The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations

Ashfaq Ali Shafin; Khandaker Mamun Ahmed

REVIEW 5 major objections 7 minor 2 cited by

The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations

T0 review · 5 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Russian influence tweets are nine times more toxic than China's.

desk verdict A useful descriptive three-way comparison of sentiment and toxicity in state-sponsored IO tweets, but the headline contrasts outrun the statistics and one machine-translated dataset could be skewing the Iran row. read the letter →

arxiv 2505.07212 v3 pith:IGXBAYKH submitted 2025-05-12 cs.SI

Ashfaq Ali Shafin , Khandaker Mamun Ahmed This is my paper · ORCID

classification cs.SI

keywords state-sponsoredinfluenceoperationssentimentanalysisemotionhatespeechtoxicityTweetNLPPerspectiveAPITwitterdatasets

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the state-sponsored influence operations run by Russia, Iran, and China on Twitter have measurably different emotional signatures: Russian tweets are predominantly negative and toxic, Chinese tweets are mostly neutral or positive, and Iranian tweets blend antagonistic and supportive tones. The evidence comes from two million English tweets sampled from Twitter's publicly released state-affiliated account datasets, labeled with standard NLP tools for sentiment, emotion, hate speech, and toxicity. If the pattern is real, then influence operations are not a single tactic but a menu of calibrated content strategies that differ by national objective, which matters for how platforms triage and counter them.

What carries the argument

Two off-the-shelf classifiers carry the analysis: TweetNLP, which assigns each tweet a sentiment (negative, neutral, positive), an emotion (anger, joy, optimism, sadness), and binary hate-speech and offensive-language flags; and Google's Perspective API, which scores six toxicity dimensions (toxic, severe toxic, profanity, identity attack, insult, threat). The paper's entire argument is the cross-country comparison of the label distributions these tools produce, tabulated in Tables 2–4; no new model or linguistic theory is introduced.

What would settle it

Manually annotate a stratified random sample of, say, 3,000 tweets from each operation for sentiment, emotion, and toxicity in the original languages, with professional translation for Persian; if the Russian-negative versus Chinese-positive versus Iranian-mixed pattern does not emerge in the human labels, the measured differences are classifier artifacts rather than properties of the campaigns. A narrower check: run the same pipeline on original Persian tweets and on their English machine translations, and if the Iranian profile changes substantially across versions, the translation step is responsible for part of the result.

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Extended reading notes

Core claim

On equal-size random samples of 500,000 tweets per operation, the paper reports sharp cross-country contrasts. Russian operators produced the highest negative sentiment (41.2%), the most anger (57.4%), the most hate speech (17.84%), and the highest toxicity (10.69% by Perspective API's overall score), with toxic content more than nine times that of Chinese operations and about 3.5 times that of Iranian operations. Chinese operations were dominated by neutral (59.0%) and positive (34.8%) sentiment and by joy (68.1%). Iranian operations sat in between, and their 2019 dataset shows a clear escalation from 2018: anger rose from 25.6% to 40.1% of tweets, and every Perspective toxicity category increased, suggesting a shift toward more confrontational messaging. The authors read these differences as content strategies tailored to each state's geopolitical ends.

Load-bearing premise

The load-bearing premise is that the off-the-shelf classifiers — trained largely on Western English discourse — produce valid, comparable labels for tweets in different dialects and for machine-translated Persian content, so that the measured cross-country differences reflect actual content strategy rather than model or translation artifacts.

