Pith. sign in

REVIEW 4 major objections 3 minor 75 references

Extracting Participation in Collective Action from Social Media

T0 review · 4 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that participation in collective action can be detected and graded into four levels from social media text alone, using a small open-source classifier that nearly matches large language models in accuracy at a fraction…

desk verdict New participation-level taxonomy and classifier suite, but the topic-agnostic claim outruns the evaluation. read the letter →

arxiv 2501.07368 v2 pith:U7ZI5ORF submitted 2025-01-13 cs.SI cs.CYphysics.soc-ph

Arianna Pera , Luca Maria Aiello This is my paper · ORCID
classification cs.SIcs.CYphysics.soc-ph
keywords collectiveactionparticipationdetectiontextclassificationRedditBERTlargelanguagemodelssocialmovementmobilizationdataaugmentation
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 claims that expressions of participation in collective action can be reliably detected in social media text by a comparatively small, open-source language model, without being tied to any single topic or cause. The authors define four levels of participation—recognizing a problem and proposing solutions, issuing a call to action, stating an intention to act, and reporting actual involvement—and build a layered classifier that first decides whether a comment expresses participation and then assigns its level. Trained on crowdsourced Reddit comments augmented with synthetic and structurally similar examples, the best BERT-based model reaches a macro F1 of 0.65 for the binary detection task (weighted F1 0.71) and 0.44 for four-level classification, close to the best fine-tuned Llama3 model at 0.52 while being far cheaper to run. If the claim holds, researchers can generate large-scale, granular ground truth on who participates in collective action from text alone, at a fraction of the computational cost of LLM-based approaches.

What carries the argument

The load-bearing object is the four-level participation taxonomy derived from consensus and action mobilization theory: Problem-solution, Call-to-action, Intention, and Execution, with a fifth None class for non-participation. This taxonomy turns a continuous psychological pathway into discrete labels that a classifier can learn, and it is what makes the method topic-agnostic, since the levels describe the speaker's engagement rather than the cause. The argument is carried by a layered pipeline—a binary participation detector followed by a four-way level classifier—trained on crowdsourced Reddit comments that were filtered through a collective-action word list and then augmented with synthetic minority-class examples and with labels propagated from similar comments in the same threads. The layer that does the heavy lifting is the BERT/RoBERTa classifier trained on the synthetically augmented set, which the paper selects for the full pipeline because it nearly matches fine-tuned LLMs in F1 while running two orders of magnitude faster at inference.

What would settle it

Take a stratified sample of comments from subreddits that do not mention activism or rights in their names—for example general news, sports, and hobby communities—annotate them with expert labelers using the paper's codebook, run the released binary classifier on the unfiltered text, and compare its macro F1 to the reported 0.65; a substantial drop would show the topic-agnostic claim depends on the dictionary filter and activist-skewed training distribution.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that participation in collective action is a learnable textual property that can be quantified along a theoretically grounded four-level scale, and that a fine-tuned small transformer captures most of the signal that large models do. The paper operationalizes the mobilization pathway from social movement theory as four mutually ordered classes, builds a human-annotated Reddit dataset around them, and shows that data augmentation—synthetic Llama3-generated examples for rare classes plus propagation of labels to similar in-thread comments—makes training feasible with only 369 original crowd labels. The resulting pipeline separates participation detection from participation-level classification, and its outputs are shown to be orthogonal to topic membership, stance, and keyword presence: participation is found across all stance classes and in subreddits that keyword-based proxies would miss. The authors conclude that small models can rival LLMs on this task, and that the tool can serve as a new source of granular ground truth for studying collective action dynamics online.

Load-bearing premise

The entire training and test signal comes from Reddit comments that were pre-selected through activist subreddit names and a 47-word collective-action dictionary, so the claim that the classifier is topic-agnostic rests on the assumption that this filtered sample represents how participation is expressed across topics, platforms, and ideologies.

