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REVIEW 4 major objections 5 minor 40 references

Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach

T0 review · 4 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A calibrated LLM judge can flag 98% of Reddit firestorms after just 8.56 comments, before the storm fully breaks.

desk verdict Useful LLM-as-judge application with an honest evaluation setup, but the class confound in the dataset means the reported detection performance is not yet evidence of firestorm detection. read the letter →

arxiv 2607.14957 v1 pith:3FGKKAEY submitted 2026-07-16 cs.AI

Besim Shala , Peter Mandl , Andreas Humpe , Martin Häusl This is my paper
classification cs.AI
keywords onlinefirestormsLLM-as-a-judgeearlywarningsequentialdetectionRedditnegativityshareescalationsocialmediamonitoring
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 online firestorms—sudden waves of hostile, negative comments—can be detected early by using a large language model as a repeated, context-aware evaluator of a thread as it grows, rather than by counting keywords or volumes. The authors build a two-mode system: a global mode that classifies a complete Reddit thread as firestorm or not, and an early-warning mode that scans a thread in sliding windows and issues an alert when three calibrated indicators—negativity share, escalation level, and contributor count—cross learned thresholds. On a balanced set of 200 Reddit threads, the global mode reaches 91.5% accuracy, and the early-warning mode detects 59 of 60 firestorms after an average of 8.56 comments and 4.02 distinct users. If correct, this gives organizations a deterministic, audit-friendly way to monitor social media discourse in real time, and it extends the 'LLM-as-judge' idea from one-shot evaluation to continuous monitoring.

What carries the argument

The load-bearing mechanism is the sequential sliding-window estimator driven by an LLM-as-a-judge prompt. The LLM is asked to output three numeric indicators—neg_share, esc_level, num_contrib—plus a categorical status, under a fixed system prompt and structured JSON output. These estimates are fed into a deterministic decision rule with thresholds calibrated via grid search on a separate 80-thread subsample. The runtime pipeline advances a window by five comments at a time and issues a warning the first time all conditions are met jointly. This design converts a subjective qualitative judgment into a repeatable, tunable early-warning signal.

What would settle it

A topic-matched validation set: collect firestorm and non-firestorm threads from the same set of subreddits (e.g., r/worldnews or r/politics) with comparable comment lengths and participant counts, then re-run the early-warning pipeline. If recall on firestorm threads drops substantially or false positives rise, it would demonstrate that the current performance relies on confounded dataset structure rather than genuine contextual escalation signals. Alternatively, an independent human annotation of the three indicators on a sample of windows, compared directly to the LLM's estimates, would sho

Watch

Extended reading notes

Core claim

The central claim is that a large language model, prompted to estimate three theoretically grounded firestorm indicators from a growing discussion window, can feed a simple threshold rule that yields high early-detection recall. In the early-warning mode, the LLM estimates the share of negative comments, the degree of escalation or piling-on, and the number of distinct contributing users for each sliding window. A grid search over 125 threshold combinations on a calibration set selects a rule that maximizes recall penalized by false positives and detection lag. The resulting rule (negativity share ≥ 0.4, escalation level ≥ 0.2, contributor count ≥ 3) triggers a warning for 98% of firestorm t

Load-bearing premise

The weakest premise is that the firestorm and non-firestorm classes are comparable except for their escalation status; in reality, they differ systematically in subreddit topic, comment length, and participation volume, so the model might be detecting these surface differences rather than firestorm dynamics.

Editorial extensions

If this is right

  • If the early-warning mode generalizes beyond this dataset, organizations could monitor Reddit and similar platforms in real time, flagging threads for human review before negative sentiments snowball.
  • The global mode offers a retrospective screening tool for archiving past firestorms, enabling post-hoc analysis of how escalation patterns unfold across different communities.
  • Because thresholds are explicitly calibrated, the same architecture can be re-tuned for different risk tolerances, platforms, or community norms without redesigning the pipeline.
  • The approach shows that LLM-based contextual assessment can serve as a practical alternative to dictionary-based sentiment analysis for detecting sarcasm, irony, and tone shifts.
  • The sequential monitoring paradigm could be transferred to other collective-behavior detection tasks, such as coordinated harassment, misinformation surges, or market-sentiment swings.