Editorial extensions

If this is right

If the claim holds, toxicity rate is a usable triage signal: Russian operations produce roughly nine times more toxic content than Chinese ones, so accounts that spike on existing toxicity APIs deserve priority review.
The 2018-to-2019 Iranian escalation across all six Perspective toxicity categories suggests that analysts should treat an operation's content profile as time-varying, not fixed.
The three national profiles give platform researchers a content-based typology — polarizing, dual-toned, and image-polishing — that can be tested on newer datasets, such as later Twitter/X influence-operation releases.
Russian reliance on anger and identity attacks implies that counter-influence efforts should focus on de-escalating emotional arousal rather than only correcting factual claims.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct test of the classifier-risk concern would be to human-annotate a few thousand sampled tweets from each operation and compare the human labels with the machine labels; if the cross-country gaps vanish under human annotation, the paper's conclusion is an artifact of the tools.
Because the two Iranian datasets (2018 and 2019) cover different campaigns and periods, the observed escalation could reflect different operations rather than a strategic shift within a single campaign; comparing the same tactic over time within one operation would separate these explanations.
The word clouds hint that each operation attacks specific targets (Chinese attacks on an exiled businessman, Russian attacks on U.S. political figures), so a natural extension is to model target entities directly rather than only aggregate tone.
The paper's focus on English tweets leaves open whether these national styles hold in other languages; comparing each operation's English and native-language output would test whether the tone is a property of the state or of the translated, foreign-facing channel.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a straightforward, transparent descriptive study. It takes Twitter's public IO datasets for China, Iran, and Russia, runs off-the-shelf sentiment/emotion/toxicity classifiers over 500k-tweet random samples, and reports the proportions. The three-way profile—Russia mostly negative and toxic, China mostly positive/neutral, Iran somewhere in between—is a useful descriptive benchmark that I don't think appears in exactly this form elsewhere. The paper is honest about its limitations and makes no pretense of fitting a model, so the circularity burden is close to zero. That's real value, and the result is reproducible in principle since the datasets and tools are public.

The soft spots are exactly where the reader puts them. All comparisons are raw proportions with no confidence intervals and no significance tests; with 500k tweets per group, even trivial differences would be significant, so the lack of error bars matters less for the existence of a difference than for its size, but the paper makes strong claims about 'distinct strategies' from numbers that could move with a different sample or classifier. The datasets span different years (China 2008–2019, Russia 2010–2018, Iran-19 2017–2018), so the country contrast is confounded with time. And the Iran-18 dataset was machine-translated into English, with the translation method and model unspecified and no translation step in Algorithm 1. TweetNLP and Perspective are English/Western-trained, so the Iran-18 row—and the abstract's 'blend of antagonistic and supportive tones'—could be partly a translation artifact. The paper explicitly admits it did no error analysis and that the models are Western-centric, so this isn't a hidden flaw, but it does cap how much weight the comparative claims can carry. The Russia-vs-China gap (9x toxicity) is large and probably robust; the Iran row is the fragile one.

Who is this for? Researchers building or evaluating IO-detection systems who want a quick descriptive baseline on the public Twitter datasets. It doesn't move the methodological needle, but it gives a clean, citable summary. I'd send it to peer review—the topic matters, the data are public, and the limitations are fixable with a more careful statistical treatment and translation validation. With a workshop-level venue it's fine as is; for a stronger venue, ask for matched baselines and confidence intervals.

Referee Report

5 major / 7 minor

Summary. The paper analyzes sentiment, emotion, hate speech, offensive language, and toxicity in 2 million tweets from state-sponsored influence operations (SIOs) by China, Iran, and Russia, using Twitter's publicly released datasets. The authors apply TweetNLP and Perspective API, report per-country proportions in Tables 2–4, and claim that Russian campaigns are predominantly negative and toxic, Iranian operations blend antagonistic and supportive tones, and Chinese activities emphasize positive and neutral rhetoric. The paper is a short descriptive study with no statistical inference or validation of the external classifiers on the target languages.