Editorial extensions

If this is right

  • Researchers can use the released classifiers to measure participation levels in any large text corpus without hand-labeling a new dataset for each topic.
  • Communities that look inactive by keyword or subreddit-membership proxies may still contain substantial collective-action participation, so prior studies based on those proxies may have underestimated activism in non-activist spaces.
  • The four-level output makes it possible to study mobilization as a gradual process—from recognizing an issue to reporting involvement—rather than as a binary activist/non-activist status.
  • Because the best small model nearly matches the best LLM at a fraction of the compute, large-scale longitudinal studies of collective action become computationally affordable.
  • The positive validation on UK parliamentary debates suggests the classifier transfers beyond Reddit to institutional political discourse, though the paper treats that test as preliminary.

Reading between the lines

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

  • An implication the paper leaves implicit is that the same four-level scale could be applied to other platforms, such as X, Facebook, or TikTok transcripts, with an annotation pass to verify that platform-specific phrasing does not degrade performance.
  • A testable extension would be to use the classifier's per-comment timeline to trace individuals moving from Problem-solution to Intention to Execution, turning the paper's cross-sectional measurement into evidence about mobilization pathways.
  • One caution suggested by the paper's own design: because training comments were pre-filtered by a 47-term collective-action dictionary, the classifier may under-detect participation expressed through novel, indirect, or highly contextual language, a gap that could be probed with unfiltered annotation samples.
  • The demographic correlations the paper reports are aggregate community-level patterns; an editorial caveat is that individual-level inference from subreddit embeddings would need additional validation before being used to make claims about who participates.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper presents a text-classification framework for detecting expressions of participation in collective action in social media comments, grounded in social-movement mobilization theory and operationalized with a four-level taxonomy (Problem-solution, Call-to-action, Intention, Execution). The authors construct a crowdsourced Reddit training set filtered by a 47-term collective-action dictionary, augment it with synthetic and Reddit-derived samples, and evaluate RoBERTa, zero-shot Llama3, fine-tuned Llama3 (SFT and DPO), plus baselines on a held-out test set. They report that a small RoBERTa model reaches weighted F1 0.71 for binary detection and macro F1 0.44 for four-level classification, close to the best fine-tuned LLM's 0.52, and they illustrate the method's usefulness through topic-modeling, stance-detection, parliamentary-debate, climate-action, and sociodemographic analyses.

Significance. If the core claims hold, the released models, annotation codebook, and training data would be a valuable resource for Computational Social Science, enabling large-scale and granular measurement of participation in collective action from text. The paper's strengths include making code, models, and datasets publicly available; grounding the annotation scheme in established mobilization theory; and systematically comparing multiple modeling strategies and augmentation techniques. However, the main evaluation is restricted to a dictionary-filtered sample from activist-named subreddits, which makes the topic-agnostic deployment claim substantially stronger than the evidence directly supports. The external validations are largely qualitative and do not provide gold-labeled F1 scores on unfiltered or out-of-domain data, leaving a measurable gap between the reported results and the stated applicability.

