This study investigates how English-language Twitter discourse surrounding the #MahsaAmini protests (October 2022–February 2023) organized into distinct yet interconnected thematic communities within civil society networks. Combining Louvain community detection, sentiment profiling (via VADER), and semantic validation (Jensen–Shannon Divergence, MANOVA, cosine similarity), we map five protest “streams” reflecting modular issue alignments, from Documentation of events involving violence and On-the-Ground Mobilization to Monarchist Advocacy & Diaspora Political Engagement and Anti-IRGC Campaigns. While these communities differ in topical focus, their shared affective tone and overlapping lexicons point to modular polyvocality, a discourse structure characterized by thematic differentiation within broader rhetorical and affective coherence. This pattern represents polyvocal consensus rather than fragmentation. We interpret these findings through the lens of connective action, networked publics, and digital diaspora activism, arguing that the discourse reflects a transnational, modular, and affectively aligned subset of protest-oriented civil society mobilization within constrained information environments. This work contributes to debates on hybrid dissent, algorithmic visibility, and the structure of protest discourse in controlled digital contexts.
AhmedS.JaidkaK. (2013). Protests against #delhigangrape on Twitter: Analyzing India's Arab spring. eJournal of eDemocracy and Open Government, 5(1), 28–58.
2.
AkbariA. (2022). Iran: Digital spaces of protest and control piloting report on the guide on digitally-mediated assemblies. European center for not-for-profit law stichting (ECNL). European Center for non profit law.
3.
Al-azawiR.SafaaO. (2022). Feature extractions and selection of bot detection on Twitter: A systematic literature review. Inteligencia Artificial, 25(69), 57–86. https://doi.org/10.4114/intartif.vol25iss69pp57-86
4.
AtefiniaF.MirnezamiS. R. (2025). Exploring Iranian sentiments on the paris agreement: Insights from Twitter. Heliyon.
BarberáP.JostJ. T.NaglerJ.TuckerJ. A.BonneauR. (2015). Tweeting from left to right: Is online political communication more than an echo chamber?Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620
7.
BennettW. L.SegerbergA. (2012). The logic of connective action: Digital media and the personalization of contentious politics. Information, Communication & society, 15(5), 739–768.
8.
BirdS.KleinE.LoperE. (2009). Natural language processing with python: Analyzing text with the natural language toolkit. O'Reilly Media, Inc.
9.
BoydD. (2010). Streams of content, limited attention: The flow of information through social media. EDUCAUSE Review.
CampbellW. M.DagliC. K.WeinsteinC. J. (2013). Social network analysis with content and graphs. Lincoln Laboratory Journal, 20(1), 61–81.
12.
CastilloC. (2016). Big crisis data: Social media in disasters and time-critical situations. Cambridge University Press.
13.
CastilloC.MendozaM.PobleteB. (2011). Information credibility on Twitter. In Proceedings of the 20th international conference on world wide web. (pp. 675–684). Association for Computing Machinery.
14.
DavisC. A.VarolO.FerraraE.FlamminiA.MenczerF. (2016). BotOrNot: A system to evaluate social bots. In Proceedings of the 25th international conference companion on world wide web (pp. 273–274). Association for Computing Machinery.
15.
DemirelS.GündüzU. (2022). World health organization's Twitter use before and during Covid-19 pandemic. World Health Organization.
16.
DemirelS.Kahraman GökalpE.GündüzU. (2024). A text mining analysis of the change in status of the Hagia sophia on Twitter: The political discourse and its reflections on the public opinion. Atlantic Journal of Communication, 32(1), 63–90. https://doi.org/10.1080/15456870.2022.2093354
17.
DunningT. E. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.
18.
EtemadifardS. M.KhazaeiT. (2021). Divergent heterogeneities in Iranian migration in France: Semantic and theoretical limitations of Diaspora. International Migration, 59(4), 207–220. https://doi.org/10.1111/imig.12779
FranzkeA. S.BechmannA.EssC., et al. (2020). Internet Research: Ethical Guidelines 3.0. AoIR (The International Association of Internet Researchers.
