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 Krishna P Gummadi

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Average citations per article8.20
Citation Count123
Publication count15
Publication years2012-2017
Available for download13
Average downloads per article433.31
Downloads (cumulative)5,633
Downloads (12 Months)1,746
Downloads (6 Weeks)388
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17 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
July 2018 KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 129,   Downloads (12 Months): 238,   Downloads (Overall): 238

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Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine which of the two is more ...
Keywords: algorithmic decision making, fairness in machine learning, fairness measures, generalized entropy, group fairness, individual fairness, inequality indices, subgroup decomposability

2 published by ACM
June 2018 SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 28,   Downloads (12 Months): 67,   Downloads (Overall): 67

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Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources such as jobs ...
Keywords: algorithmic fairness, amortized fairness, attention, exposure, fair ranking, individual fairness, position bias

3
April 2018 WWW '18: Proceedings of the 2018 World Wide Web Conference
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 37,   Downloads (12 Months): 217,   Downloads (Overall): 218

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As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. ...

4 published by ACM
November 2017 CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 12,   Downloads (12 Months): 160,   Downloads (Overall): 160

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Search engines in online communities such as Twitter or Facebook not only return matching posts, but also provide links to the profiles of the authors. Thus, when a user appears in the top- k results for a sensitive keyword query, she becomes widely exposed in a sensitive context. The effects ...
Keywords: information retrieval, privacy, ranking exposure, search exposure, social search

5
April 2017 WWW '17: Proceedings of the 26th International Conference on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 33,   Downloads (12 Months): 210,   Downloads (Overall): 286

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Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over ...
Keywords: discrimination in decision making, fair classification, machine learning and law, algorithmic decision making, fair decision making

6
April 2017 WWW '17: Proceedings of the 26th International Conference on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 16,   Downloads (12 Months): 49,   Downloads (Overall): 49

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Online news media sites are emerging as the primary source of news for a large number of users. The selection of 'front-page' stories on these media sites usually takes into consideration several crowdsourced popularity metrics, such as number of views or shares by the readers. In this work, we focus ...
Keywords: front-page news selection, recency, non-personalized recommender systems, relevancy

7 published by ACM
February 2017 CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
Publisher: ACM
Bibliometrics:
Citation Count: 13
Downloads (6 Weeks): 72,   Downloads (12 Months): 248,   Downloads (Overall): 367

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Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. ...
Keywords: political bias inference, search bias, search bias quantification, social media search, sources of search bias, twitter

8 published by ACM
July 2016 SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 6,   Downloads (12 Months): 53,   Downloads (Overall): 209

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Privacy of Internet users is at stake because they expose personal information in posts created in online communities, in search queries, and other activities. An adversary that monitors a community may identify the users with the most sensitive properties and utilize this knowledge against them (e.g., by adjusting the pricing ...
Keywords: sensitive states, privacy models, sensitive topics, online communities, privacy, privacy risks, susceptibility, user ranking

9 published by ACM
February 2016 CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 2,   Downloads (12 Months): 89,   Downloads (Overall): 318

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Extracting news on specific topics from the Twitter microblogging site poses formidable challenges, which include handling millions of tweets posted daily, judging topicality and importance of tweets, and ensuring trustworthiness of results in the face of spam. To date, all scalable approaches have relied on crowd wisdom, i.e., keyword-matching on ...
Keywords: Experts vs. Crowds, Topical experts, Topical news, Twitter Lists

10 published by ACM
February 2016 WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 10,   Downloads (12 Months): 50,   Downloads (Overall): 356

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Social media sites are information marketplaces, where users produce and consume a wide variety of information and ideas. In these sites, users typically choose their information sources, which in turn determine what specific information they receive, how much information they receive and how quickly this information is shown to them. ...
Keywords: information, information network, optimization, rewiring algorithm, cover set, lossless, efficiency, social media

11 published by ACM
May 2015 WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 2,   Downloads (12 Months): 26,   Downloads (Overall): 103

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A number of recent studies of information diffusion in social media, both empirical and theoretical, have been inspired by viral propagation models derived from epidemiology. These studies model propagation of memes, i.e., pieces of information, between users in a social network similarly to the way diseases spread in human society. ...
Keywords: empirical, hashtags, information diffusion, online social networks, topic-aware diffusion, topical interest, topic modeling, topical alignment, topical expertise, personal bias, prediction, social exposure, urls, information propagation, topical influence, twitter

12 published by ACM
October 2014 RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 12,   Downloads (12 Months): 158,   Downloads (Overall): 566

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We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations ...
Keywords: labeled lda, lists, user interests, twitter

13 published by ACM
February 2014 CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 5,   Downloads (12 Months): 34,   Downloads (Overall): 390

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We present a semantic methodology to identify topical groups in Twitter on a large number of topics, each consisting of users who are experts on or interested in a specific topic. Early studies investigating the nature of Twitter suggest that it is a social media platform consisting of a relatively ...
Keywords: identity-based groups, seekers of topical information, topical experts, topical groups, twitter

14 published by ACM
October 2013 CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 13
Downloads (6 Weeks): 7,   Downloads (12 Months): 38,   Downloads (Overall): 311

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Several applications today rely upon content streams crowd-sourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated ...
Keywords: random sampling, sampling content streams, sampling from experts, twitter, twitter lists

15 published by ACM
May 2013 WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 8,   Downloads (12 Months): 37,   Downloads (Overall): 248

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The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide ...
Keywords: online social networks, privacy

16 published by ACM
September 2012 ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12: Volume 42 Issue 4, October 2012
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 10,   Downloads (12 Months): 36,   Downloads (Overall): 555

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In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective ...
Keywords: twitter, topic inference, lists, crowdsourcing, who is who

17 published by ACM
August 2012 SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 41
Downloads (6 Weeks): 4,   Downloads (12 Months): 71,   Downloads (Overall): 840

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Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds -- ...
Keywords: crowdsourcing, hubs, topic experts, authorities, twitter, lists



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