Reading between the lines

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

  • A key risk is that the model may be learning shallow cues from the dataset's construction: firestorm threads come from a wide range of politically charged and consumer-focused subreddits, while non-firestorm threads come from a few neutral, fact-oriented communities (r/askscience, r/changemyview). The model could be exploiting differences in topic, comment length, or participation volume rather th
  • The paper does not validate the individual LLM-estimated indicators against human-annotated comment-level ground truth. A direct comparison—say, asking human coders to rate negativity share, escalation, and contributor count on a set of windows—would tell whether the model's numbers are meaningful or just internally consistent enough for the threshold rule.
  • The early-warning rule requires at least three distinct contributing users and a moderate negativity share, which seems tailored to the 'piling-on' definition. On platforms where escalation is driven by a small number of highly influential accounts, such as Twitter/X, the contributor-count threshold may be less appropriate; the rule might need a different structural filter.
  • Because the system relies on GPT-4o mini and has not been tested across model versions or prompt variations, the exact thresholds may not transfer. The paper itself notes this, and the calibration procedure would need to be re-run for each new model, which is a practical burden for deployment.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes an LLM-based firestorm detection system with two modes: a global mode that classifies complete Reddit threads by hierarchically merging chunk-level LLM judgments, and an early warning mode that processes threads sequentially in sliding windows, using the LLM to estimate negativity share, escalation level, and contributor count, and triggering a warning when calibrated thresholds are met. The system is evaluated on 200 Reddit threads (100 firestorm, 100 non-firestorm). The global mode reports 0.915 accuracy; the early warning mode reports 0.98 recall on firestorm threads with a mean detection point of 8.56 comments and 4.02 contributing users. Thresholds are calibrated on 80 threads and evaluated on the remaining 120, a clean separation. The paper's limitations honestly acknowledge platform dependence, single-annotator labeling, absence of onset annotation, model dependence, and unvalidated LLM indicator estimates.

Significance. If the central claim is established, the paper makes a useful contribution by extending LLM-as-a-Judge from static evaluation to sequential monitoring with a deterministic, calibrated decision rule. The calibration/test split is methodologically sound, and the explicit documentation of thresholds and the scoring function supports reproducibility. However, because the two classes are drawn from largely disjoint subreddit populations and differ strongly in volume and comment length, the reported results may reflect community/style classification rather than detection of firestorm dynamics. The additional absence of same-data baselines and of an annotated escalation onset means that the key claims of 'detection' and 'early' are not yet compellingly supported. The contribution is potentially valuable but currently under-validated.

major comments (4)
  1. [§III-A, Tables I–II] The two classes are not comparable: 85% of non-firestorm threads come from science-oriented subreddits, while firestorm threads come from politics, consumer, entertainment, news, and gaming communities. Table II shows firestorm threads have nearly twice as many comments and contributors and roughly half the average comment length. The reported global accuracy (0.915) and early-warning recall (0.98) may therefore reflect subreddit- or style-level differences rather than firestorm-specific escalation. The limitations section does not address this class confound. Please provide evidence that the detector discriminates firestorm dynamics within comparable subreddits, e.g., matched non-firestorm threads from the same subreddits, or at least a per-subreddit breakdown showing the classifier does not simply separate science from politics/entertainment threads.
  2. [§VI.C] The paper states that the LLM indicator estimates (neg_share, esc_level, num_contrib) have not been validated against comment-level ground truth, and that escalation onset annotation was deliberately omitted. The headline timing result, 'detection after 8.56 comments', is therefore measured from thread start, not from the actual escalation onset. This does not establish early warning relative to the escalation event. Please add an annotated onset for at least a subset of threads, or a defensible proxy (e.g., the first comment meeting a human-defined escalation criterion), and report detection latency relative to that point. Without this, the timeliness claim is not well supported.
  3. [§V, Tables IV–VII] The comparison to the 70–75% accuracy range from [8] is cross-dataset and not informative for validating the proposed architecture. To demonstrate the value of the LLM-based sequential approach, please include same-data baselines: a volume-threshold detector, a lexicon/sentiment classifier, a feature-based classifier, and/or a simple LLM prompt using only aggregate negativity share. This would clarify whether the reported performance comes from the contextual indicators and calibration or from the underlying subreddit/class structure.
  4. [§III-D, Eq. (2)] The scoring function uses a penalty weight λ, but its value is never specified. Moreover, the grid search is performed on only 80 calibration threads, and no confidence intervals or stability analysis are reported for the selected thresholds or for the held-out recall/FPR/timeliness metrics. Please report λ, and provide bootstrap confidence intervals or repeated calibration/evaluation across splits to establish that the thresholds and headline numbers are not artifacts of a particular calibration sample.
minor comments (5)
  1. [Appendix] The prompts are said to be reported in the Appendix, but the appendix is not included in the manuscript. Please include the full prompts to make the system reproducible.
  2. [§III-D] The description of numerical parameters as 'selected through iterative empirical testing' is vague. Please state which parameters were tuned, on what criterion, and how the final values were fixed.
  3. [Table VII] The caption 'Mean comment ratio (det./thread avg.)' should clarify that the denominator is the mean firestorm thread length in the test set; currently it is easy to misread as a per-thread ratio.
  4. [§VI.C] The limitation about single-annotator bias is acknowledged, but the imbalance between classes in subreddit selection is a related threat to construct validity that should be explicitly listed there as well.
  5. [§I] The phrase 'deterministic and calibrated decision rule' is strong given the admitted lack of validation for the individual indicator estimates; consider softening the wording in the abstract and introduction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; thresholds are calibrated on a separate split and the evaluation is an honest held-out test, with the temporal metric transparently acknowledged as operational.