21.
FreelonD.WellsC. (2020). Disinformation as political communication. Political communication.
22.
GerbaudoP. (2012). Tweets and the streets: Social media and contemporary activism. Pluto Press.
23.
GhorashiH.BoersmaK. (2009). The 'Iranian diaspora' and the new media: From political action to humanitarian help. Development and Change, 40(4), 667–691. https://doi.org/10.1111/j.1467-7660.2009.01567.x
24.
GolkarS. (2015). Student activism, social media, and authoritarian rule in Iran. In The whole world is texting: Youth protest in the information age (pp. 61–79). Sense Publishers.
25.
GuestG.BunceA.JohnsonL. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
26.
GündüzU.DemirelS.TombulI. (2024). Exploring the concept of financial domination on social media: Sentiment and text analysis on Twitter. Atlantic Journal of Communication, 32(4), 602–625. https://doi.org/10.1080/15456870.2023.2178000
27.
HaralabopoulosG.SimperlE. (2017). Crowdsourcing for beyond polarity sentiment analysis: A pure emotion lexicon. arXiv preprint arXiv:1710.04203.
28.
HuttoC.GilbertE. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
29.
JacksonS.Stromer-GalleyJ.HemsleyJ. (2020). Differentiated information flows: Social media curation practices in the 2016 US presidential elections. International Journal of Communication, 14(1), 22.
30.
KeckM. E.SikkinkK. (1998). Transnational advocacy networks in the movement society. The social Movement society: Contentious Politics for a New Century, 221(2), 217–237.
31.
KeckM. E.SikkinkK. A. (2014). Activists beyond borders: Advocacy networks in international politics. Cornell university press.
32.
KellerF. B.KlingerU. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171–189. https://doi.org/10.1080/10584609.2018.1526238
33.
KermaniH. (2023). Profile: #MahsaAmini: Iranian Twitter activism in times of computational propaganda. Social Movement Studies, 24(2), 257–267. https://doi.org/10.1080/14742837.2023.2180354
34.
KermaniH. (2025). The art of delirium: Social media suppression in authoritarian regimes. Communication Theory, 35(4), qtaf006.
KhorramrouzA.DuttaS.KhudaBukhshA. R. (2023). For women, life, freedom: A participatory AI-based social web analysis of a watershed moment in Iran's gender struggles. arXiv Preprint arXiv:2307.03764.
37.
KosmajacD.KeseljV. (2019). Twitter bot detection using diversity measures. In Proceedings of the 3rd international conference on natural language and speech processing (pp. 1–8). Association for Computational Linguistics.
38.
KrippendorffK. (2013). Content analysis: An introduction to its methodology. Sage Publications.
39.
MerazS. (2017). Hashtag wars and networked framing: The private/public networked protest repertoires of occupy on Twitter. In Between the public and private in mobile communication (pp. 303–323). Routledge.
40.
MerazS.PapacharissiZ. (2013). Networked gatekeeping and networked framing on #egypt. The International Journal of Press/Politics, 18(2), 138–166. https://doi.org/10.1177/1940161212474472
41.
MerceaD.BastosM. T. (2016). Being a serial transnational activist. Journal of Computer-Mediated Communication, 21(2), 140–155. https://doi.org/10.1111/jcc4.12150
42.
MichaelsenM. (2018). Exit and voice in a digital age: Iran’s exiled activists and the authoritarian state. Globalizations, 15(2), 248–264. https://doi.org/10.1080/14747731.2016.1263078
43.
MichelenM.GlasiusM. (2018). Authoritarian practices in the digital age—Introduction. International Journal of Communication.
44.
MilanS. (2015). From social movements to cloud protesting: The evolution of collective identity. Information, Communication & Society, 18(8), 887–900. https://doi.org/10.1080/1369118x.2015.1043135
45.
