john derby evans associate professor
school of information
university of michigan

We propose an alternate framework for ML interpretability grounded in Weick’s sensemaking theory, which focuses on who the explanation is intended for. The paper introduces design guidelines for sensible AI, AI that factors in human cognition when explaining itself.
This article describes a survey in 14 regions around the world (N=3,993), detailing women’s perceptions of harm associated with online harassment, and preferred platform responses to that harm.
Chandrasekharan, Jhaver, Bruckman & Gilbert | TOCHI 2022
We present studies of what happened when Reddit quarantined the influential communities r/TheRedPill and r/The_Donald. The quarantine made it more difficult to recruit new members: new users decreased by 79.5% and 58%, respectively.
We introduce the feminist theory of affirmative consent, using it to explain existing problems on social platforms, and to generate new social media design ideas.
Im et al. | Web Science 2020
best paper honorable mention
Russia’s Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter. Here, we develop ML models to identify them, and then use the models to find active accounts still likely acting on behalf of the Russian state.
In this paper, we propose a new idea called synthesized social signals (S3s): social signals computationally derived from an account's history, and then rendered into the profile. We also introduce a system Sig that implements S3s to flag toxicity and misinformation.
Chandrasekharan, Gandhi, Mustelier & Gilbert | CSCW 2019
We introduce a new sociotechnical moderation system for Reddit called Crossmod. Crossmod makes decisions using cross-community learning—an approach that leverages a large corpus of previous moderator decisions via an ensemble of classifiers.
Using 32M Reddit posts, we characterize removal explanations that are provided to Redditors, and link them to future post removals. We show that offering explanations for content moderation reduces the odds of future removals.
Jhaver, Appling, Gilbert & Bruckman | CSCW 2019
best paper honorable mention
Thousands of users post on Reddit every day, but a fifth of all posts are removed. How do users react to these removals? We conducted a survey of 907 Reddit users, asking them to reflect on their post removal a few hours after it happened.
On Reddit, moderators heavily rely on a configurable, automated program called Automoderator (or Automod). How do they use it? What advantages and challenges does Automod present?
We study how users perceive and interact with potentially biased and deceptive opaque algorithms—in the context of reviewing on Yelp.
We use mixed methods to identify three classes of norms: macro norms that are universal to most of Reddit; meso norms shared across groups of subreddits; and micro norms that are specific to unique subreddits.
Jhaver, Ghoshal, Bruckman & Gilbert | TOCHI
We use Twitter blocklists to interrogate online harassment—the forms it takes, as well as tactics used by harassers.
We explore the ways Facebook supports—and does not support—the bartering of goods in Venezuela amid economic crisis.
In 2015 Reddit banned a number of hate subreddits. In this paper, we study the ban's effectiveness via causal inference methods.
Tanushree Mitra, Graham Wright & Eric Gilbert | CSCW 2018
Events with intermittent bursts of attention have lower levels of credibility. That is, as more people showed interest during transient collective attention, the uncertainty surrounding these events also increased.
In this paper, we introduce a novel computational approach for detecting abusive behvaior called Bag of Communities—a technique that leverages large-scale, preexisting data from other internet communities.
Tanushree Mitra, Graham Wright & Eric Gilbert | CSCW 2017
We present a parsimonious model that maps language cues to perceived levels of credibility. For example, hedge words and positive emotion words are associated with lower credibility.
Results from surveys about user awareness of privacy settings on Facebook. We also analyzed what (or who) is more likely post publicly.
Hiruncharoenvate, Smith, Edwards & Gilbert | GROUP 2016
supplement: system code
We present a new platform for building hyper-local social computing applications, running on home wireless routers via an underlying mesh network.
We present a quantitative study investigating pro-ED communities on Instagram in the aftermath of moderation and find that non-standard lexical variations of moderated tags are used to circumvent restrictions.
Here, we show that it is possible to computationally generate homophone substitutions for banned terms on Sina Weibo, a technique that is difficult for the censorship apparatus to defend against.
Bakhshi, Shamma, Kennedy & Gilbert | ICWSM 2015
We present a large-scale data analysis and in-depth interviews to understand filter-work. We find many use cases for filters, and that filtered photos are much more likely to be viewed and commented on.
In this paper we present CREDBANK, a corpus of tweets, topics, events and associated human credibility judgements based on the real-time tracking of events on Twitter.
We introduce a technique called Open Book designed to address encryption's social usability problems. It uses data mining and NLP to make messages vaguer than the originals.
Tanushree Mitra, C.J. Hutto & Gilbert | CHI 2015
best paper honorable mention
We measure the efficacy of selected strategies for obtaining high quality data annotations from non-experts. Our results point to the advantages of person-oriented strategies over process-oriented strategies.
We propose and evaluate a crowdsourcing approach to better support people with autism by offering rapid, concise, and socially appropriate coping strategies without compromising emotional support.
Catherine Grevet & Eric Gilbert | CHI 2015
best paper honorable mention
We propose a 6-stage prototyping mechanism for designing new social computing systems on top of existing ones. This allows a focus on what people do on a system rather than how to attract people to it.
Saeideh Bakshi & Eric Gilbert | PLOS One 2015
press: The Atlantic
We investigate whether there is link between color and diffusion. We find that color significantly impacts the diffusion of images and adoption of content on image sharing communities such as Pinterest.
Soni, Mitra, Gilbert & Eisenstein | ACL 2014
We obtain annotations of perceived certainty of quoted statements in Twitter. We find that readers are influenced by linguistic framing devices and do not consider other factors, e.g. sources, journalist.
Eric Gilbert | Foundations & Trends in HCI
This work presents a long-arc view of inferring tie strength via social media traces, and how we can alter interfaces to take advantage of it. Part of Eric's dissertation work, and set in the Twitter of 2010.
