Phrases that signal workplace hierarchy

Abstract

Hierarchy fundamentally shapes how we act at work. In this paper, we explore the relationship between the words people write in workplace email and the rank of the email's recipient. Using the Enron corpus as a dataset, we perform a close study of the words and phrases people send to those above them in the corporate hierarchy versus those at the same level or lower. We find that certain words and phrases are strong predictors. For example, "thought you would" strongly suggests that the recipient outranks the sender, while "let's discuss" implies the opposite. We also find that the phrases people write to their bosses do not demonstrate cognitive processes as often as the ones they write to others. We conclude this paper by interpreting our results and announcing the release of the predictive phrases as a public dataset, perhaps enabling a new class of status-aware applications.

Dimensions of Interpersonal Relationships: Corpus and Experiments

“…Similarly, Gilbert (2012) explores how people in hierarchical relationships communicate through email, and Bramsen et al (2011) focus on identifying power relationships in social networks. Politeness in online forums has also been studied (Danescu-Niculescu-Mizil et al, 2013).…”

Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions

“…Using NLP to deduce social relations from online communication is a relatively new area of active research. Bramsen et al (2011) and Gilbert (2012) first applied NLP based techniques to predict power relations in Enron emails, approaching this task as a text classification problem using bag of words or ngram features. More recently, our work has used dialog structure features derived from deeper dialog act analysis for the task of power prediction in Enron emails (Prabhakaran and Rambow, 2014;Prabhakaran et al, 2012;Prabhakaran and Rambow, 2013).…”

“…Understanding these manifestations is important not only to answer fundamental questions in social sciences about power and social interactions, but also to build computational models that can automatically infer social power structures from interactions. The availability and access to large digital repositories of naturally occurring social interactions and the advancements in natural language processing techniques in recent years have enabled researchers to perform large scale studies on linguistic correlates of power, such as words and phrases (Bramsen et al, 2011;Gilbert, 2012), linguistic coordination (DanescuNiculescu-Mizil et al, 2012), agenda control (Taylor et al, 2012), and dialog structure (Prabhakaran and Rambow, 2014).…”

Twitter Can Predict Your Next Place of Visit

“…For deriving personality traits Linguist Inquiry and Word Count (LIWC) [20] had been used frequently. References [20][21][22][23][24][25][26][27][28][29] have shown that lexicons used by people can be used for understanding their personal values and how to use these traits for a recommendation. Though all these approaches have been used extensively in analyzing personality traits, these also have shortcomings of predefined word category correlation.…”