Characterizing and Predicting Email Deferral Behavior

Sarrafzadeh, Bahareh; Awadallah, Ahmed Hassan; Lin, Christopher H.; Lee, Chia-Jung; Shokouhi, Milad; Dumais, Susan T.

doi:10.1145/3289600.3291028

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Although there is limited recent literature on how legislators interact with email, there is more research on how the general public interacts with email. When deciding what emails to attend to, individuals rank importance based on a variety of factors, such as perceived time and effort, number of email recipients, current workload, perceived urgency, and the message sender (Sarrafzadeh et al, 2019). The authority of the sender also matters, such that senders with higher status prompt higher email engagement (Lim et al, 2016).…”

Section: Legislator Email Behaviormentioning

confidence: 99%

Who is listening? Profiles of policymaker engagement with scientific communication

et al. 2021

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The evolving science around COVID-19 made timely digital communication with policymakers increasingly important for all constituencies. This holds true particularly for the scientific community, where evidenceinformed policymaking can influence the effectiveness of public responses. In this context, understanding how to reach policymakers effectively and which policymakers are likely to engage with scientific information delivered through digital mediums is critical. This study provides a novel observational approach to understanding reach with policymakers through science email campaigns. Using a sample of nearly 3000 state policymakers, we assessed data from five digital messaging campaigns. Results indicate four profiles of legislators: those who rarely open and open slowly (Never Openers), those who only opened a couple of emails (Rare Openers), those who open quickly, but do not always open (Intermittent Openers), and those that consistently open quickly (Always Openers). Female legislators and legislators who had served for fewer terms were more likely to be Always Openers or Intermittent Openers, relative to male legislators and legislators who had served more terms. This study reveals patterns of email engagement and indicates science communication efforts may need to adopt more targeted strategies that better reach policymakers who tend to engage less frequently with emailed research content.

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Section: Legislator Email Behaviormentioning

confidence: 99%

Who is listening? Profiles of policymaker engagement with scientific communication

et al. 2021

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“…Detecting user intents, especially action-item intents [7], can help service providers to enhance user experience. Recent research focuses on predicting actionable email intent from email contents [31,51], and identify related user actions such as reply [52], deferral [47], re-finding [33]. In contrast to all these models trained on manually annotated clean labels, we develop a framework Hydra that leverages weak supervision signals from user interactions for intent classification in addition to a small amount of clean labels.…”

Section: Email Intent Classificationmentioning

confidence: 99%

Learning with Weak Supervision for Email Intent Detection

Shu

Mukherjee

Zheng

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Email remains one of the most frequently used means of online communication. People spend significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails.

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“…This work aims to leverage a user's context to improve the quality of personalized services. For example, previous approaches have tailored recommendations by integrating user context in conversational search [46], travel planners [8], intelligent email reminders [49], and calendar-aware email recommendation [67]. Relatively little prior research has focused on contextual modeling for writing assistance, although Horvitz et al [32] did explore using Bayesian inference to understand writers' intents and goals given their actions and queries.…”

Section: Contextual Recommendationsmentioning

confidence: 99%

Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring

Sarrafzadeh¹,

Jauhar²,

Gamon³

et al. 2020

Preprint

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Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.

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Characterizing and Predicting Email Deferral Behavior

Cited by 13 publications

References 26 publications

Who is listening? Profiles of policymaker engagement with scientific communication

Who is listening? Profiles of policymaker engagement with scientific communication

Learning with Weak Supervision for Email Intent Detection

Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring

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