We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks. We see it as a bigger and badder LIWC.
Saeideh Bakhshi, David A. Shamma & Eric Gilbert | CHI 2014
press: Mashable
This work finds that photos with human faces are 38% more likely to receive likes and 32% more likely to receive comments on Instagram, regardless of age and gender of the faces.
This paper studies eliciting product reviews as social inferences (i.e., "How do you think other people will rate this product?"). I find that they substantially reduce variance.
Grevet, Choi, Kumar & Gilbert | CHI 2014
We find that email overload, both in terms of volume and of status, is still a problem today. While work email tends to be status overloaded, personal email is also type overloaded.
This is a study of Nextdoor, a social media system designed to support neighborhoods. Nextdoor raises tensions in how it defines boundary of neighborhoods, and in the privacy issues it raises among its users.
Saeideh Bakhshi, Partha Kanuparthy & Eric Gilbert | WWW 2014
supplement: data set | press: New York Times
This paper studies the effects of demographics and weather on restaurant reviews: restaurants in educated neighborhoods are highly reviewed and reviews written in nice weather are highly rated.
Catherine Grevet, Loren Terveen & Eric Gilbert | CSCW 2014
press: NBC | Ars Technica
We investigate political disagreements on Facebook to explore the conditions under which diverse opinions can coexist online, and suggest design opportunities to bridge across difference.
We explore the factors which lead to funding on Kickstarter. The language used in the project has surprising predictive power–accounting for 58.56% of the variance around successful funding.
We study two fundamental issues for social curation sites: flow of information & activities that attract followers. For example, sharing diverse content increases your following, but only up to a certain point.
To increase productivity, informal learning, and collaborations within and across research groups, we have been experimenting with a new kind of interaction that we call pair research.
Fairbanks, Ediger, McColl, Bader & Gilbert | ASONAM 2013
We present a novel approach to the analysis of temporally varying networks that leverages time series and statistical techniques to quantitatively describe a social network.
In this paper, we introduce a mobile application called Political Blend designed to combat echo chambers: it brings people with different political beliefs together for a cup of coffee.
Tanushree Mitra & Eric Gilbert | ACM Newsletter Winter 2013
Adopting the Enron email dataset and natural language techniques, we find that workplace gossip is common at all levels of the organizational hierarchy, with people most likely to gossip with their peers
Gilbert, Bakhshi, Chang & Terveen | CHI 2013
supplement: data set
We use a quantitative approach to study three research questions about Pinterest. What drives activity? What role does gender play? Finally, what distinguishes Pinterest from Twitter?
C.J. Hutto, Sarita Yardi & Eric Gilbert | CHI 2013
In this paper, we compare variables related to social behavior, message content, and network structure in order to interpret their relative impact to follower growth from different theoretical views.
In this paper, we present findings suggesting that widespread underprovision of votes is happening on Reddit. Notably, we find that 52% of the most popular links went overlooked on their first submisison.
Eric Gilbert | Digital Confidential (MIT Press)
In this chapter, we explore building social software to answer social science questions, covering issues like getting users and responding to the demands of the internet.
Have You Heard?: How Gossip Flows Through Workplace Email
Tanushree Mitra & Eric Gilbert | ICWSM 2012
Gossip is fundamental to social life. Here, we present the first large-scale study of it in cmc, looking at email where someone is mentioned in the message body but not included on the recipient list.
Eric Gilbert | CHI 2012
best paper honorable mention
Social translucence is a landmark theory in social computing. However, we argue that it breaks down over modern social network sites and build a theory relating network structure to design.
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.
Eric Gilbert | CSCW 2012
best paper honorable mention
The term tie strength denotes the differential closeness with the people in our lives. In this paper, we explore how well a tie strength model developed for one social medium adapts to another.
Eric Gilbert | UIUC PhD dissertation, 2010
Relationships make social media social. But, not all relationships are created equal. This dissertation addresses this problem, merging the theories behind tie strength with the data from social media.
Widespread Worry and the Stock Market
Eric Gilbert & Karrie Karahalios | ICWSM 2010
Our emotional state influences our choices. Here, we demonstrate that estimating emotions from blogs provides new information about future stock market prices.
Eric Gilbert & Karrie Karahalios | CSCW 2010
awarded best paper
People who review products on the web invest considerable time and energy in them. So why would someone write a review that restates earlier reviews? We look to answer this question.
Eric Gilbert & Karrie Karahalios | CHI 2009
awarded best paper
Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In this paper, we present a predictive model that maps social media data to tie strength.
Eric Gilbert, Tony Bergstrom & Karrie Karahalios | HICSS 2009
Many commentators and researchers speculate that blogs isolate readers in echo chambers, cutting them off from dissenting opinions. Our empirical paper tests this hypothesis.
Eric Gilbert, Karrie Karahalios & Christian Sandvig | ABS 2009
We know little about how rural communities use modern technologies. Using social capital theory, we predict differences between rural and urban users and find strong evidence supporting our hypotheses.
Eric Gilbert, Karrie Karahalios & Christian Sandvig | CHI 2008
awarded best paper
We know little about how rural communities use modern technologies. To address this gap, we explore behavioral differences between more than 3,000 rural and urban social media users.
Eric Gilbert & Karrie Karahalios | IEEE MM 2008
In this article we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development.
Eric Gilbert & Karrie Karahalios | Interact 2007
We present CodeSaw, a social visualization of distributed software development. It visualizes a distributed software community from two perspectives: code repositories and project communication.
Marge Bardeen, Eric Gilbert, et al. | Journal of Grid Computing 2005
We describe a case study that uses grid computing techniques to support the collaborative learning of high school students investigating cosmic rays.