Significance. If the central claim of distinct affective and rhetorical patterns per country were robust, this would be a useful descriptive contribution to the literature on state-sponsored influence operations, leveraging publicly available Twitter datasets and reproducible off-the-shelf tools. The paper's strengths include a large sample (2M tweets), transparent counts in the tables, and the use of established classifiers (TweetNLP, Perspective API). However, the headline cross-country comparisons are based on raw proportions without confidence intervals or significance tests, one of the four datasets (Iran-18) enters the pipeline as machine-translated text with the translation procedure unspecified, and the classifiers are acknowledged to be trained on Western discourse. These issues directly affect the validity of the 'distinct patterns' claim as currently stated.

major comments (5)

[Section 3 and Algorithm 1] The Iran-18 tweets were machine-translated into English, but the translation method/model is never specified, and Algorithm 1's pipeline contains no translation step. Because TweetNLP and Perspective API are English-only models, the Iran-18 row in Tables 2–4 may reflect translation artifacts (e.g., 'translationese' systematically shifting sentiment, emotion, or toxicity scores) rather than the actual Iranian strategy. Since Iran-18 constitutes one quarter of the analyzed data and feeds directly into the abstract's characterization of Iran as blending antagonistic and supportive tones, the authors must either specify the translation procedure, validate the classifiers on the translated text, or provide a robustness analysis that excludes or re-weights Iran-18.
[Section 5, Tables 2–4] The headline comparisons (e.g., 'Russian IOs contain more than 9 times toxic content than Chinese IOs' in the Introduction, and the cross-country proportionality claims throughout Section 5) are made from samples of 500,000 tweets per dataset with no confidence intervals, standard errors, or significance tests. While the large sample sizes imply tiny sampling variability under simple random sampling, the samples are not independent across time, and the measurement pipeline itself is a source of error. The claim of 'distinct patterns' requires at least a bootstrap confidence interval or a formal comparison (e.g., chi-square or proportion tests) to establish that the observed differences exceed what could arise from sampling or model noise. Without this, the central inference is not supported beyond descriptive observation.
[Section 3 and Table 1] The four datasets cover different time periods (China Feb 2008–Aug 2019, Iran-18 Dec 2010–Aug 2018, Iran-19 Jul 2017–Mar 2018, Russia Aug 2010–Nov 2018). The paper argues that random sampling 'does not introduce temporal misalignment, as the selection was not time-bound,' but this only ensures that sampling is not time-selected; it does not address the inherent non-overlap of the operating periods. Cross-country differences in sentiment or toxicity could therefore reflect temporal shifts in platform policies, world events, or campaign objectives rather than stable country-level strategies. The authors should either restrict the comparison to a common time window, or explicitly discuss and control for temporal confounding in the interpretation of Tables 2–4.
[Section 4 and Section 6] The external classifiers were trained primarily on Western/English discourse, and the paper itself acknowledges in Section 6 that this 'limits the model's ability to accurately capture abusive content across different cultural contexts.' No per-language validation, error analysis, or calibration against human labels is provided, despite the explicit statement that the study 'did not perform a comprehensive error analysis.' For a comparative claim across languages and cultures, this is a load-bearing gap: the measured cross-country differences in hate speech, toxicity, and emotion could be artifacts of differential model accuracy rather than genuine differences in content. At minimum, the authors should report a small manual validation set for each country or compare against existing benchmarks for the tools on non-Western text.
[Section 6, Discussion] The claim that 'Russian IOs accumulate almost twice the amount of hate speech and offensive tweets compared to the IOs distributed by both Iran and China' is directly contradicted by Table 3: Russia has 89,220 hate-speech tweets versus China's 8,668 (a ratio of approximately 10.3), and versus Iran-18's 36,175 (ratio 2.5) and Iran-19's 40,824 (ratio 2.2). The 'almost twice' wording is only approximately correct for Iran-19 and is incorrect for China. This internal inconsistency in the interpretation of the results should be corrected, and the discussion should present the actual ratios rather than a misstated generalization.

minor comments (7)

