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Effects of Social Bots in the Iran-Debate on Twitter

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Andree Thieltges, Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich

Social and Information Networks · Submitted 25 May 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim By using text mining methods, we show that these social bots are responsible for negative sentiment in the debate.

how it is stated · asserted 1.00 read the record →

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arXiv ID1805.10105 (version 1)
Pith IDF6CKMKP5, Pith's permanent record of this paper and its review
CitationAndree Thieltges, Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, Simon Hegelich. "Effects of Social Bots in the Iran-Debate on Twitter." arXiv:1805.10105 (2018). https://pith.science/paper/F6CKMKP5
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,631 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.06 · human participants 0.09 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.83 · compares with prior results 0.07 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

2018 started with massive protests in Iran, bringing back the impressions of the so called "Arab Spring" and it's revolutionary impact for the Maghreb states, Syria and Egypt. Many reports and scientific examinations considered online social networks (OSN's) such as Twitter or Facebook to play a critical role in the opinion making of people behind those protests. Beside that, there is also evidence for directed manipulation of opinion with the help of social bots and fake accounts. So, it is obvious to ask, if there is an attempt to manipulate the opinion-making process related to the Iranian protest in OSN by employing social bots, and how such manipulations will affect the discourse as a whole. Based on a sample of ca. 900,000 Tweets relating to the topic "Iran" we show, that there are Twitter profiles, that have to be considered as social bot accounts. By using text mining methods, we show that these social bots are responsible for negative sentiment in the debate. Thereby, we would like to illustrate a detectable effect of social bots on political discussions on Twitter.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 2018 started with massive protests in Iran, bringing back the impressions of the so called "Arab Spring" and it's revolutionary impact for the Maghreb states, Syria and Egypt. background 1.00 0.04 0.02 asserted 1.00
s1 Many reports and scientific examinations considered online social networks (OSN's) such as Twitter or Facebook to play a critical role in the opinion making of people behind those protests. background 1.00 0.04 0.02 asserted 0.78
s2 Beside that, there is also evidence for directed manipulation of opinion with the help of social bots and fake accounts. background 1.00 0.07 0.04 asserted 0.85
s3 So, it is obvious to ask, if there is an attempt to manipulate the opinion-making process related to the Iranian protest in OSN by employing social bots, and how such manipulations will affect the discourse as a whole. gap 0.70 0.05 0.04 asserted 0.35
s4 Based on a sample of ca. 900,000 Tweets relating to the topic "Iran" we show, that there are Twitter profiles, that have to be considered as social bot accounts. result 0.73 0.95 0.13 asserted 1.00
s5 By using text mining methods, we show that these social bots are responsible for negative sentiment in the debate. result 0.99 0.94 0.60 asserted 1.00
s6 Thereby, we would like to illustrate a detectable effect of social bots on political discussions on Twitter. interpretation 0.81 0.65 0.58 hedged possibility 0.48

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from analysis of existing data 0.96.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

2018 started with massive protests in Iran, bringing back the impressions of the so called "Arab Spring" and it's revolutionary impact for the Maghreb states, Syria and Egypt. Many reports and scientific examinations considered online social networks (OSN's) such as Twitter or Facebook to play a critical role in the opinion making of people behind those protests. Beside that, there is also evidence for directed manipulation of opinion with the help of social bots and fake accounts. So, it is obvious to ask, if there is an attempt to manipulate the opinion-making process related to the Iranian protest in OSN by employing social bots, and how such manipulations will affect the discourse as a whole. Based on a sample of ca. 900,000 Tweets relating to the topic "Iran" we show, that there are Twitter profiles, that have to be considered as social bot accounts. By using text mining methods, we show that these social bots are responsible for negative sentiment in the debate. Thereby, we would like to illustrate a detectable effect of social bots on political discussions on Twitter.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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