In the typical state of an ever growing mailbox, it becomes essential to assist the user to better organize and quickly look up the content of his electronic life. Our work addresses this challenge, by identifying related messages within a user's mailbox. We study the notion of semantic relatedness between email messages and aim to offer the user with a wider context of the message he selects or reads. The context is represented by a small set of messages that are semantically related to the given message. We conduct experiments on a large-scale mail dataset obtained from a major Web mail service and demonstrate the effectiveness of our model in this task.

The 'like' button, introduced by Facebook several years ago, has become one of the most prominent icons of social media. Similarly to other popular social media features on the web, enterprises have also recently adopted it. In this paper, we present a first comprehensive study of liking activity in the enterprise. We studied the logs of an enterprise social media platform within a large global organization along a period of seven months, in which 393,720 'likes' were performed. In addition, we conducted a survey of 571 users of the platform's 'like' button. Our evaluation combines quantitative and qualitative analysis to inspect what employees like, why they use the 'like' button, and to whom they give their 'likes'.

Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind that mail extraction supports a variety of mail mining applications such as ad re-targeting, mail search, and mail summarization. We introduce new structural clustering methods that leverage the HTML structure that is common to messages generated by a same mass-sender script. We discuss how such structural clustering can be conducted at different levels of granularity, using either strict or flexible matching constraints, depending on the use cases.