Social computing CS 347 Michael Bernstein Announcements Abstract - - PowerPoint PPT Presentation

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.

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Recall…

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Social interactions define the system Technical infrastructure defines the system The two components are interrelated and both responsible

Sociotechnical system

Recall…

Social computing behavioral science as offering a new lens onto traditional social science theory

Predicting tie strength with social media Social capital’s relationship to social media use

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YOU READ THIS

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

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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

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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

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How social media influences us

The Good (?) Stuff

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

• ther 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

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“Social network site” [boyd and Ellison 2007]

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]

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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]

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How does SNS use impact…

New models for how we interact

The Good Stuff

Discussion

[Viégas and Donath, CHI ’99]

Chat circles: “narrowcasting” via physical proximity

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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)

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This work introduces an algorithm that decomposes words and nondeterministically creates homophones that are likely to create confusion for censors

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!

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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

• r negative initial posts in a thread

Measure resulting trolling behavior

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35% troll comments 49% troll comments 47% troll comments 68% troll comments Positive Mood Negative Mood Positive Norm Negative Norm The effects compound.

Antisocial behavior tracks human diurnal mood patterns

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Proportion of flagged posts on CNN.com

0.03 0.033 0.036 0.039 0.042

Time of day

6 12 18 24

Daily negative affect [Golder & Macy 2011]

Why does this happen? [1min]

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

• ffline.

(It would also imply that we write harsher CS 347 commentaries online than we might share in class, or to the author’s face.)

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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]

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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

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Result: many accounts left; those that stayed, did not introduce hate speech into

• ther subreddits they migrated into

How do we manage trolls?

[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!)

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Y O U R E A D T H I S

Recall: friendsourced moderation [Mahar, Karger and Zhang ’18]

Friends intercept harassing emails before they appear in your inbox

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Disinformation

The Bad Stuff

FAEK NEWS!!!1one

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.

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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

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From Starbird@Stanford 2019

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

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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

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Participants worked on a computer to learn facts about pop culture. Afterwards, participants take a

• test. The computer messages at the end that it

“did a good job”.

this machine did a good job

The Media Equation

[Reeves and Nass 1996]

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Participants were then asked to evaluate the computer’s helpfulness. Half of them evaluated

• n the same computer, half were sent across the

room to evaluate on a second computer. Participants worked on a computer to learn facts about pop culture. Afterwards, participants take a

• test. The computer messages at the end that it

“did a good job”.

this machine did a good job

The Media Equation

[Reeves and Nass 1996]

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The evaluations were more positive when evaluating from the same computer than when evaluating from another computer …almost as if people were being nice to the computer’s face and meaner behind its back. When asked about it, participants would swear that they were not being nicer to its face; that it was just a computer.

this machine did a good job

The Media Equation

[Reeves and Nass 1996]

The same principle has been replicated many times…

For example, putting a blue wristband on the user and a blue sticker on the computer, and calling them “the blue team”, resulted in participants viewing the computer as more like them, more cooperative, and friendlier [Nass, Fogg, and Moon 1996] The authors’ purported method: find experiments about how people react to people, cross out the second “people”, write in “computer” instead, and test it.

The reaction is psychological and built in to us: the “social and natural responses come from people, not from media themselves”

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The Media Equation

[Reeves and Nass 1996]

Algorithms among us

Algorithms increasingly mediate content in socio-technical systems. Many are unaware of these algorithms. [Eslami 2015] People respond to these algorithms by creating folk theories: intuitive, informal theories to explain the system’s behavior [DeVito et al. 2018, French and Hancock 2017]. Facebook’s feed algorithm is:

Transparent platform (4.4 out of 7 on a Likert scale) Unwanted observer (4.4 out of 7) Corporate black box (3.6 out of 7) Rational assistant (2.9 out of 7)

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YOU READ THIS

The replicant effect

[Jakesch et al. 2019]

When the environment is all-AI or all-human, people rate the content as trustable — or at least calibrate their trust. However, when the environment is a mix of AI and human actors, and you can’t tell which, the content believed to be from AIs is trusted far less.

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For more: take CS 278

Today was focused on recent research results in the space

Discussion

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