[Section 3] The description of the sampling procedure is internally inconsistent: the text first states 'we randomly selected 500 thousand English tweets from each of the four different IOs' and then says that for Iran-18, 'due to the limited number of English tweets... we translated the available tweets into English.' Please clarify the exact procedure: were 500k tweets sampled first and then translated, or were all available tweets translated and then a sample drawn?
[Algorithm 1] Algorithm 1 should include the translation step for non-English input, or the pseudocode should note that translation is performed as part of preprocessing for the Iran-18 dataset.
[Section 5] In the toxicity analysis, the phrase 'more than 12% increase' and '7% rise' for Iran-19 relative to Iran-18 are ambiguous: specify whether these are percentage-point differences or relative percentage changes. The table counts imply relative increases of approximately 12.8% for hate speech and 7.8% for offensive language, so the text should be explicit.
[Section 5] There are typos: 'Chinease' should be 'Chinese' in the opening of Section 5, and 'contnet' should be 'content' in the hate speech subsection. Additionally, 'offensive-positive' is an unusual phrasing; consider using 'offensive = true' or defining the term clearly.
[Section 5 and Figure 1] For the word cloud analysis, the methodology for combining hate speech, offensive language, and toxic content into 'abusive speech' is not described. Please state the exact criteria (e.g., union of tweets flagged by any of the three models) and whether the word clouds are based on the 2019 datasets only.
[Section 3] The claim that Twitter published 'over 141 information operation datasets' lacks a citation; please add a reference to the Twitter data archive or the specific release page.
[Section 3, Table 1] The rationale that the 2019 dataset is 'substantially larger' is not uniformly true: Russia-19 has 920,761 tweets, which is smaller than Iran-18's 1,122,936. Please revise the justification for choosing the 2019 datasets.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: external classifiers and public datasets supply all inputs; self-citations are not load-bearing.

full rationale

The paper's claimed derivation chain is a direct measurement pipeline: public Twitter state-sponsored information-operation datasets (Section 3), random sampling of 500,000 English tweets per operation, preprocessing (Algorithm 1), classification by external pre-trained tools TweetNLP and Perspective API (Section 4), and tabulated output counts (Tables 2-4). No free parameter is fitted to the data, no derived quantity is defined in terms of another derived quantity, and the conclusions are descriptive restatements of the observed classifier labels. The self-references [4] and [24] appear only in Related Work and Introduction as motivation or prior-work context; neither is used to justify the content-analysis claims, so they are not load-bearing. The acknowledged limitations in Section 6 (no comprehensive error analysis; models trained primarily on Western discourse) and the translation of Iran-18 tweets in Section 3 are validity threats about measurement equivalence, not circularity: even if the labels are biased or translation artifacts, the paper is not assuming its conclusions as inputs. Algorithm 1's omission of a translation step is an internal methodological inconsistency, but it does not make any result equivalent to its inputs by construction. The paper is self-contained against external benchmarks and its findings are empirical measurements rather than derivations, so no circular step is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities or fitted parameters. Its load-bearing assumptions are about the validity of the external classifiers and the comparability of the public datasets; these are exogenous to the analysis and are not independently validated here.

assumptions (4)

domain assumption TweetNLP and Perspective API labels are valid and comparable across the three corpora, including translated Persian tweets.
Invoked throughout Section 5; the central comparative claims are computed from these labels.
domain assumption Twitter's state-affiliated account datasets correctly identify influence-operation accounts and their tweet timelines.
Stated in Section 3; the entire dataset is taken at face value.
domain assumption A random sample of 500,000 English tweets per dataset is representative of each IO's content and is not temporally biased.
Section 3 claims random sampling preserves representativeness; different durations and time ranges across the four datasets make comparability questionable.
domain assumption English-only analysis sufficiently characterizes each IO's strategy.
Section 3 argues target audiences are English-speaking; this excludes non-English IO content and may bias cross-country comparison.

