Information mining from public mailing lists: A case study on IETF mailing lists

Heiko Niedermayer, Nikolai Schwellnus, Daniel Raumer, Edwin Cordeiro, Georg Carle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Public mailing lists, such as the mailing lists used by the IETF for Internet Standardization, can be used as big real world data set for analysis of social interactions. However, volatile participation and the usage of mail addresses as changeable pseudonyms constitute a challenge for data mining in these data. We conducted a case study of mailing list analysis wherein we address the consistent identification of a person with all of her contributions to be used as panel data. Based on the postings of individuals on different mailing lists, correlations between standardization areas in the IETF groups can be computed. Isolated and meshed standardization areas can be identified.

Original languageEnglish
Title of host publicationInternet Science - 4th International Conference, INSCI 2017, Proceedings
EditorsDonald McMillan, Georg Carle, Antonella Passani, Jonathan Cave, Ioannis Kompatsiaris, Anna Satsiou, Efstratios Kontopoulos, Sotiris Diplaris
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319702834
StatePublished - 2017
Event4th International Conference on Internet Science, INSCI 2017 - Thessaloniki, Greece
Duration: 22 Nov 201724 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Internet Science, INSCI 2017


  • Clustering
  • Identity deduplication
  • Mailing lists
  • Standardization


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