTERNATIVES
The online world is rife with scenarios in which a user must select one from a finite set of alternatives: which movie to watch, which song to play, which camera to order, which website to visit. This talk will begin with an overview of these problems, sketching some different models, and describing some recent work on large-scale learning of the structure of choice. From there, we'll look at some examples, beginning with a discussion of choice among restaurants, taking into account factors of physical location. We'll also consider the dynamics of repeated consumption of the same item, which has a long history of interest to marketers and psychologists but is less well-studied in computer science. Finally, time permitting, we'll cover a more complex scenario of sequential consumption of a range of items, and will show how the theory of discrete choice can be incorporated into the theory of markov processes, requiring a new algorithmic approach.
Daniel Olmedilla (Facebook)
Daniel Olmedilla is Engineering Manager at Facebook. He leads the Machine Learning efforts in the Integrity area around products such as Ads, Pages, Groups and Commerce at Facebook. Prior to joining Facebook in 2014, Daniel was Vice President of Data Science at XING, and served as an independent expert and evaluator for the European Commission in a number of ICT-related domains (including Big Data and Machine Learning). Daniel holds two PhDs from Universidad Autonoma de Madrid and Leibniz Universität Hannover, and during his professional career, he has combined technology research, big data, and business strategy in multiple companies such as XING, Telefónica and Deloitte.
APPLYING MACHINE LEARNING TO ADS INTEGRITY AT FACEBOOK
With over two million active advertisers and more than one billion daily active users, one of the missions of the Ads Integrity team is to protect people and advertisers by creating a safe, high-quality ad experience. Given Facebook scale, manually reviewing each ad created before it is shown to our users would be unfeasible. Instead, the team uses a combination of automated Machine Learning models and Human Computing to detect policy violating and low quality ads. This talk will provide an overview of the challenges faced and highlight some of the solutions currently running at Facebook.
Ricardo Baeza-Yates (Universität Pompeu Fabra, Spain & Universidad de Chile)
Ricardo Baeza-Yates is VP of Research and Chief Research Scientist at Yahoo Labs based in Sunnyvale, California, since August 2014. Before he founded and lead from 2006 to 2015 the labs in Barcelona and Santiago de Chile. Between 2008 and 2012 he also oversaw the Haifa lab. He is also part time Professor at the Dept. of Information and Communication Technologies of the Universitat Pompeu Fabra, in Barcelona, Spain. During 2005 he was an ICREA research professor at the same university. Until 2004 he was Professor and before founder and Director of the Center for Web Research at the Dept. of Computing Science of the University of Chile (in leave of absence until today). He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electronics engineer degree from the University of Chile in Santiago. He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, that won the ASIST 2012 Book of the Year award. He is also co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 500 other publications. From 2002 to 2004 he was elected to the board of governors of the IEEE Computer Society and in 2012 he was elected for the ACM Council. He has received the Organization of American States award for young researchers in exact sciences (1993), the Graham Medal for innovation in computing given by the University of Waterloo to distinguished ex-alumni (2007), the CLEI Latin American distinction for contributions to CS in the region (2009), and the National Award of the Chilean Association of Engineers (2010), among other distinctions. In 2003 he was the first computer scientist to be elected to the Chilean Academy of Sciences and since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow.
The Web is the largest public big data repository that humankind has created. In this overwhelming data ocean we need to be aware of the quality and in particular, of biases the exist in this data, such as redundancy, spam, etc . These biases affect the algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, specially in the context of recommendation systems. We give several examples and their relation to sparsity, novelty, and privacy, stressing the importance of the user context to avoid these biases. [slides as PDF]
Jure Leskovec (Stanford University)
User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In the talk I will discuss antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We will find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We will use these insights to identify antisocial users early on, a task of high practical importance to community maintainers. [slides as PDF]
Helen Margetts (Oxford Internet Institute)
Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics, investigating political behaviour, digital government and government-citizen interactions in the age of the internet, social media and big data. She has published over a hundred books, articles and major research reports in this area, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, scott Hale and Taha Yasseri, 2015); Paradoxes of Modernization (with Perri 6 and Christopher Hood, 2010); Digital Era Governance (with Patrick Dunleavy, 2006); and The Tools of Government in the Digital Age (with Christopher Hood, 2007). In 2003 she and Patrick Dunleavy won the 'Political Scientists Making a Difference' award from the UK Political Studies Association, in part for a series of policy reports on Government on the Internet for the UK National Audit Office (1999, 2002 and 2007), and she continues working to maximise the policy impact of her research. She sits on the Digital Advisory Board of the UK Government Digital Service and the World Economic Forum Global Agenda Council on the Future of Government. She is editor-in-chief of the journal Policy and Internet. She is a fellow of the Academy of Social Sciences. From 2011- 2014 she held the ESRC professorial fellowship 'The Internet, Political Science and Public policy: Re-examining Collective Action, Governance and Citizen-Governance Interactions in the Digital Era'.
Professor Margetts joined the OII in 2004 from University College London where she was a Professor in Political Science and Director of the School of Public Policy. She began her career as a computer programmer and systems analyst with Rank Xerox after receiving her BSc in mathematics from the University of Bristol. She returned to studies at the London School of Economics and Political Science in 1989, completing an MSc in Politics and Public Policy in 1990 and a PhD in Government in 1996. She worked as a researcher at LSE from 1991 to 1994 and a lecturer at Birkbeck College, University of London from 1994 to 1999.
THE DATA SCIENCE OF POLITICS: HOW SOCIAL MEDIA SHAPE COLLECTIVE ACTION
The internet and social media bring political change, allowing 'tiny acts' of political participation which can scale up to large-scale mobilization of millions - but mostly fail. These new forms of mobilization increase instability and uncertainty in political systems, challenging policy-makers in both democratic and authoritarian regimes. But they also generate new sources of large-scale data, which can enrich our understanding of human behaviour and political participation. Drawing on research carried out for the new book Political Turbulence: How Social Media Shape Collective Action (Margetts, John, Hale and Yasseri, 2015, Princeton University Press), this talk discusses how social media is changing political systems - and how data science tools and methodologies might be used to understand, explain and even forecast the new 'political turbulence'.