I like chocolate. Also at [email protected]

California, USA
Joined June 2008
Justin Cheng retweeted
In our latest @icwsm paper, we study how proactive moderation impacts groups on Facebook. We do so by studying a feature called "Post Approvals," popular among groups that seek to ensure a high standard of quality for all their posts 🧵arxiv.org/abs/2205.03258
2
23
1
74
Justin Cheng retweeted
Our paper is out in @PNASNews (w/ @andyguess, C. Tarnita): Using a model & Twitter data, we show that polarized news coverage can lead people to unwittingly sort their social networks along political lines, even w/o knowing their friends' politics! 1/🧵⬇️ pnas.org/content/118/50/e210…
4
66
8
285
Awesome new paper from some of my favorite researchers. They designed a very nice experiment and this punchline is 💯: Dealing with a biased sample is challenging for statistical models AND mental models of the world. journal.sjdm.org/21/210225/j…
2
54
7
298
How does social comparison on social media vary by country? research.fb.com/blog/2020/10… #CSCW2020
1
12
What does it mean to make causal inferences about conversational behaviours? Why does that matter, why's it difficult, what would it take to get there? My #CSCW2020 paper w/ @m_sendhil & @Cristian_DNM takes a step towards addressing these questions bit.ly/2FbwwOv [1/]
3
28
2
134
If you’re interested in working on computational social science problems at FB in 2021, I’m looking for interns! Also happy to chat about any questions you have! More info: facebook.com/careers/jobs/32…
5
37
103
When do misperceptions happen in online conversations? (with Jonathan Chang & Cristian Danescu-Niculescu-Mizil) research.fb.com/blog/2020/04…
1
5
25
Research on @GamesDoneQuick by Stephen Tsing-Han Sher suggests that featuring “the couch” on stream helps create a virtual living room that viewers can be a part of. #CSCW2019 static1.squarespace.com/stat…
2
A large-scale analysis of dark patterns on shopping websites found that (1) 40% of countdown timers don’t actually matter (2) 9% of uses of social proof (e.g., “N people bought X in the past hour”) are false. Work by @AruneshMathur at #CSCW2019 webtransparency.cs.princeton…
2
Algorithm-in-the-loop decision making should be accurate, reliable, and fair. But people don’t do the last two: they don’t rely more on an algorithm even if it’s more accurate, and rely on their own judgment more when evaluating some groups of people. @benzevgreen at #cscw2019
1
9
Many factors to consider when designing interventions for reducing political polarization (e.g., if an interface seems to question a person’s competency). By @matnel at #cscw2019 tuhat.helsinki.fi/ws/portalf…
1
2
4
On Github and Wikipedia, larger teams produce more work per team member. (Have/can these observations be replicated in an RCT?) Work by @goranmuric, @aabeliuk, @KristinaLerman, @emilio__ferrara #CSCW2019
2
5
18
When given a speech speedometer, native speakers “slowed down” by using shorter sentences, not speaking slower. Unfortunately, the speedometer was distracting, so their speech became less clear to non-native speakers. By Wen Duan. dl.acm.org/citation.cfm?id=3… #CSCW2019
4
When a team that previously broke apart reconvenes in an anonymous setting (members don’t know they’re working together again), it fractures again on some tasks (debating viewpoints) but not others (generating ideas). By @MarkWhiting, @msbernst hci.stanford.edu/publication… #CSCW2019
1
8
Slowing algorithms down can both improve people's judgment and promote task reflection. Work by @joon_s_pk dl.acm.org/citation.cfm?id=3… #CSCW2019
1
5
User behavior may improve after comment deletion simply because they were in a worse mood when they made a deleted comment. Controlling for this, comment deletion still reduces future comment deletions, but does not decrease swearing. @ChenhaoTan chenhaot.com/papers/content-… #cscw2019
3
10