major comments (4)
  1. [Data Curation / Test Set] The evaluation reported in Tables 2 and 3 is conducted on a test set that required at least two matches from the 47-term collective-action dictionary and was sampled from subreddits whose names or descriptions contain 'activism,' 'activist,' or 'rights.' Because the same filter is used to construct the training set, the reported F1 scores are conditional on text that already contains substantial dictionary vocabulary and come from an activist-skewed community distribution. This does not support the abstract's topic-agnostic claim, which is used to justify applying the classifier to unfiltered comments in the Climate Action and sociodemographic analyses. The paper should either add a labeled evaluation on unfiltered comments from non-activist subreddits or substantially soften the generalization claim.
  2. [Results, Multi-class Task / Tables 3 and B3] The multi-class comparison rests on extremely small class sizes: the test set contains only 11 Intention and 13 Execution labels. With so few instances, the F1 differences between methods for these classes (e.g., BERT CS+SynA Intention F1=0.17 vs. SFT CS+SynA Intention F1=0.29) are within the range of chance variation, and no confidence intervals are reported. Consequently, the claim that 'the SFT and DPO models consistently outperformed others, particularly in classifying Intention and Execution' is not statistically supported; the macro-F1 differences between the best BERT (0.44) and best LLM (0.52) may also be driven largely by these unstable minority classes.
  3. [Validation] The external validation experiments do not provide quantitative gold-standard evaluation on out-of-domain data. The topic-modeling comparison uses unlabeled threads from r/intj and r/wallstreetbets and reports qualitative distribution comparisons; the stance-detection experiment reports participation distributions across stance classes without gold participation labels; and the parliamentary-debate analysis provides only high/low probability examples rather than labeled F1 scores. These analyses can illustrate potential utility, but they cannot establish the classifier's transferability to unfiltered text or non-Reddit domains, which is required for the claimed broader applicability.
  4. [Impact of Collective Action Terms] The adversarial test that removes or replaces dictionary words operates on the already dictionary-filtered test set. Since every test comment originally contained at least two collective-action terms, this experiment cannot measure recall on comments that express participation without ever using such vocabulary. The risk that the classifier's positive predictions are dependent on dictionary words therefore remains unquantified for the unfiltered target distribution, and the robustness conclusion drawn from Figure 1(f) does not close that gap.
minor comments (3)
  1. [Abstract / Table 2] The abstract's 'weighted F1=0.71' is not tied to a specific model configuration; Table 2 shows that zero-shot Llama3 achieves weighted F1=0.76–0.77, so the headline figure should explicitly identify the BERT model trained on CS+SynA.
  2. [Figure 2 / Climate Action] The x-axis in Figure 2 is labeled 'normalized,' but neither the caption nor the surrounding text defines the normalization procedure used for the percentage values.
  3. [Paper Checklist / Introduction] The Paper Checklist states that an 'anonymized shared repository' is linked, but the Introduction provides a non-anonymized GitHub URL; this inconsistency should be resolved if the manuscript is intended for anonymous review.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central classifier evaluation rests on an independently expert-annotated test set, and the theoretical framework is grounded in external social-movement literature rather than in the authors' own prior results.

full rationale

The paper's derivation chain runs from a literature-grounded framework (Klandermans and Oegema 1987; Benford and Snow 2000; Wright 2009) to crowdsourced labels, supervised training, and a held-out test set of 809 comments annotated by domain experts. The main F1 numbers in Tables 2 and 3 are computed on that test set, which was not used to fit the dictionary threshold, the classifier weights, or the augmentation pipeline; therefore the reported predictions are not forced by construction. The 47-term dictionary (Smith et al. 2018) is used as a sampling filter to select candidate comments and highlight sentences for both training and test, but the labels are assigned by human annotators and the 'Impact of Collective Action Terms' experiment shows that removing or replacing dictionary words does not shift the predicted participation distribution, so the dictionary does not define the model's output. Self-citations to Pera and Aiello (2024) and Møller et al. (2024) appear in related-work and methodology contexts, but they are not load-bearing: the comparative performance claims are established by the paper's own experiments, and the augmentation techniques are independently evaluated on the annotated test set. The main limitations (activist-named subreddit selection, dictionary-filtered inputs, single-platform training) are external-validity concerns about generalization rather than circularity, and the paper itself acknowledges them in the Limitations section. No step reduces to its own input by definition.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central claim rests on the construct validity of the four-level taxonomy and on the representativeness of a dictionary-filtered, activist-subreddit sample. The free parameters are mostly data-curation and augmentation choices; the main fitted parameter is the dictionary baseline threshold, which is not the contribution. No new physical entities are introduced; the four-level schema and the revised collective action definition are theoretical constructs with no external benchmark.

