Samenvatting Politics, Media And Communication (7322E060FY)

INTRODUCTION

Diamond (2010)
- Liberation Technology:
o The Orange Revolution (2004): Oekraïne
 Verkiezingen beïnvloed
 Via sms onderling contact tussen mensen hierover
 Bottom-up  grote protesten
 Uiteindelijk: omverwerping regime
o Green movement (2009): Iran
 Geloof dat verkiezingen beïnvloed waren
 Via social media + sms mensen bij elkaar gebracht
 Uiteindelijk geen omverwerping regime
 Via de platformen mensen gevonden + gearresteerd
 Repression techniques
o Arab Spring
 Via social media snel opgeroepen tot demonstraties
 Tunesië + Egypte: omverwerping regime
 Andere landen: niet, slecht einde
 Gezamenlijke taal  hielp
o Obama/Trump: nieuwe manier scial media gebruik
 Top-down ipv bottom-up
 Social media ingezet om mensen bijeen te krijgen, donaties, nieuws zo
uitlichten dat je er goed uitziet
 Obama-campagne (2008): miljard euro uitgegeven
 Oa hoe aandacht van mensen vast te houden op internet, wat
mensen interessant vinden, etc.
 Trump-campagne (2016): gebruikt technologie op nieuwe manier
 Negatieve, agressieve toon
 Mobilize supporters
o Chinese muur:
 Social media-activiteit burgers monitoren
 Framen: mensen krijgen geld om positief te berichten over dingen die
gebeuren in het land


Deibert (2019)
- 3 painful truths about social media
o The social media business is built around personal-data surveillance
 Products ultimately designed to spy on us  push personalized
advertising in our direction
o We have consented to this, but not willingly
 Social media are designed as addiction machines
 Expressly designed to draw upon our emotions
o The attention-grabbing algorithms underlying social media also propel
authoritarian practices that aim to show confusion, ignorance, prejudice and
chaos

,  Facilitating manipulation and undermining accountability
 The fine-grained surveillance that companies perform for economic
reasons is a valuable proxy for authoritarian control

FILTER BUBBLES AND ECHO CHAMBERS

Barbera et al. (2015)
- Production homogeneous networks through social media depends on the topic and
the individual
o DV: Polarization
 Percentage of retweets among ideologically similar
 Degree of ideological homogeneity in communities of users detected
in the research network
 Average ideological extremity of the content that was spread through
retweets
o IV:
 Ideology
 Topic (political vs non-political)
o Method: Exploring dataset of 150 million tweets
o Main claims:
 Filter bubbles/echo chambers arise sometimes, depending on the
topic
 Ideological asymmetry: Liberals retweet more conservative tweets
than vice-versa

o Grafiek: als je dichtbij elkaar staat, ben je het heel erg eens met de mensen
om je heen.
 Grafiek a: heel gepolariseerd, duidelijk 2 kampen. Tussen de kampen
veel minder contact dan binnen de kampen.
 Grafiek b: weinig interactie (weinig retweets etc), weinig gepolariseerd




Q&A

, - How to measure ideology?
o Ideology can be seen as a latent concept (= set of beliefs on the world)
 Barbera et al. (2015)’s method is new and uses a logic of proximity (no
access to people, only data). Assumption that proximity (with
someone on social media) and ideology relate (=homophily)
 Validity checks in Figure 1
 Proximity is a continuum, but interaction is measured as going
from Democrat  Republican, not from extreme 
center

- Does social media create homogeneous topics?
o It depends (who, what and how)
 Barbera et al (2015): it depends on the topic and the individual

Dahlgren et al. (2015)
- DV:
o Selective exposure
 Traditional and online news media
o Political attitudes (ideological leaning)
- IV:
o Selective exposure
 Traditional and online news media
o Political attitudes (ideological leaning)
- ! Onafhankelijke en afhankelijke variabele veranderen steeds van plaats

- Method: Three-wave panel study

- Main claim: People do seek-out ideologically consistent news (selective exposure) but
the effects are minor
- Additional claim: Attitude-consistent news exposure reinforces ideological leaning
over time
- Additional claim: Relatively low ideological selective exposure
- Additional claim: Significant degree of cross-cutting news exposure online


Vaccari et al. (2016)
- DV:
o Level of engagement (H1) (with supportive networks  H3)
o Composition of discussion network (H2)
- IV:
o Type of network (supportive, oppositional, mixed) (H1)
o Online vs offline (H2)
o Quantity of sent messages (H3)  intensity of online political involvement
- Method: Survey
- Main claim: Offline dynamics are similar to online dynamics. People who only want to
hear supportive messages offline, also behave this way online. People with a higher
political interest are more likely to select into supportive networks.