Friendships are dynamic. In this project, we uncover the dynamics of tie strength and online social interactions in terms of various aspects, such as reciprocity, temporality, and contextually. Based upon these dynamics, we build a learning to rank framework to predict social interactions in online social networks.
One out of four people in the world have experienced mental illness at some point in their lives. DiPsy is a digital psychologist presented as a personalized chatbot, who can evaluate, diagnose, treat and study users' mental processes through natural conversations.
In the field of computer science, large-scale experimentation on users is not new: there have been many efforts in both the public and private sectors to analyze users and to create experimental conditions to provoke changes in their behavior. However, new autonomous and semi-autonomous systems for experimentation, driven by techniques from AI and machine learning, raise important questions for the field. Many of these questions are about the social and ethical implications of these systems.
Labs: New York
Embedding information networks into low-dimensional spaces is potentially useful in many applications such as visualization, node classification, link prediction and recommendation. In this project, we proposed a large-scale information network embedding model called the "LINE", which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted.
The Ability team is a virtual team consisting of members of MSR's Labs who work on accessible technologies.
This project aims to enable people to converse with their devices. We are trying to teach devices to engage with humans using human language in ways that appear seamless and natural to humans. Our research focuses on statistical methods by which devices can learn from human-human conversational interactions and can situate responses in the verbal context and in physical or virtual environments.
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 theoretical foundation of developing large-scale human-machine systems that combine the intelligence of human and the computing power of machine to solve the problems that are difficult to solve 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.
Recent years have witnessed growing availability of human behavioral data, which provides us unprecedented opportunities to gain more in depth understanding of users in both the physical world and cyber world. In this project, we aim to develop computational models for learning individual's lifestyle specification and lifestyle spectrum of a community from heterogeneous networks. LifeSpec is also a data platform enabling our various research projects in mobile user understanding.
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
Labs: New York
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.
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.