free parameters (4)
  • Dictionary match threshold = minimum 2 term matches
    Comments were retained only if they contained at least two matches from the 47-term collective action dictionary; this shapes both training and test sets and directly affects the measured F1.
  • Dict baseline threshold = threshold maximizing TPR-FPR on training set
    The dictionary baseline selects the threshold that maximizes the difference between true positive rate and false positive rate on the training set; this is a parameter fitted to the training data.
  • Synthetic augmentation size = 20 per class
    20 synthetic examples were generated per anchor comment for augmented training sets (Syn I/E and Syn A); the choice affects class balance and model performance.
  • Reddit extension similarity top 5% = top 5% most similar, exclude similarity greater than 0.95
    For each labeled comment, the top 5% most similar comments in the original thread were assigned the same label, assuming similar contexts imply the same participation level; the 0.95 cutoff removes near-duplicates or bots.
assumptions (4)
  • domain assumption Definition of collective action problem (human-generated, current, opportunity for action, shared responsibilities)
    The classification construct rests on this literature-informed definition; if one does not accept this definition, the labels measure a different construct. Invoked in the Theoretical Framework.
  • domain assumption Four-level participation taxonomy (Problem-solution, Call-to-action, Intention, Execution)
    The mapping from consensus and action mobilization theory to four discrete levels is an interpretive choice; alternative mappings would change labels. Invoked in the Theoretical Framework.
  • domain assumption Label propagation via sentence similarity (similar comments share labels)
    The Reddit-informed extension assumes top-5% similar comments in the same thread carry the same participation label; this can propagate initial annotation errors. Invoked in Data Augmentation.
  • domain assumption Synthetic Llama3-generated examples are valid instances of the target classes
    Minority-class training data is largely synthetic; manual review reduces but does not eliminate the risk that generated examples differ from naturalistic expression. Invoked in Data Augmentation.
invented entities (2)
  • Four-level participation classification schema
    purpose: To categorize expressions of participation in collective action in text
    The schema is introduced by the authors as an operationalization of mobilization theory; it is validated only through annotation agreement and classifier performance within the paper, with no external benchmark.
  • Alternative definition of collective action as efforts to mitigate a collective action problem
    purpose: To define the target construct for classification
    The paper proposes this definition to address limitations of Wright et al. (1990); it is a theoretical construct, not independently evidenced.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Extracting Participation in Collective Action from Social Media." pith.science (2026). https://pith.science/paper/U7ZI5ORF

@misc{pith2026250107368,
  author       = {Pith},
  title        = {Pith review of: Extracting Participation in Collective Action from Social Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U7ZI5ORF}},
  note         = {Machine review of arXiv:2501.07368}
}
read the original abstract

Social media play a key role in mobilizing collective action, holding the potential for studying the pathways that lead individuals to actively engage in addressing global challenges. However, quantitative research in this area has been limited by the absence of granular and large-scale ground truth about the level of participation in collective action among individual social media users. To address this limitation, we present a novel suite of text classifiers designed to identify expressions of participation in collective action from social media posts, in a topic-agnostic fashion. Grounded in the theoretical framework of social movement mobilization, our classification captures participation and categorizes it into four levels: recognizing collective issues, engaging in calls-to-action, expressing intention of action, and reporting active involvement. We constructed a labeled training dataset of Reddit comments through crowdsourcing, which we used to train BERT classifiers and fine-tune Llama3 models. Our findings show that smaller language models can reliably detect expressions of participation (weighted F1=0.71), and rival larger models in capturing nuanced levels of participation. By applying our methodology to Reddit, we illustrate its effectiveness as a robust tool for characterizing online communities in innovative ways compared to topic modeling, stance detection, and keyword-based methods. Our framework contributes to Computational Social Science research by providing a new source of reliable annotations useful for investigating the social dynamics of collective action.

Figures

Figures reproduced from arXiv: 2501.07368 by the authors.

