measuring*polariza/on** onsocialmedia - ETH Z · measuring*polariza/on** onsocialmedia michaelmathioudakis computer)science) aalto)university) hp:// . 2 socialmedia Michael)Mathioudakis)

measuring polariza/on on social media michael mathioudakis

computer science aalto university

h6p://www.michalis.co

2

social media

Michael Mathioudakis

consume content news about friends, poli/cs, favorite ar/sts

generate content share experiences, interes/ng ar/cles

interact with others comment, rate, and discuss

hundreds of millions of ac/ve users

why study social media

a window into the thoughts and ac/ons of people

how people spend their /me what a6racts their a6en/on

what they think about poli/cal issues how they interact socially

unique opportunity for social scien/sts

study human behavior at large scale & fine detail

Michael Mathioudakis 3

but you are a computer scien/st

develop algorithms to extract insights from social media

two challenges

1-‐ volume of data 2-‐ complexity of social behavior

calls for complex models (e.g., graphs) that require fast algorithms

solu/on

algorithmic methods, soIware tools and systems for large-‐scale computa/on

analyze efficiently & automa/cally large amounts of data


5

no informa/on barriers

ci/zen journalism

social connec/vity …

…

harassment

fake news

polariza/on

social media: good and bad sides


polariza/on

poli/cal or social polariza/on ‘the act of separa-ng or making people separate into

two groups with completely opposite opinions’*

related term: controversy

‘public discussion and argument about something that many people strongly disagree about’*

*oxford english dic-onary


why study polariza/on?

polariza/on can be linked to adverse effects

social segmenta/on stereotypes

echo chambers

goal understand and mi/gate them


8

0 10 20 30 40 50 60 70 80 90 100

Donald Trump Approval Ra0ng % February 2017

Democrats Republicans


9

ideology of USA public source: Pew Research Center

1994

2014 Democrats and Republicans

have driIed apart


why study polariza/on on social media

extract insights on the polariza/on process

some specific ques/ons

what are the polarizing issues? does polariza/on increase over /me?

we saw one such instance: US public how could we ‘nudge’ people away from

extreme polariza/on? is polariza/on linked to echo chambers? do social media increase polariza/on?


this

talk

in this talk…

twi6er

algorithms to measure it

long-‐term study

next steps

11

polariza/on


12

retweets tweet

replies

connecCons

interacCons Michael Mathioudakis

twi6er

microblogging plaXorm since 2006; 300 million ac/ve users users post short messages -‐ ‘tweets’

13 Michael Mathioudakis

global structure of interac/ons

nodes

14

user accounts

edges retweets replies connec/ons

interac/on graph


1% sample of all tweets from the internet archive

data

in what follows…


twi6er algorithms to measure it

long-‐term study next steps

polariza/on

Quan/fying Controversy in Social Media. WSDM 2016. With Kiran Garimella, Gianmarco De Francisci Morales, Aris/des Gionis.

how polarized is a discussion?

e.g., obamacare in January 2017


how could we approach this ques/on?

content? do opposing sides say different things?

sen/ment? do polarized topics exhibit wider

range of emo/ons?

interac/ons? do people interact more with their own side?


let’s try this

method template

build an interac/on graph several types to try

retweets, replies, connec/ons

is the interac/on graph polarized?

output: polariza/on score

18

not polarized polarized two sides, well separated


pipeline


topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon

what type of interac/on graph should we use?

how to find two sides in the

graph?

how to measure the separa/on between

two sides?

do we iden/fy polarized

discussions?

retweets replies

connec/ons


topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon



graph?


two sides?


discussions?

21

par//on into two sides


topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon



graph?


two sides?


discussions?

par//oning

many exis/ng algorithms spectral clustering label propaga/on me/s


well connected

badly connected

random walk controversy (RWC)


topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon



graph?


two sides?


discussions?


polariza/on score how difficult to reach c from B

24

A and B: two sides c and d: central nodes

and values -‐1 to 1

the more difficult the higher the score how difficult to reach d from A


c d

A B

random walk

random walk


discrete process on a graph step 1, 2, 3, …

at each step, we are at one node

at next step, we move to a nearby node -‐ following an edge at random

specify where the walk starts (e.g., at c) where the walk ends (e.g., at d)

c d


26

A and B: two sides c and d: central nodes


c d

A B

random walk start: random node of A / B 50% chance that we do either end: at central nodes c of side A or d of side B

