Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.
Quantitative studies targeted to specific application-oriented tasks such as classification of emails into folders @cite_23 @cite_27 were conducted mainly on the open Enron email data @cite_11 , which is one of the few complete temporal data on email communication whose structure has been studied extensively @cite_34 . However, Enron's email communication patterns were very specific as they were limited to the context of the company and by its evolution and dramatic fall. This peculiarity held back researchers from drawing conclusions on general patterns of email use. Other less known, small-scale email datasets, such as the email corpora from OSS projects @cite_37 can provide interesting traces on user interaction, but they do not allow any generalization of the findings. Email communication networks have been also used to investigate the propagation of computer viruses @cite_16 and word-of-mouth advertising @cite_21 , or to solve specific tasks such as expert finding within organizations @cite_32 .
@cite_37 @cite_21 @cite_32 @cite_16 @cite_27 @cite_23 @cite_34 @cite_11
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A large set of email messages, the Enron corpus, was made public during the legal investigation concerning the Enron corporation. This dataset, along with a thorough explanation of its origin, is available at http: www-2.cs.cmu.edu enron . This paper provides a brief introduction and analysis of the dataset. The raw Enron corpus contains 619,446 messages belonging to 158 users. We cleaned the corpus before this analysis by removing certain folders from each user, such as “discussion_threads”. These folders were present for most users, and did not appear to be used directly by the users, but rather were computer generated. Many, such as “all_documents”, also contained large numbers of duplicate emails, which were already present in the users’ other folders. Our goal in this paper is to analyze the suitability of this corpus for exploring how to classify messages as organized by a human, so these folders would have likely been misleading. We investigate the structures present in the Enron email dataset using singular value decomposition and semidiscrete decomposition. Using word frequency profiles, we show that messages fall into two distinct groups, whose extrema are characterized by short messages and rare words versus long messages and common words. It is surprising that length of message and word use pattern should be related in this way. We also investigate relationships among individuals based on their patterns of word use in email. We show that word use is correlated to function within the organization, as expected. Lastly, we show that relative changes to individuals' word usage over time can be used to identify key players in major company events. Email logs have been considered as a useful resource for research in fields like link analysis, social network analysis and textual analysis. Most of the experiments in these fields of research are performed on synthetic data due to lack of an adequate and real life benchmark. The Enron email dataset is a touchstone for such research. This dataset is very similar to the kind of the data collected for fraud detection and counter terrorism hence it is a perfect test bed for testing the effectiveness of techniques used for counter terrorism and fraud detection. In this report we describe the MySql database prepared for the dataset and also statistically analyze its appropriateness for research. We further derive a social network constituting of 151 employees from the email logs, by defining a social contact to be someone with whom an individual has exchanged a pre decided threshold number of emails. The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis. We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective. We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels. We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles. Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication. The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure. We investigate the interaction of power, gender, and language use in the Enron email corpus. We present a freely available extension to the Enron corpus, with the gender of senders of 87 messages reliably identified. Using this data, we test two specific hypotheses drawn from the sociolinguistic literature pertaining to gender and power: women managers use face-saving communicative strategies, and women use language more explicitly than men to create and maintain social relations. We introduce the notion of “gender environment” to the computational study of written conversations; we interpret this notion as the gender makeup of an email thread, and show that some manifestations of power differ significantly between gender environments. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions. We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in an automatic fashion and can adopt itself to organizational changes over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players. Today’s email clients were designed for yesterday’s email. Originally, email was merely a communication medium. Today, people engage in a variety of complex behaviours using email, such as project management, collaboration, meeting scheduling, to-do tracking, etc. Our goal is to develop automated techniques to help people manage complex activities or tasks in email. The central challenge is that most activities are distributed over multiple messages, yet email clients allow users to manipulate just isolated messages. We describe machine learning approaches to identifying tasks and relations between individual messages in a task (i.e., finding cause-response links between emails) and for semantic message analysis (i.e., extracting metadata about how messages within a task relate to the task progress). Our key innovation compared to related work is that we exploit the relational structure of these two problems. Instead of attacking them separately, in our synergistic iterative approach, relations identification is used to assist semantic analysis, and vice versa. Our experiments with real-world email corpora demonstrate an improvement compared to nonrelational benchmarks. This paper presents findings from a study of how knowledge workers use the Web to seek external information as part of their daily work. Thirty-four users from seven companies took part in the study. Participants were mainly IT specialists, managers, and research marketing consulting staff working in organizations that included a large utility company, a major bank, and a consulting firm. Participants answered a detailed questionnaire and were interviewed individually in order to understand their information needs and information seeking preferences. A custom-developed WebTracker software application was installed on each of their work place PCs, and participants' Web-use activities were then recorded continuously during two-week periods. The WebTracker recorded how participants used the browser to seek information on the Web: it logged menu choices, button bar selections, and keystroke actions, allowing browsing and searching sequences to be reconstructed. In a second round of personal interviews, participants recalled critical incidents of using information from the Web. Data from the two interviews and the WebTracker logs constituted the database for analysis. Sixty-one significant episodes of information seeking were identified. A model was developed to describe the common repertoires of information seeking that were observed. On one axis of the model, episodes were plotted according to the four scanning modes identified by Aguilar (1967), Weick and Daft (1983): undirected viewing, conditioned viewing, informal search, and formal search. Each mode is characterized by its own information needs and information seeking strategies. On the other axis of the model, episodes were plotted according to the occurrence of one or more of the six categories of information seeking behaviors identified by Ellis (1989, 1990): starting, chaining, browsing, differentiating, monitoring, and extracting. The study suggests that a behavioral framework that relates motivations (Aguilar) and moves (Ellis) may be helpful in analyzing patterns of Web-based information seeking.