Figure 1
Figure 1. Validation of the proposed approach. (a-d) comparison with topic modeling; (e) comparison with stance detection – [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Comparison of climate change-focused comment [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Fraction of Reddit comments showing partici [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

75 extracted references · 54 canonical work pages

  1. [1]

    Aceves, P.; and Evans, J. A. 2024. Mobilizing conceptual spaces: How word embedding models can inform measurement and theory within organization science. Organization Science, 35(3): 788--814

    work page 2024

  2. [2]

    Barthel, M.; Stocking, G.; Holcomb, J.; and Mitchell, A. 2016. Reddit news users more likely to be male, young and digital in their news preferences. Pew Research Center, 25

    work page 2016

  3. [3]

    D.; and Snow, D

    Benford, R. D.; and Snow, D. A. 2000. Framing processes and social movements: An overview and assessment. Annual review of sociology, 26(1): 611--639

    work page 2000

  4. [4]

    Boulianne, S.; and Theocharis, Y. 2020. Young people, digital media, and engagement: A meta-analysis of research. Social science computer review, 38(2): 111--127

    work page 2020

  5. [5]

    Brown, O.; Lowery, C.; and Smith, L. G. 2022. How opposing ideological groups use online interactions to justify and mobilise collective action. European Journal of Social Psychology, 52(7): 1082--1110

    work page 2022

  6. [6]

    Bucher, M. J. J.; and Martini, M. 2024. Fine-Tuned'Small'LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification. arXiv preprint arXiv:2406.08660

    arXiv 2024

  7. [7]

    Cerqueira, V.; Torgo, L.; Branco, P.; and Bellinger, C. 2023. Automated imbalanced classification via layered learning. Machine Learning, 112(6): 2083--2104

    work page 2023

  8. [8]

    M.; Varga, K

    Choi, M.; Aiello, L. M.; Varga, K. Z.; and Quercia, D. 2020. Ten social dimensions of conversations and relationships. In Proceedings of The Web Conference 2020, 1514--1525

    work page 2020

Show all 75 references
  1. [9]

    Choi, M.; Pei, J.; Kumar, S.; Shu, C.; and Jurgens, D. 2023. Do llms understand social knowledge? evaluating the sociability of large language models with socket benchmark. arXiv preprint arXiv:2305.14938

    2023 arXiv

  2. [10]

    Coff \'e , H.; and Bolzendahl, C. 2010. Same game, different rules? Gender differences in political participation. Sex roles, 62: 318--333

    2010

  3. [11]

    Dettmers, T.; Pagnoni, A.; Holtzman, A.; and Zettlemoyer, L. 2024. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, 36

    2024

  4. [12]

    Dubey, A.; Jauhri, A.; Pandey, A.; Kadian, A.; Al-Dahle, A.; Letman, A.; Mathur, A.; Schelten, A.; Yang, A.; Fan, A.; et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783

    2024 arXiv

  5. [13]

    V.; and Elliott, T

    Earl, J.; Maher, T. V.; and Elliott, T. 2017. Youth, activism, and social movements. Sociology Compass, 11(4): e12465

    2017

  6. [14]

    Ebrahimi, J.; Rao, A.; Lowd, D.; and Dou, D. 2017. Hotflip: White-box adversarial examples for text classification. arXiv preprint arXiv:1712.06751

    2017 arXiv

  7. [15]

    Erseghe, T.; Badia, L.; D z anko, L.; Formanowicz, M.; Nikadon, J.; and Suitner, C. 2023. Projection of Socio-Linguistic markers in a semantic context and its application to online social networks. Online Social Networks and Media, 37: 100271

    2023

  8. [16]

    FORCE11. 2020. The FAIR Data principles. https://force11.org/info/the-fair-data-principles/. Accessed: 2024-01-05

    2020

  9. [17]

    Furlong, C.; and Vignoles, V. L. 2021. Social identification in collective climate activism: Predicting participation in the environmental movement, extinction rebellion. Identity, 21(1): 20--35

    2021

  10. [18]

    Gamson, W. A. 1975. The Strategy of Social Protest

    1975

  11. [19]

    W.; Wallach, H.; Iii, H

    Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; Iii, H. D.; and Crawford, K. 2021. Datasheets for datasets. Communications of the ACM, 64(12): 86--92

