Emails data are not completely independent of each other, because they could be written as a response to another email. Detecting dependencies among emails can improve the quality of email data analysis. An email conversation thread is defined as "a topic-centric discussion unit that is composed of exchanged emails among the same group of people by reply or forwarding".  Two structures could be assumed for conversation threads: linear structure and tree structure. In linear structure, emails that belong to the same conversation are detected and arranged in chronological order, shaping a flat structure. On the other hand, since in email conversations, users are allowed to choose any preceding email to reply to, many branches of discussions appear in a conversation and a conversation thread demonstrates a tree-shaped structure. In tree structure, conversation threads are shaped like a rooted tree in which the first email is the root and replies or forwards are shown as its children:

We proposed some methods to reconstruct conversation threads in an email corpus. We have two different approaches to reconstruct conversation threads.

In the first one, an evolutionary-based algorithm is exploited.  Generally, we map the problem of finding conversation threads as an optimization problem in which each conversation thread is represented with a single rooted tree and the target is to find a jungle of best rooted trees in the space of all possible rooted trees. We exploit Genetic Programming using a multiple objectives fitness function to solve this problem.

For more details, please read this paper:

• Mostafa Dehghani, M. Asadpour, and A. Shakery, "An Evolutionary-Based Method for Reconstructing Conversation Threads in Email Corpora", In proceedings of The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'12), 2012.[ACM-DL Link,]

In LExLinC, the problem is mapped to a graph clustering problem  in which first a weighted network  of emails is created and using email are grouped into threads using graph clustering algorithm:

In LExTreC, we break down the problem to finding  paths of arguments in emails and solve this problem as a retrieval problem (Qmail/Dmail) and employ "Learning to Rank" to learn model of fusing information from different features:

Then, we reconstructing conversation trees by integrating explicit and implicit information from extracted arguments:

For more details, please take a look at our paper:

• Mostafa Dehghani, A. Shakery, M. Asadpour, and A. Koushkestani, "A Learning Approach for Email Conversation Thread Reconstruction", Journal of Information Science (JIS), Volume 39 Issue 6, 2013, pp. 846-863. [ACM-DL Link]