Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation platform (CommTool), which enables communicators to get diverse message-level metrics such as reading time, relevance rate, comments, etc. We evaluated these designs through a 2-month field deployment with 5 communicators and 149 organization employees. We found that 1) the message-level metrics, such as reading time and relevance rate, helped communicators understand their audience and design bulk emails; 2) the cost and reputation metrics did not influence the organization leaders' decisions. We summarize with suggestions on designing organizational bulk email evaluation platforms that provide message-level performance and cost information.

A single digital newsletter usually contains many messages (regions). Users’ reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users’ interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data.

Bulk email is often used in organizations to communicate "important-to-organization'' messages such as policy changes, organizational plans, and administrative updates. However, normal employees may prefer messages more relevant to their jobs or interests. Organizations face the challenge of balancing prioritizing the messages they prefer employees to know (tactical goals) while maintaining employees' positive experiences with these bulk emails, then they continue to read these emails in the future (strategic goals). Could personalization help organizations achieve these tactical and strategic goals? In an 8-week field experiment with a university newsletter, we implemented a 4x5x5 factorial design on personalizing subject lines, top news, and message order based on both the employees' and the organization's preferences. We measured these designs' influences on the open/interest/recognition/read-in-detail rate of the whole newsletter and the single messages within it. We found that ''important-to-organization'' messages only got higher recognition rates when being put on subject lines / top news (tactical goal). Mixing them with employee-preferred messages in top news did not bring further improvement to their own recognition rates but could improve the whole newsletter's recognition rate. Only when the top news solely contained the employee-preferred messages were the employees slightly more interested in the newsletter (strategic goal). We further analyze on which topics the employees and the organization's preferences conflicted. Finally, we discuss the design suggestions for organizational bulk email.

Structured data peer production (SDPP) platforms like Wikidata play an important role in knowledge production. Compared to traditional peer production platforms like Wikipedia, Wikidata data is more structured and intended to be used by machines, not (directly) by people; end-user interactions with Wikidata often happen through intermediary "invisible machines." Given this distinction, we wanted to understand Wikidata contributor motivations and how they are affected by usage invisibility caused by the machine intermediaries. Through an inductive thematic analysis of 15 interviews, we find that: (i) Wikidata editors take on two archetypes---Architects who define the ontological infrastructure of Wikidata, and Masons who build the database through data entry and editing; (ii) the structured nature of Wikidata reveals novel editor motivations, such as an innate drive for organizational work; (iii) most Wikidata editors have little understanding of how their contributions are used, which may demotivate some. We synthesize these insights to help guide the future design of SDPP platforms in supporting the engagement of different types of editors.

Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.