    2021

  12. [20]

    Gerbaudo, P. 2012. Tweets and the streets: Social media and contemporary activism. Pluto Press

    2012

  13. [21]

    Ghaddar, A.; Langlais, P.; Rashid, A.; and Rezagholizadeh, M. 2021. Context-aware adversarial training for name regularity bias in named entity recognition. Transactions of the Association for Computational Linguistics, 9: 586--604

    2021

  14. [22]

    Grootendorst, M. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794

    2022 arXiv

  15. [23]

    S.; and Louis, W

    Gulliver, R.; Fielding, K. S.; and Louis, W. R. 2021. Assessing the mobilization potential of environmental advocacy communication. Journal of Environmental Psychology, 74: 101563

    2021

  16. [24]

    Hofmann, V.; Sch \"u tze, H.; and Pierrehumbert, J. B. 2022. The reddit politosphere: a large-scale text and network resource of online political discourse. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, 1259--1267

    2022

  17. [25]

    L.; Moss, A

    Hoyt, C. L.; Moss, A. J.; Burnette, J. L.; Schieffelin, A.; and Goethals, A. 2018. Wealth inequality and activism: Perceiving injustice galvanizes social change but perceptions depend on political ideologies. European Journal of Social Psychology, 48(1): O81--O90

    2018

  18. [26]

    Huang, T.; Yi, J.; Yu, P.; and Xu, X. 2025. Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies. arXiv preprint arXiv:2503.00724

    2025 arXiv

  19. [27]

    Israeli, A.; Kremiansky, A.; and Tsur, O. 2022. This must be the place: Predicting engagement of online communities in a large-scale distributed campaign. In Proceedings of the ACM Web Conference 2022, 1673--1684

    2022

  20. [28]

    Kim, S.; Cha, J.; Kim, D.; and Park, E. 2023. Understanding mental health issues in different subdomains of social networking services: computational analysis of text-based Reddit posts. Journal of Medical Internet Research, 25: e49074

    2023

  21. [29]

    Klandermans, B. 1984. Mobilization and participation: Social-psychological expansisons of resource mobilization theory. American sociological review, 583--600

    1984

  22. [30]

    Klandermans, B. 1988. The formation and mobilization of consensus. International social movement research, 1(1): 173--196

    1988

  23. [31]

    Klandermans, B.; and Oegema, D. 1987. Potentials, networks, motivations, and barriers: Steps towards participation in social movements. American sociological review, 519--531

    1987

  24. [32]

    Klandermans, B.; and Stekelenburg, J. V. 2014. Why people don't participate in collective action. Journal of Civil Society, 10(4): 341--352

    2014

  25. [33]

    L.; Leskovec, J.; and Jurafsky, D

    Kumar, S.; Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2018. Community interaction and conflict on the web. In Proceedings of the 2018 world wide web conference, 933--943

    2018

  26. [34]

    Kumarage, T.; Bhattacharjee, A.; and Garland, J. 2024. Harnessing artificial intelligence to combat online hate: Exploring the challenges and opportunities of large language models in hate speech detection. arXiv preprint arXiv:2403.08035

    2024 arXiv

  27. [35]

    Lan, X.; Gao, C.; Jin, D.; and Li, Y. 2024. Stance detection with collaborative role-infused llm-based agents. In Proceedings of the international AAAI conference on web and social media, volume 18, 891--903

    2024

  28. [36]

    M.; Monti, C.; and Morales, G

    Lenti, J.; Aiello, L. M.; Monti, C.; and Morales, G. D. F. 2025. Causal Modeling of Climate Activism on Reddit. In Proceedings of the ACM on Web Conference 2025, 590--600

    2025

  29. [37]

    Lenti, J.; Monti, C.; and De Francisci Morales, G. 2024. Likelihood-based methods improve parameter estimation in opinion dynamics models. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 350--359

    2024

  30. [38]