MohammadiS.MoradiP.FirouzabadiS. M.JafariG. (2022). The footprint of campaign strategies in farsi Twitter: A case for 2021 Iranian presidential election. PLOS ONE, 17(7), Article e0270822. https://doi.org/10.1371/journal.pone.0270822
46.
O'ConnorB.BalasubramanyanR.RoutledgeB.SmithN. (2010). From tweets to polls: Linking text sentiment to public opinion time series. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 122–129. https://doi.org/10.1609/icwsm.v4i1.14031
47.
OlteanuA.CastilloC.DiazF.KıcımanE. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2(1), 13. https://doi.org/10.3389/fdata.2019.00013
48.
PachecoD.HuiP. M.Torres-LugoC.TruongB. T.FlamminiA.MenczerF. (2021). Uncovering coordinated networks on social media: Methods and case studies. Proceedings of the International AAAI Conference on Web and social Media, 15(1), 455–466. https://doi.org/10.1609/icwsm.v15i1.18075
49.
PapacharissiZ. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.
50.
PapacharissiZ. (2016). Affective publics and structures of storytelling: Sentiment, events and mediality. Information, Communication & society, 19(3), 307–324. https://doi.org/10.1080/1369118x.2015.1109697
51.
PapacharissiZ.de Fatima OliveiraM. (2012). Affective news and networked publics: The rhythms of news storytelling on #egypt. Journal of Communication, 62(2), 266–282. https://doi.org/10.1111/j.1460-2466.2012.01630.x
52.
PavanE.RapiniA. (2022). Battles over issues in networked publics: Investigating the discursive mobilization of the antifascist frame on Twitter. Mobilization, 27(1), 69–90. https://doi.org/10.17813/1086-671x-27-1-69
53.
PiperN.UhlinA. (2004). Transnational activism in Asia. Problems of power and democracy. Routledge.
54.
RauchfleischA.KaiserJ. (2020). The false positive problem of automatic bot detection in social science research. PloS One, 15(10), Article e0241045. https://doi.org/10.1371/journal.pone.0241045
55.
ŘehůřekR.SojkaP. (2010). Software Framework for Topic Modelling with Large Corpora. Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks.
56.
RosenbaumJ. E.BouvierG. (2020). Twitter, social movements and the logic of connective action: Activism in the 21st century–an introduction. Participation: Journal of Audience & Reception Studies, 17(1), 120–125.
StewartM.SchultzeU. (2019). Producing solidarity in social media activism: The case of my stealthy freedom. Information and Organization, 29(3), Article 100251. https://doi.org/10.1016/j.infoandorg.2019.04.003
60.
SunsteinC. R. (2018). Republic: Divided democracy in the age of social media. Princeton University Press.
61.
TabachnickB. G.FidellL. S. (2013). Using multivariate statistics (6. Baskı). MA: Pearson.
62.
TanZ.D’SouzaJ. (2025). Bridging the evaluation gap: Leveraging large language models for topic model evaluation. 21st Conference on. Information and Research science Connecting to Digital and Library science.
63.
TownsendL.WallaceC. (2017). The ethics of using social media data in research: A new framework. The ethics of online research.
64.
TufekciZ. (2017). Twitter and tear gas: The power and fragility of networked protest. Yale University Press.
65.
VarolO.FerraraE.DavisC.MenczerF.FlamminiA. (2017). Online human-bot interactions: Detection, estimation, and characterization. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 280–289. https://doi.org/10.1609/icwsm.v11i1.14871
66.
WulfV.RandallD.AalK.RohdeM. (2022). The personal is the political: Internet filtering and counter appropriation in the Islamic Republic of Iran. Computer Supported Cooperative Work, 31(2), 373–409. https://doi.org/10.1007/s10606-022-09426-7
67.
ZajakS. (2014). Pathways of transnational activism: A conceptual framework (No. 14/5). MPIfG Discussion Paper.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
