pizza talk iii: online-firestorms - negative dynamics in social media networks

Center for Computa-onal Analysis of Social and Organiza-onal Systems

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Pizza Talk III: Online-Firestorms

Negative Dynamics in Social Media Networks!

Sunbelt XXXIV

Thomas Zorbach & Jürgen PfefferFebruary 21st 2014, St. Pete Beach, FL!

Thomas Zorbach, Jürgen Pfeffer

Online-Firestorms. Negative Dynamics in Social Media Networks!

Agenda Online-‐Firestorms – The dark side of word of mouth

– What is it? –  How does it work? – What can we do?



A German problem? → Poli%cians, celebri%es, companies under heavy fire

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Unse3ledness in Poli6cs and Business “Let’s be;er not say anything. Otherwise, we’ll have an online firestorm tomorrow!” (Angela Merkel, Bundeskanzlerin)

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→ Sign of the participatory culture (Jenkins, 2006)



Anglicism of the Year 2011 in Germany „Shitstorm fills the gap in German vocabulary that was opened up by changes in the public debate culture.“

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→ Kick off for research project



Intercultural Differences “Shitstorm” vs. “Online-‐Firestorm”

→ “Title is not appropriate”



Defini6on “An Online-‐Firestorm is a phenomenon that describes the sudden discharge of large quan%%es of nega%ve word of mouth and complaint behavior against a person, company, or group in social media networks, oHen paired with intense indigna%on that has shiHed its focus from an actual point of cri%cism.“

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Pfeffer, J. & Zorbach, T. & Carley, K.M. (2013). Understanding online firestorms: Nega%ve word of mouth dynamics in social media networks. Journal of Marke%ng Communica%ons



Case Study: Rolling Stone Magazine → Cover showing Dzhokhar Tsarnaev

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Rolling Stone Firestorm -‐ Analysis TwiVer data used:

–  ~1.5% of all tweets are geo-‐tagged –  Long-‐term collec-on of US geo-‐Tweets –  “rollingstone” or “rolling stone”



Empirical Observa6ons/Factors Constant flow of informa6on, short informa6on half-‐life •  Real-‐-me messages in social media create a constant flow of

communica-on •  Many people can be reached within a short period of -me •  Result: Temporal dominance of a single topic •  Communica-on/reac-on cycle

–  Tradi-onal newspaper: 1 day –  Social media: hours or minutes

•  Half-‐Life (Burton and Kebler, 1960) of social media informa-on: –  Hours or even minutes (Fang, 2007).

•  TwiVer: Cri-cal role in the propaga-on of all online firestorms

→ Speed and volume

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Tweets/Day



Rolling Stone Firestorm -‐ Numbers •  Peak: 7/17 + 7/18 •  5,836 tweets, 4,571 different users •  60 users with min. 5 tweets (=473 tweets)



Empirical Observa6ons/Factors No gradualist opinions, absence of discursive interac6ons •  Opinion-‐forming systems without sophis-cated or gradualist opinions •  Re-‐tweet, ‘like’, or ‘+1’ •  Decision-‐making process as binary choices (Schelling, 1973) •  ”either-‐or situa-ons”

→ Binary choices

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Rolling Stone Firestorm -‐ Retweets

RT @LisaLampanelli: Poll: 65% of Americans thought Rolling Stone cover w Boston Bomber was inappropriate.

Other 35% thought John Mayer grew…



Empirical Observa6ons/Factors Friends act as informa6on source and as filter •  Connec-ons tend to be based on homophily (McPherson et al., 2001)

–  similar in age, gender, and socio-‐economic status •  Plus: Technology filters informa-on based on interests •  Filter bubble (Pariser, 2011) is a concept which refers to over-‐

emphasizing the importance of single topics or opinions → Lack of diversity

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Rolling Stone Firestorm -‐ Top users? •  Top 10 user and their number of geo-‐tweets in July 2013:

–  trendinaliaUS 9,741 –  Boston_CP 7,612 –  ChelseaKrost 7,314 –  IndianapolisCP 6,873 –  Logan_Rosalez 3,377 –  JDawgMadden 3,301 –  LisaG_325 3,266 –  MaVhewKeysLive 2,784 –  TimothyODonnel2 2,645 –  GlutenFreeDog 1,848

•  Spammers, bots?



Empirical Observa6ons/Factors Amplified epidemic spreading, network clusters •  Transi-ve link crea-ons (Heider, 1946)

•  Social media systems intensify this effect with link sugges-ons → Network clusters & echo chambers

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A B

C



Empirical Observa6ons/Factors Offline and online media reinforce one another •  Social media are an important informa-on source for tradi-onal

media (Diakopoulos et al., 2012). •  TwiVer is used as “radar” •  “Sharability” triggers prolifera-on in social media

→ Cross media dynamics

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Social Media

Tradi-onal Media



Rolling Stone Firestorm – Media Dynamics •  702 re-‐tweets:

–  35% RT @ny-mes –  24% RT @OpieRadio –  30% RTs from 5 journalists –  Almost all are re-‐tweets from media outlets

RT @nytimes: CVS and Walgreens Ban an Issue of Rolling Stone http://

t.co/6mSRHuT671

RT @OpieRadio: The news shows outrage for the Rolling Stone cover by SHOWING THE

ROLLING STONE COVER. #Dopes



Empirical Observa6ons/Factors Modified opinion adop6on process, dominant network effects Diffusion and adop-on process (Rogers, 1995): •  Knowledge

–  The agent gets in contact with a new opinion or belief. •  Persuasion

–  The agent starts to have a posi-ve or nega-ve stance. Re-‐infec-on. •  Decision

–  The opinion or belief is accepted or rejected. •  Propaga%on

–  The agent starts to ac-vely propagate the opinion or belief. Infec-on. •  Affirma%on

–  Posi-ve feedback encourages, nega-ve feedback destabilizes. → Cogni%ve processes get replaced by network effects!

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Filter Bubble

Echo Chamber

Echo Chamber



Thomas Zorbach

t.zorbach@vm-‐people.de @vmpeople

Jürgen Pfeffer

[email protected] @JurgenPfeffer

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