    L.; Choi, Y.; and Hajishirzi, H

    Liu, J.; Liu, A.; Lu, X.; Welleck, S.; West, P.; Bras, R. L.; Choi, Y.; and Hajishirzi, H. 2021. Generated knowledge prompting for commonsense reasoning. arXiv preprint arXiv:2110.08387

    2021 arXiv

  31. [39]

    Liu, Y. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 364

    2019 arXiv

  32. [40]

    Liu, Y.; Yao, Y.; Ton, J.-F.; Zhang, X.; Cheng, R. G. H.; Klochkov, Y.; Taufiq, M. F.; and Li, H. 2023. Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment. arXiv preprint arXiv:2308.05374

    2023 arXiv

  33. [41]

    M.; Alessandretti, L.; De Francisci Morales, G.; Starnini, M.; and Baronchelli, A

    Lucchini, L.; Aiello, L. M.; Alessandretti, L.; De Francisci Morales, G.; Starnini, M.; and Baronchelli, A. 2022. From Reddit to Wall Street: The role of committed minorities in financial collective action. Royal Society Open Science, 9(4): 211488

    2022

  34. [42]

    Luo, Y.; Card, D.; and Jurafsky, D. 2020. Detecting stance in media on global warming. arXiv preprint arXiv:2010.15149

    2020 arXiv

  35. [43]

    G.; and Aiello, L

    M ller, A. G.; and Aiello, L. M. 2024. Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks. arXiv preprint arXiv:2408.01346

    2024 arXiv

  36. [44]

    MySociety. 2024. UK Parlament Debates. https://www.theyworkforyou.com/pwdata/scrapedxml/. Accessed: 2024-12-15

    2024

  37. [45]

    G.; Pera, A.; Dalsgaard, J

    Møller, A. G.; Pera, A.; Dalsgaard, J. A.; and Aiello, L. M. 2024. The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks. In Proceedings of the European of the Association of Computational Linguistics, EACL'24. ACL

    2024

  38. [46]

    Ostrom, E. 2010. Analyzing collective action. Agricultural economics, 41: 155--166

    2010

  39. [47]

    Oswald, L.; and Bright, J. 2022. How do climate change skeptics engage with opposing views online? Evidence from a major climate change skeptic forum on Reddit. Environmental Communication, 16(6): 805--821

    2022

  40. [48]

    M.; Donges, J

    Otto, I. M.; Donges, J. F.; Cremades, R.; Bhowmik, A.; Hewitt, R. J.; Lucht, W.; Rockstr \"o m, J.; Allerberger, F.; McCaffrey, M.; Doe, S. S.; et al. 2020. Social tipping dynamics for stabilizing Earth’s climate by 2050. Proceedings of the National Academy of Sciences, 117(5)...

    2020

  41. [49]

    S.; and Kim, W

    Park, K.; Hong, J. S.; and Kim, W. 2020. A methodology combining cosine similarity with classifier for text classification. Applied Artificial Intelligence, 34(5): 396--411

    2020

  42. [50]

    S.; Shi, H.; Xu, Y.; Yim, A.; Yin, Y.; and Golab, L

    Parsa, M. S.; Shi, H.; Xu, Y.; Yim, A.; Yin, Y.; and Golab, L. 2022. Analyzing climate change discussions on reddit. In 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 826--832. IEEE

    2022

  43. [51]

    Pera, A.; and Aiello, L. M. 2024. Narratives of Collective Action in YouTube’s Discourse on Veganism. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, 1220--1236

    2024

  44. [52]

    Pera, A.; Morales, G. d. F.; and Aiello, L. M. 2023. Measuring Behavior Change with Observational Studies: a Review. arXiv preprint arXiv:2310.19951

    2023

  45. [53]

    D.; Ermon, S.; and Finn, C

    Rafailov, R.; Sharma, A.; Mitchell, E.; Manning, C. D.; Ermon, S.; and Finn, C. 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36

    2024

  46. [54]

    Rajadesingan, A.; Budak, C.; and Resnick, P. 2021. Political discussion is abundant in non-political subreddits (and less toxic). In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, 525--536