PXY = P(started in X | ended in Y)

consider two independent instances one ending at the central node of side A, the other at the central node of side B

RWC = PAA PBB -‐ PBA PAB

both random walks started in the side where they ended

both random walks started in a side other than where they ended

values?



topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon



graph?


two sides?


discussions?


topic

evaluaCo

n

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon



graph?


two sides?


discussions?

evalua/on

non-‐polarized topics

indian beelan, nemtsov protests, netanyahu US congress speech,

bal/more riots, ukraine polarized topics

germanwings plane crash, sxsw, mother’s day, jurassic world movie,

na/onal kissing day


popular topics of 2015 graphs with 1 – 150 thousand nodes

examples:

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon

results


does the pipeline dis/nguish polarized from non-‐polarized topics?

retweets replies

connec/ons

two sides random walk controversy (RWC)

topic

(if we use retweets) yes!

results

nemtsov protests

indian beef ban

sxsw conference

germanwings plane crash

interac/on graphs: retweets 31 Michael Mathioudakis

polarized

topics non-‐polarized topics

high RWC low RWC

results

32

retweets replies

interac/on graphs for nemtsov protests


an algorithmic way to quan/fy polariza/on


retweets two sides random walk controversy (RWC)

topic

based on structure of interac/ons language-‐independent

Graph Building

Graph Partitioning

Controversy Measure

PolarizaCon

new method can be deployed in the wild

in what follows…




polariza/on

Graph Building

Graph Partitioning

Controversy Measure

The Ebb and Flow of Controversial Debates on Social Media. ICWSM 2017. The Effect of Collec/ve A6en/on on Controversial Debates on Social Media. WebScience 2017. With Kiran Garimella, Gianmarco De Francisci Morales, Aris/des Gionis.

PolarizaCon

polariza/on over /me

has polariza/on around controversial topics increased over /me?


polariza/on over /me

data 1% sample of all tweets from the internet archive

September 2011 to September 2016

method for a given topic (e.g., obamacare)

retrieve related tweets build an interac/on graph for each day

using retweets measure RWC score


RWC vs Time


September 2011

September 2016

volume of ac/vity spikes at major events



does polariza/on spike with volume? measure rwc vs volume of ac/vity


RWC vs Volume

higher volume

higher con

troversy


higher volume higher volume

RWC vs Volume


higher volume

higher con

troversy

rwc

summary




polariza/on

why study polariza/on on social media

extract insights on the polariza/on process

some specific ques/ons

what are the polarizing issues? does polariza/on increase over /me?

we saw one such instance: US public how could we ‘nudge’ people away from

extreme polariza/on? is polariza/on linked to echo chambers? do social media increase polariza/on?


this

talk

next steps

thank you!


Beanplot of RWC on retweet networks

Michael Mathioudakis 46 0.0

0.2

0.4

0.6

0.8

1.0

BCC EC GMCK MBLB RWC

Cont

rove

rsy

scor

e

ControversialNon−controversial

controversial non-‐controversial RW

C

me/s

par//oning scheme george karypis et.al.

phase 1

coarsening (random or connec/vity-‐based)

phase 2 2-‐way-‐par//oning phase

(spectral clustering) phase 3

uncoarsening & refining phase


other polariza/on measures

betweenness of cross-‐cusng edges (BCC) frac/on of shortest paths that include an edge

embedding distance

2d embedding produced by ForceAtlas2

48

average in-‐cluster distances

average cross-‐cluster distance


Polariza/on scores on retweet networks

0.0

0.2

0.4

0.6

0.8

1.0

BCC EC GMCK MBLB RWC

Con

trove

rsy

scor

e

ControversialNon−controversial


… beyond polariza/on

develop methods, tools and systems to help social scien/sts

study social ac/vity on the web

computa/onal social science social influence

understand how people experience their ci/es track poli/cal-‐social-‐ethnic trends


Documents

measuring*polariza/on** on*social*media - ETH Z · measuring*polariza/on** on*social*media michael*mathioudakis computer)science) aalto)university) hp:// . 2 social*media Michael)Mathioudakis)

measuring*polariza/on** onsocialmedia - ETH Z · measuring*polariza/on** onsocialmedia michaelmathioudakis computer)science) aalto)university) hp:// . 2 socialmedia Michael)Mathioudakis)