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Cite this review

Pith. "Pith review of The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations." pith.science (2026). https://pith.science/paper/IGXBAYKH

@misc{pith2026250507212,
  author       = {Pith},
  title        = {Pith review of: The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IGXBAYKH}},
  note         = {Machine review of arXiv:2505.07212}
}

read the original abstract

State-sponsored influence operations (SIOs) have become a pervasive and complex challenge in the digital age, particularly on social media platforms where information spreads rapidly and with minimal oversight. These operations are strategically employed by nation-state actors to manipulate public opinion, exacerbate social divisions, and project geopolitical narratives, often through the dissemination of misleading or inflammatory content. Despite increasing awareness of their existence, the specific linguistic and emotional strategies employed by these campaigns remain underexplored. This study addresses this gap by conducting a comprehensive analysis of sentiment, emotional valence, and abusive language across 2 million tweets attributed to influence operations linked to China, Iran, and Russia, using Twitter's publicly released dataset of state-affiliated accounts. We identify distinct affective and rhetorical patterns that characterize each nation's digital propaganda. Russian campaigns predominantly deploy negative sentiment and toxic language to intensify polarization and destabilize discourse. In contrast, Iranian operations blend antagonistic and supportive tones to simultaneously incite conflict and foster ideological alignment. Chinese activities emphasize positive sentiment and emotionally neutral rhetoric to promote favorable narratives and subtly influence global perceptions. These findings reveal how state actors tailor their information warfare tactics to achieve specific geopolitical objectives through differentiated content strategies.

Figures

Figures reproduced from arXiv: 2505.07212 by the authors.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations
cs.SI 2026-07 conditional novelty 6.0 of 10

In seven state-attributed influence operations, only ~19% of detector-flagged hostile content is identity-directed and dehumanizing/inciting hate; the rest is partisan or geopolitical invective, and the mix varies by ...
Toxicity in State Sponsored Information Operations
cs.SI 2025-07 conditional novelty 5.0 of 10

Analyzing 56 million posts from 42,405 state-linked accounts, toxic posts (1.53% of the total) drew about 6 times more engagement than non-toxic posts, with Russian operations showing the largest effect.

Reference graph

Works this paper leans on

35 extracted references · 23 canonical work pages · cited by 2 Pith papers

[1]

Perspective API

2017. Perspective API. https://www.perspectiveapi.com. Accessed: 2025-01-06

2017
[2]

Mohammad Majid Akhtar, Rahat Masood, Muhammad Ikram, and Salil S Kanhere

work page
[3]

Meysam Alizadeh, Jacob N Shapiro, Cody Buntain, and Joshua A Tucker. 2020. Content-based features predict social media influence operations. Science ad- vances 6, 30 (2020), eabb5824

work page 2020
[4]

Saad Almohaimeed, Saleh Almohaimeed, Ashfaq Ali Shafin, Bogdan Carbunar, and Ladislau Bölöni. 2023. THOS: A benchmark dataset for targeted hate and offensive speech. arXiv preprint arXiv:2311.06446 (2023)

work page Pith review arXiv 2023
[5]

Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017. Deep Learning for Hate Speech Detection in Tweets. In Proceedings of the 26th International Conference on World Wide Web Companion(Perth, Australia) (WWW ’17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 759–760. https:...

doi:10.1145/3041021 2017
[6]

Ranjan Kumar Behera, Monalisa Jena, Santanu Kumar Rath, and Sanjay Misra

work page
[7]

Pascal Brangetto and Matthijs A Veenendaal. 2016. Influence cyber operations: The use of cyberattacks in support of influence operations. In 2016 8th Interna- tional Conference on Cyber Conflict (CyCon) . IEEE, 113–126

work page 2016
[8]

Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System...

work page 2022

Show all 35 references

[9]

Saloni Dash and Tanu Mitra. 2024. Decoding The Playbook: Multi-Modal Char- acterization of Coordinated Influence Operations on Indian Social Media. ACM Journal on Computing and Sustainable Societies 2, 4 (2024), 1–19

2024
[10]

Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated Hate Speech Detection and the Problem of Offensive Language. Pro- ceedings of the International AAAI Conference on Web and Social Media 11, 1 (May 2017), 512–515. https://doi.org/10.1609/icwsm.v11i1.14955

2017 doi
[11]

Gregory Eady, Tom Paskhalis, Jan Zilinsky, Richard Bonneau, Jonathan Nagler, and Joshua A. Tucker. 2023. Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior. Nature...