    2021

  47. [55]

    Reimers, N. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084

    2019 arXiv

  48. [56]

    Rogers, A.; Kovaleva, O.; and Rumshisky, A. 2019. Calls to action on social media: Detection, social impact, and censorship potential. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, 36--44

    2019

  49. [57]

    G.; McGarty, C.; and Thomas, E

    Smith, L. G.; McGarty, C.; and Thomas, E. F. 2018. After Aylan Kurdi: How tweeting about death, threat, and harm predict increased expressions of solidarity with refugees over time. Psychological science, 29(4): 623--634

    2018

  50. [58]

    G.; Piwek, L.; Hinds, J.; Brown, O.; Chen, C.; and Joinson, A

    Smith, L. G.; Piwek, L.; Hinds, J.; Brown, O.; Chen, C.; and Joinson, A. 2023. Digital traces of offline mobilization. Journal of Personality and Social Psychology, 125(3): 496

    2023

  51. [59]

    A.; Rochford Jr, E

    Snow, D. A.; Rochford Jr, E. B.; Worden, S. K.; and Benford, R. D. 1986. Frame alignment processes, micromobilization, and movement participation. American sociological review, 464--481

    1986

  52. [60]

    F.; Duncan, L.; McGarty, C.; Louis, W

    Thomas, E. F.; Duncan, L.; McGarty, C.; Louis, W. R.; and Smith, L. G. 2022. MOBILISE: A higher-order integration of collective action research to address global challenges. Political Psychology, 43: 107--164

    2022

  53. [61]

    Treen, K.; Williams, H.; O’Neill, S.; and Coan, T. G. 2022. Discussion of climate change on Reddit: Polarized discourse or deliberative debate? Environmental Communication, 16(5): 680--698

    2022

  54. [62]

    Van Stekelenburg, J.; and Klandermans, B. 2013. The social psychology of protest. Current Sociology, 61(5-6): 886--905

    2013

  55. [63]

    Van Vugt, M. 2009. Averting the tragedy of the commons: Using social psychological science to protect the environment. Current Directions in Psychological Science, 18(3): 169--173

    2009

  56. [64]

    Van Zomeren, M. 2013. Four core social-psychological motivations to undertake collective action. Social and Personality Psychology Compass, 7(6): 378--388

    2013

  57. [65]

    Van Zomeren, M.; and Iyer, A. 2009. Introduction to the social and psychological dynamics of collective action

    2009

  58. [66]

    Van Zomeren, M.; Postmes, T.; and Spears, R. 2008. Toward an integrative social identity model of collective action: a quantitative research synthesis of three socio-psychological perspectives. Psychological bulletin, 134(4): 504

    2008

  59. [67]

    Waller, I.; and Anderson, A. 2019. Generalists and specialists: Using community embeddings to quantify activity diversity in online platforms. In The World Wide Web Conference, 1954--1964

    2019

  60. [68]

    Waller, I.; and Anderson, A. 2021. Quantifying social organization and political polarization in online platforms. Nature, 600(7888): 264--268

    2021

  61. [69]

    Wright, S. C. 2009. The next generation of collective action research. Journal of social Issues, 65(4): 859--879

    2009

  62. [70]

    C.; Taylor, D

    Wright, S. C.; Taylor, D. M.; and Moghaddam, F. M. 1990. Responding to membership in a disadvantaged group: From acceptance to collective protest. Journal of personality and social psychology, 58(6): 994

    1990

  63. [71]

    Yasseri, T.; Margetts, H.; John, P.; and Hale, S. 2016. Political turbulence: How social media shape collective action. Princeton University Press

    2016

  64. [72]

    Ziems, C.; Held, W.; Shaikh, O.; Chen, J.; Zhang, Z.; and Yang, D. 2024. Can large language models transform computational social science? Computational Linguistics, 50(1): 237--291

    2024

  65. [73]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...

  66. [74]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  67. [75]

    11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorbl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.