Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant
Many software projects adopt mailing lists for the communication of developers and users. Researchers have been mining the history of such lists to study communities' behavior, organization, and evolution. A potential threat of this kind of study is that users often use multiple email addresses...
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2016
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sg-smu-ink.sis_research-97772024-05-23T03:51:35Z Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant WIESE, Igor Scaliante DA SILVA, José Teodoro STEINMACHER, Igor TREUDE, Christoph GEROSA, Marco Aurélio Many software projects adopt mailing lists for the communication of developers and users. Researchers have been mining the history of such lists to study communities' behavior, organization, and evolution. A potential threat of this kind of study is that users often use multiple email addresses to interact in a single mailing list. This can affect the results and tools, when, for example, extracting social networks. This issue is particularly relevant for popular and long-term Open Source Software (OSS) projects, which attract participation of thousands of people. Researchers have proposed heuristics to identify multiple email addresses from the same participant, however there are few studies analyzing the effectiveness of these heuristics. In addition, many studies still do not use any heuristics for authors' disambiguation, which can compromise the results. In this paper, we compare six heuristics from the literature using data from 150 mailing lists from Apache Software Foundation projects. We found that the heuristics proposed by Oliva et al. and a Naïve heuristic outperformed the others in most cases, when considering the F-measure metric. We also found that the time window and the size of the dataset influence the effectiveness of each heuristic. These results may help researchers and tool developers to choose the most appropriate heuristic to use, besides highlighting the necessity of dealing with identity disambiguation, mainly in open source software communities with a large number of participants. 2016-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8774 info:doi/10.1109/ICSME.2016.13 https://ink.library.smu.edu.sg/context/sis_research/article/9777/viewcontent/icsme16b.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Apache software foundation Email address disambiguation Mailing lists Mining software repositories Software Engineering |
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Apache software foundation Email address disambiguation Mailing lists Mining software repositories Software Engineering WIESE, Igor Scaliante DA SILVA, José Teodoro STEINMACHER, Igor TREUDE, Christoph GEROSA, Marco Aurélio Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
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Many software projects adopt mailing lists for the communication of developers and users. Researchers have been mining the history of such lists to study communities' behavior, organization, and evolution. A potential threat of this kind of study is that users often use multiple email addresses to interact in a single mailing list. This can affect the results and tools, when, for example, extracting social networks. This issue is particularly relevant for popular and long-term Open Source Software (OSS) projects, which attract participation of thousands of people. Researchers have proposed heuristics to identify multiple email addresses from the same participant, however there are few studies analyzing the effectiveness of these heuristics. In addition, many studies still do not use any heuristics for authors' disambiguation, which can compromise the results. In this paper, we compare six heuristics from the literature using data from 150 mailing lists from Apache Software Foundation projects. We found that the heuristics proposed by Oliva et al. and a Naïve heuristic outperformed the others in most cases, when considering the F-measure metric. We also found that the time window and the size of the dataset influence the effectiveness of each heuristic. These results may help researchers and tool developers to choose the most appropriate heuristic to use, besides highlighting the necessity of dealing with identity disambiguation, mainly in open source software communities with a large number of participants. |
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WIESE, Igor Scaliante DA SILVA, José Teodoro STEINMACHER, Igor TREUDE, Christoph GEROSA, Marco Aurélio |
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WIESE, Igor Scaliante DA SILVA, José Teodoro STEINMACHER, Igor TREUDE, Christoph GEROSA, Marco Aurélio |
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WIESE, Igor Scaliante |
title |
Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
title_short |
Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
title_full |
Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
title_fullStr |
Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
title_full_unstemmed |
Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant |
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who is who in the mailing list? comparing six disambiguation heuristics to identify multiple addresses of a participant |
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Institutional Knowledge at Singapore Management University |
publishDate |
2016 |
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https://ink.library.smu.edu.sg/sis_research/8774 https://ink.library.smu.edu.sg/context/sis_research/article/9777/viewcontent/icsme16b.pdf |
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