This project aims to understand and characterize user behavior in online social networks. Specifically, we posit, analyze, and validate different models of formation and evolution of opinions in social networks. We characterize our models both in terms of stability and goodness in terms of an approximation ratio w.r.t. to a social optimum (e.g., Price of Anarchy, Price of Stability etc).
If search and Twitter data are to be treated as a survey, they would follow a very peculiar methodology: participation is a time-varying, demographically biased sample of the population, participants are effectively continuously answering different “survey” questions, and, finally, participants can choose how often they are allowed to answer the question. In response, we show alternative methods for interpreting and using online and social media data fruitfully.
Crowd-sourcing is increasingly being used for providing answers to online polls and surveys. However, existing systems, while taking care of the mechanics of attracting crowd workers, poll building, and payment, provide little that would help the survey-maker or pollster to obtain statistically significant results devoid of even the obvious selection biases. InterPoll: a platform for programming of crowd-sourced polls. Polls are expressed as embedded LINQ queries, whose results are provided to t
The global hub for sustainable development at Microsoft Research
Face In The Crowd examines the social impact of crowdsourcing platforms—cloud-based computational systems that allow the outsourcing of work through open requests—and how they might shape the future of work.
Labs: New England
Socl — pronounced social — lets you create, collect and share stuff you love. From rich visual collages to short animated media and memes, express yourself through posts that take seconds to create, collect and share on Socl, as well as Facebook, Pinterest, Tumblr, and Twitter.
Labs: FUSE Labs
A mobile web app that makes latent, hyperlocal neighborhood communities more visible, to help neighbors connect. This project leverages intelligent filters and event detection algorithms to help users find relevant, spiking topics about what is happening here and now.
Labs: FUSE Labs
We are working toward a theoretic foundation of developing large-scale human-machine systems that combine the intelligence of human and the computing power of machine to address tasks that are difficult to complete by either human or machine alone.
This project focuses on rural government maternal health workers in India (called Accredited Social Health Activists, or ASHAs), using a tool called ASHA Assist to help ASHAs engage their clients in persuasive discussions about various topics related to maternal health. ASHA Assist consists of interactive videos on mobile phones, covering topics related to maternal health for use in counseling their clients.
With proliferation of ubiquitous access to information, the question arises of how distracting processing information can be in social settings, especially during a face-to-face conversation. In this paper, we investigate how much information users can consume during a conversation and what information delivery mode, via audio or visual aids, helps them effectively conceal the fact that they are receiving information.
Data is all the buzz. It's being seen in everything and found everywhere. But what are the consequences of this vision of a data-rich world for those of us on the street; what impact if any does it have on our everyday experiences and with the things that matter most to us. Here, we aim to reflect on the rise of (big) data and investigate what it does mean for us, and what it could come to mean.
We investigate how people's behaviour online can be characterized in terms of psychometric measurements such as the Big-5 personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism as well as general intelligence and satisfaction-with-life. We investigate patterns of Facebook usage, website preferences, query logs, and Facebook Likes and look for interesting correlations which can be used to predict users behaviours, preferences or characteristics.
In recent years the Web has evolved substantially, transforming from a place where we primarily find information to a place where we also leave, share and keep it. This presents a fresh set of challenges for the management of personal information, which include how to underpin greater awareness and more control over digital belongings and other personally meaningful content that is hosted online.
Identifying and Visualizing Viral Content
Research study conducted jointly by Microsoft Research and the University of Rochester, exploring the potential of harnessing social networking sites to answer visual questions on behalf of blind people.
With increasingly more data on every aspect of our daily activities – from what we buy, to where we travel, to who we know – we are able to measure human behavior with precision largely thought impossible just a decade ago. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science uses large-scale demographic, behavioral and network data to address longstanding questions in sociology, economics, politics, and beyond.
Web platforms such as Amazon’s Mechanical Turk are revolutionizing our ability to conduct human behavioral experiments of the kind historically performed in physical labs. Such “virtual lab” experiments allow for individual-level psychology and economics experiments to be carried out with unprecedented scale and speed, and also permit larger and more complex “networked” experiments on topics such as cooperation, learning, and collective problem solving.
MSR Project exploring the values of various social and informational components of social network Q&A exchanges.
We present AffectAura, an emotional prosthetic that allows users to reflect on their emotional states over long periods of time. We designed a multimodal sensor set-up for continuous logging of audio, visual, physiological and contextual data, a classification scheme for predicting user affective state and an interface for user reflection. The system continuously predicts a user's valence, arousal and engagement, and correlates this with information on events, communications and data i
Using analysis of social media posts, we look for linguistic markers that might indicate postpartum depression.
Design recommendations for computer-human interfaces that would allow a first-time, non-literate person, on first contact with a PC or a mobile phone, to immediately realize useful interaction with minimal or no external assistance. Follows an ethnographic design and iterative prototyping process and rigorous user evaluations, involving more than 700 hours spent in the field and 570 study participants from low-income, low-literate communities across India, Philippines and South Africa.
People are increasingly conscious of their everyday health and wellness conditions, and actively seek to improve them. In this project, we build software and hardware solutions that utilize and/or augment mobile phones to continuously monitor users' wellness without changing their existing lifestyle. Instead of solely passive monitoring, we further explore the actuation possibilities, i.e., seek to leverage the social networks to properly motivate the user towards improved health conditions.
We use data from Xbox games to investigate the impact of social play, retention of players, and usage of game features. All this can help to inform engineering decisions during game development.
Mood-based detection of affects in tweets
We introduced a game-theoretic framework for crowdsourcing systems.