2023 doi
[12]

Björn Gambäck and Utpal Kumar Sikdar. 2017. Using Convolutional Neural Networks to Classify Hate-Speech. InProceedings of the First Workshop on Abusive Language Online, Zeerak Waseem, Wendy Hui Kyong Chung, Dirk Hovy, and Joel Tetreault (Eds.). Association for Computational Li...

2017 doi
[13]

Ignas Kalpokas. 2016. Influence operations: challenging the social media– democracy nexus. SAIS Europe Journal of Global Affairs 19, 1 (2016), 18–29

2016
[14]

Tobias Keller, Tim Graham, Dan Angus, Axel Bruns, Rolf Nijmeijer, Kristof- fer Laigaard Nielbo, Anja Bechmann, Lisa-Maria Neudert, Nahema Marchal, Samantha Bradshaw, et al. 2020. ‘Coordinated inauthentic behaviour’and other online influence operations in social media spaces.Ao...

2020
[15]

Abdurahman Maarouf, Nicolas Pröllochs, and Stefan Feuerriegel. 2024. The Virality of Hate Speech on Social Media. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 186 (April 2024), 22 pages. https://doi.org/10.1145/3641025

2024 doi
[16]

Thomas Magelinski, Lynnette Hui Xian Ng, and Kathleen M Carley. 2021. A synchronized action framework for responsible detection of coordination on social media. arXiv preprint arXiv:2105.07454 (2021)

2021 arXiv
[17]

Lennart Maschmeyer, Alexei Abrahams, Peter Pomerantsev, and Volodymyr Yermolenko. 2025. Donetsk don’t tell–‘hybrid war’in Ukraine and the limits of social media influence operations. Journal of Information Technology & Politics 22, 1 (2025), 49–64

2025
[18]

Binny Mathew, Ritam Dutt, Pawan Goyal, and Animesh Mukherjee. 2019. Spread of Hate Speech in Online Social Media. In Proceedings of the 10th ACM Confer- ence on Web Science (Boston, Massachusetts, USA) (WebSci ’19). Association for Computing Machinery, New York, NY, USA, 173–1...

2019
[19]

Muhammad Shujaat Mirza, Labeeba Begum, Liang Niu, Sarah Pardo, Azza Abouzied, Paolo Papotti, and Christina Pöpper. 2023. Tactics, Threats & Targets: Modeling Disinformation and its Mitigation.. In NDSS

2023
[20]

Vanessa Molter and Renée DiResta. 2020. Pandemics & Propaganda: How Chinese State Media Creates and Propagates CCP Coronavirus Narratives. https://mi sinforeview.hks.harvard.edu/article/pandemics-propaganda-how-chinese- state-media-creates-and-propagates-ccp-coronavirus-narrat...

2020 doi
[21]

Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci, and Maurizio Tesconi. 2021. Coordinated behavior on social media in 2019 UK general election. In Proceedings of the international AAAI conference on web and social media, Vol. 15. 443–454

2021
[22]

Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong, Alessan- dro Flammini, and Filippo Menczer. 2021. Uncovering coordinated networks on social media: methods and case studies. In Proceedings of the international AAAI conference on web and social media , Vol....

2021
[23]

Hilary Piedrahita-Valdés, Diego Piedrahita-Castillo, Javier Bermejo-Higuera, Pa- tricia Guillem-Saiz, Juan Ramón Bermejo-Higuera, Javier Guillem-Saiz, Juan An- tonio Sicilia-Montalvo, and Francisco Machío-Regidor. 2021. Vaccine hesitancy on social media: Sentiment analysis fro...

