Social computing CS 347 Michael Bernstein
Announcements Abstract drafts due Friday We recommend getting feedback in office hours this week and next! We will work hard with you to help shape the project. 2
Recall… Sociotechnical system The two components are interrelated and both responsible Technical infrastructure defines the system Social interactions define the system 3
Recall… Social computing behavioral science as offering a new lens onto traditional social science theory Predicting tie strength with social media YOU READ THIS Social capital’s relationship to social media use 4
Recall… Social computing systems as supporting new, or more pro-social, forms of social interaction. Examples: Q&A systems — Answer Garden evolves into StackOverflow and Quora Collective action — Dynamo, SquadBox 5
Today The Good Stuff Encouraging contributions Social media’s influence on us New models for online interaction The Bad Stuff Trolls, harassment, and moderation Disinformation AIs in social environments 6
Encouraging contributions The Good Stuff
Combating social loafing [Beenen et al., CSCW ’04] Social loafing: why should I contribute if many others could as well? Hypothesis: calling out uniqueness will increase participation Method: rating campaign on MovieLens (think: IMDB ratings) “As someone with fairly unusual tastes, you have been an especially valuable user of MovieLens [...] You have rated movies that few others have rated: [...]” Result: participants in the uniqueness condition rated 18% more movies 8
How social media influences us The Good (?) Stuff
“Social network site” [boyd and Ellison 2007] Does SNS use impact tie strength? [Burke and Kraut 2014] “The Internet Paradox” [Kraut 1998]: people are more lonely the more they use the internet. Does Facebook use really displace other forms of social interaction? Method: longitudinal time-series analysis of self-reported tie strength, compared to Facebook activity logs Result: composed pieces (comments, posts, messages) increase it substantially, but one-click pieces (likes) only by a bit 10
How does SNS use impact… Well-being? “Receiving targeted, composed communication from strong ties was associated with improvements in well-being while viewing friends' wide-audience broadcasts and receiving one-click feedback were not.” [Burke and Kraut 2016] Job hunting? “Most people are helped through one of their numerous weak ties but a single stronger tie is significantly more valuable at the margin ” [Gee, Jones and Burke 2017] 11
How does SNS use impact… Exposure to diverse political news? “We find strong evidence that [social media] foster more varied online news diets . The results call into question fears about the vanishing potential for incidental news exposure in digital media environments.” [Scharkow et al. PNAS 2020] “We […] quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content .” [Bakshy, Messing, and Adamic Science 2015] 12
New models for how we interact The Good Stuff
Discussion [Viégas and Donath, CHI ’99] Chat circles: “narrowcasting” via physical proximity 14
Combating censorship [Hiruncharoenvate, Lin and Gilbert, ICWSM ’15] The Chinese government censors sensitive topics on social media However, homophones can be difficult for censors to distinguish from intended use 和谐 (slang ‘censorship’) vs. 河蟹 (river crab) This work introduces an algorithm that decomposes words and nondeterministically creates homophones that are likely to create confusion for censors 15
Aardvark: social search [Horowitz and Kamvar, WWW ’10] Technical challenge: question routing over IM Use a joint model over topical relevance and social distance Interesting equilibrium: people were more willing to answer questions than ask them! 16
Trolls, harassment, and moderation The Bad Stuff
Anyone can become a troll [Cheng et al., CSCW 2017] Popular press: trolling is confined to an antisocial sociopathic minority. But is this true? Experiment: put people in a good or bad mood, show them positive or negative initial posts in a thread Measure resulting trolling behavior 18
Positive Mood Negative Mood Positive Norm 35% troll comments 49% troll comments The effects compound. Negative Norm 47% troll comments 68% troll comments 19
Antisocial behavior tracks human diurnal mood patterns 0.042 flagged posts on [Golder & Macy 2011] Daily negative affect Proportion of 0.039 CNN.com 0.036 0.033 0.03 0 6 12 18 24 Why does this happen? [1min] Time of day 20
Online disinhibition effect [Suler 2004] A major theory as to why trolling happens: when we interact online, we say and do things that we would not do IRL. We self-disclose more, and we act out more. This is known as the online disinhibition effect : we have less inhibition when online. Online disinhibition would imply that we do troll more online than offline. (It would also imply that we write harsher CS 347 commentaries online than we might share in class, or to the author’s face.) 21
Anonymity Should we use real names? Pseudonyms? Let people be anonymous? This is a classic, old question in the field. Anonymous environments create greater disinhibition, which results in more trolling, negative affect, and antisocial behavior [Kiesler et al. 2012] On the other hand, anonymity can foster stronger communal identity [Ren, Kraut, and Kiesler 2012] and more creativity [Jessup, Connolly, and Galegher 1990] 22
How do we manage trolls? [Chandrasekharan et al., CSCW 2018] Question: does banning bad behavior help, or just relocate the behavior? Dataset: Reddit banned /r/CoonTown and /r/FatPeopleHate as violating its hate speech policy Result: many accounts left; those that stayed, did not introduce hate speech into other subreddits they migrated into 23
Y O U How do we manage trolls? R E A D T H I S [Seering et al., CSCW 2017] Moderating content or banning substantially decreases negative behaviors in the short term on Twitch. Analysis: interrupted time series What happens to the channel right before vs. right after a moderator’s injunction? Result: the behaviors of high-status users has ripple effects on others’ behaviors. It can reduce bad behavior (or amplify bad behavior!) 24
Recall: friendsourced moderation [Mahar, Karger and Zhang ’18] Friends intercept harassing emails before they appear in your inbox 25
Disinformation The Bad Stuff
FAEK NEWS!!! 1 one Misinformation spreads: Reddit’s Boston Bomber rumors were corrected, but the corrections spread too slowly. [Starbird et al. 2014] Investigation of rumors spread on Twitter over eleven years… [Vosoughi, Roy, and Aral 2018] The top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to more than 1000. Falsehoods also diffused faster than the truth. Bots accelerated true and false news at the same rate, so false news is spreading more virally than truth because humans, not bots, are spreading it. 27
Is it really Russian trolls? Pink — anti-White Helmet accounts on Twitter — are dominant in volume. But, not bots and trolls: lots of journalists aligned with Syrian and Russian government interests, Syrian and Russian government members, and alternative media It looks more like activism than deliberate disinformation From Starbird@Stanford 2019 28
Disinformation campaigns [Starbird, Arif, and Wilson 2019] The question is often posed: can’t we train classifiers to identify pieces of disinformation and automatically remove them? But the problem is, an individual piece of content is hard to disambiguate. Starbird’s argument: it’s much more effective to study and classify disinformation campaigns — a collection of information actions 29
AIs in social environments
The Media Equation [Reeves and Nass 1996] People react to computers (and other media) the way they react to other people We often do this unconsciously, without realizing it 31
The Media Equation [Reeves and Nass 1996] Participants worked on a computer to learn facts this machine about pop culture. Afterwards, participants take a did a good job test. The computer messages at the end that it “did a good job”. 32
The Media Equation [Reeves and Nass 1996] Participants worked on a computer to learn facts this machine about pop culture. Afterwards, participants take a did a good job test. The computer messages at the end that it “did a good job”. Participants were then asked to evaluate the computer’s helpfulness. Half of them evaluated on the same computer, half were sent across the room to evaluate on a second computer. 33
Recommend
More recommend
