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Bruno Abrahao
New York University Shanghai
19Publications
11H-index
371Citations
Publications 18
Newest
Jul 6, 2019 in ICWSM (International Conference on Weblogs and Social Media)
#1Koustuv Saha (Georgia Institute of Technology)H-Index: 4
#2Benjamin Sugar (Georgia Institute of Technology)H-Index: 2
Last.Munmun De Choudhury (Georgia Institute of Technology)H-Index: 33
view all 6 authors...
Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 3...
5 Citations
#2Bruno Abrahao (New York University Shanghai)H-Index: 11
Apr 21, 2018 in CHI (Human Factors in Computing Systems)
#1Will Qiu (Stanford University)H-Index: 1
#2Palo Parigi (Stanford University)H-Index: 1
Last.Bruno Abrahao (New York University Shanghai)H-Index: 11
view all 3 authors...
The large majority of reputation systems use features such as star ratings and reviews to give users a reputation in online peer-to-peer markets. Both have been shown to be effective for signaling trustworthiness. However, the exact extent to which these features can change perceptions of users' trustworthiness remains an open question. Using data from an online experiment conducted on Airbnb users, we investigate which of the two types of reputation information --average star rating or the numb...
6 CitationsSource
#1Bruno Abrahao (Stanford University)H-Index: 11
#2Paolo Parigi (Stanford University)H-Index: 11
Last.Karen S. Cook (Stanford University)H-Index: 38
view all 4 authors...
To provide social exchange on a global level, sharing-economy companies leverage interpersonal trust between their members on a scale unimaginable even a few years ago. A challenge to this mission is the presence of social biases among a large heterogeneous and independent population of users, a factor that hinders the growth of these services. We investigate whether and to what extent a sharing-economy platform can design artificially engineered features, such as reputation systems, to override...
15 CitationsSource
Jan 1, 2016 in ICWSM (International Conference on Weblogs and Social Media)
#1Bogdan State (Stanford University)H-Index: 9
#2Bruno Abrahao (Stanford University)H-Index: 11
Last.Karen S. Cook (Stanford University)H-Index: 38
view all 3 authors...
2 Citations
#1Bruno Abrahao (Cornell University)H-Index: 11
#2Sucheta Soundarajan (Cornell University)H-Index: 7
Last.Robert Kleinberg (Cornell University)H-Index: 46
view all 4 authors...
Four major factors govern the intricacies of community extraction in networks: (1) the literature offers a multitude of disparate community detection algorithms whose output exhibits high structural variability across the collection, (2) communities identified by algorithms may differ structurally from real communities that arise in practice, (3) there is no consensus characterizing how to discriminate communities from noncommunities, and (4) the application domain includes a wide variety of net...
17 CitationsSource
Jan 1, 2014 in ICML (International Conference on Machine Learning)
#1Tian Lin (THU: Tsinghua University)H-Index: 7
#2Bruno Abrahao (Cornell University)H-Index: 11
Last.Wei Chen (Microsoft)H-Index: 43
view all 5 authors...
In online learning, a player chooses actions to play and receives reward and feedback from the environment with the goal of maximizing her reward over time. In this paper, we propose the model of combinatorial partial monitoring games with linear feedback, a model which simultaneously addresses limited feedback, infinite outcome space of the environment and exponentially large action space of the player. We present the Global Confidence Bound (GCB) algorithm, which integrates ideas from both com...
31 Citations
Aug 11, 2013 in KDD (Knowledge Discovery and Data Mining)
#1Bruno Abrahao (Cornell University)H-Index: 11
#2Flavio Chierichetti (Sapienza University of Rome)H-Index: 19
Last.Alessandro Panconesl (Sapienza University of Rome)H-Index: 33
view all 4 authors...
The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental ques...
37 CitationsSource
Aug 12, 2012 in KDD (Knowledge Discovery and Data Mining)
#1Bruno Abrahao (Cornell University)H-Index: 11
#2Sucheta Soundarajan (Cornell University)H-Index: 7
Last.Robert Kleinberg (Cornell University)H-Index: 46
view all 4 authors...
Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from non-communities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the...
40 CitationsSource
Dec 17, 2008 in PODC (Principles of Distributed Computing)
#1Bruno Abrahao (Cornell University)H-Index: 11
#2Robert D. Weinberg (Cornell University)
12