2021
[24]

Ruben Recabarren, Bogdan Carbunar, Nestor Hernandez, and Ashfaq Ali Shafin
[25]

Ulrike Reisach. 2021. The responsibility of social media in times of societal and political manipulation. European journal of operational research 291, 3 (2021), 906–917

2021
[26]

Sheikh Muhammad Sarwar and Vanessa Murdock. 2022. Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach. Proceedings of the International AAAI Conference on Web and Social Media 16, 1 (May 2022), 852–862. https://doi.org/10.1609/icwsm.v16i1.19340

2022 doi
[27]

Karishma Sharma, Yizhou Zhang, Emilio Ferrara, and Yan Liu. 2021. Identify- ing coordinated accounts on social media through hidden influence and group behaviours. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1441–1451

2021
[28]

Charlyn Villavicencio, Julio Jerison Macrohon, X Alphonse Inbaraj, Jyh-Horng Jeng, and Jer-Guang Hsieh. 2021. Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes. Information 12, 5 (2021), 204

2021
[29]

Hajime Watanabe, Mondher Bouazizi, and Tomoaki Ohtsuki. 2018. Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection. IEEE Access 6 (2018), 13825–13835. https: //doi.org/10.1109/ACCESS.2018.2806394

2018
[30]

Derek Weber and Frank Neumann. 2020. Who’s in the gang? Revealing coordi- nating communities in social media. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) . IEEE, 89–93

2020
[31]

Derek Weber and Frank Neumann. 2021. Amplifying influence through coordi- nated behaviour in social networks. Social Network Analysis and Mining 11, 1 (2021), 111

2021
[32]

Savvas Zannettou, Tristan Caulfield, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, and Jeremy Blackburn. 2019. Disinformation Warfare: Un- derstanding State-Sponsored Trolls on Twitter and Their Influence on the Web. In Companion Proceedings of The 2019 Worl...

2019
[2021]

Information Processing & Management 58, 1 (2021), 102435

Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management 58, 1 (2021), 102435

2021
[2023]

In 32nd USENIX Security Symposium (USENIX Security 23)

Strategies and vulnerabilities of participants in venezuelan influence operations. In 32nd USENIX Security Symposium (USENIX Security 23) . 6683– 6700
[2024]

In Proceedings of the 19th ACM Asia Conference on Computer and Communications Security (Singapore, Singapore) (ASIA CCS ’24)

SoK: False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations. In Proceedings of the 19th ACM Asia Conference on Computer and Communications Security (Singapore, Singapore) (ASIA CCS ’24) . Association for Computing Machinery, New Yo...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Perspective API

2017. Perspective API. https://www.perspectiveapi.com. Accessed: 2025-01-06

2017

[2] [2]

Mohammad Majid Akhtar, Rahat Masood, Muhammad Ikram, and Salil S Kanhere

work page

[3] [3]

Meysam Alizadeh, Jacob N Shapiro, Cody Buntain, and Joshua A Tucker. 2020. Content-based features predict social media influence operations. Science ad- vances 6, 30 (2020), eabb5824

work page 2020

[4] [4]

Saad Almohaimeed, Saleh Almohaimeed, Ashfaq Ali Shafin, Bogdan Carbunar, and Ladislau Bölöni. 2023. THOS: A benchmark dataset for targeted hate and offensive speech. arXiv preprint arXiv:2311.06446 (2023)

work page Pith review arXiv 2023

[5] [5]

Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017. Deep Learning for Hate Speech Detection in Tweets. In Proceedings of the 26th International Conference on World Wide Web Companion(Perth, Australia) (WWW ’17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 759–760. https:...

doi:10.1145/3041021 2017

[6] [6]

Ranjan Kumar Behera, Monalisa Jena, Santanu Kumar Rath, and Sanjay Misra

work page

[7] [7]

Pascal Brangetto and Matthijs A Veenendaal. 2016. Influence cyber operations: The use of cyberattacks in support of influence operations. In 2016 8th Interna- tional Conference on Cyber Conflict (CyCon) . IEEE, 113–126

work page 2016

[8] [8]

Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System...

work page 2022