full rationale

The paper's central derivation is not circular. The early warning thresholds are derived by grid search on a calibration subsample of 80 threads and then applied unchanged to 120 held-out threads, so the reported recall (0.98), FPR (0.217), and mean detection time (8.56 comments) are genuine test-set quantities rather than fittings renamed as predictions. The global mode uses the full 200 threads but involves no calibration, so its 0.915 accuracy is also an independent evaluation. There are no load-bearing self-citations: the reference list contains no works by the present authors, and no uniqueness theorem or prior-work ansatz is invoked to force the design. The paper explicitly discloses the absence of escalation onset annotation, stating that the temporal metrics 'provide a pragmatic operationalization of detection timeliness but are approximations rather than precise measurements relative to the true escalation moment.' This transparency removes any hidden reduction of 'early detection' to the trigger rule itself. The class-distribution confound (different subreddits, volume, and comment length between firestorm and non-firestorm threads) is a genuine external-validity and correctness concern, but it is not a circularity: the model is not defined in terms of the outcome, and the held-out evaluation still tests a fixed decision rule against thread-level labels.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central empirical result is a supervised system: thresholds and several design choices (step sizes, horizon, chunk size, lambda) are fit on a small calibration set. The main assumptions are that the LLM's three numeric estimates are truthful and that the sampled calm threads are valid controls; neither is independently evidenced. No new theoretical entities are introduced.

free parameters (6)
  • min_neg (negativity-share threshold) = 0.4
    Selected by grid search over {0.30,...,0.70} on the 80-thread calibration set (Section III-D1); applied in Algorithm 1.
  • min_esc (escalation-level threshold) = 0.2
    Selected by grid search over {0.20,...,0.60} on the calibration set; a low value makes the rule recall-heavy.
  • min_contrib (contributor-count threshold) = 3
    Grid-searched over {3,5,8,12,15}; operationalizes the multi-actor criterion from firestorm theory.
  • lambda (recall-vs-timeliness penalty weight) = not reported ('mild')
    In the calibration objective score=2*Rec_FS - FPR - lambda*mean_comments_to_detection, the weight was chosen by hand and not swept or justified.
  • step sizes and horizon = calibration step 10, runtime step 5, max horizon 100
    Chosen empirically; they control how often the LLM is queried and when a warning can first fire.
  • chunk size = 12,000 characters
    Chosen to fit LLM context limits; affects how much local discourse context each global-mode chunk preserves.
assumptions (5)
  • domain assumption The three indicators (negativity share, escalation level, contributor count) are valid early signals of firestorms.
    Section II.C/III.D grounds them in firestorm literature, but the paper provides no independent validation of their predictive sufficiency.
  • domain assumption LLM estimates of the three indicators are accurate enough for thresholding.
    Section IV/V assume GPT-4o mini's structured JSON outputs reflect true discourse properties; Section VI.C admits no comment-level ground-truth validation.
  • domain assumption Single-annotator thread labels following [1],[2] are correct.
    Section III.A assigns labels from one annotator; Section VI.C concedes systematic labeling biases cannot be ruled out.
  • domain assumption Purposive sampling across subreddits yields comparable firestorm and non-firestorm corpora.
    Section III.A says the classes were drawn from different subreddit distributions; Tables I-II show strong class-subreddit and class-length confounding.
  • domain assumption Grid-search thresholds on the calibration split transfer to held-out threads.
    Standard supervised calibration, but with only 80 calibration threads, threshold stability is uncertain and no bootstrap/CI is reported.

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Pith. "Pith review of Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach." pith.science (2026). https://pith.science/paper/3FGKKAEY

@misc{pith2026260714957,
  author       = {Pith},
  title        = {Pith review of: Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3FGKKAEY}},
  note         = {Machine review of arXiv:2607.14957}
}
read the original abstract

Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage. Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features. Such signals are useful, but they capture contextual meaning shifts in evolving discussion threads only indirectly. This paper proposes an LLM-based detection system with two operating modes. The first mode classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment. The second mode processes threads sequentially and issues early warnings when a sliding window exceeds calibrated thresholds. In this mode, the language model estimates three firestorm indicators: negativity share, escalation level, and contributor count. On a balanced Reddit dataset, the global mode achieves strong classification performance, while the early warning mode reaches high recall and detects escalating threads after only a small number of comments and distinct contributors. The results indicate that LLMs can be used not only for static judgment tasks, but also as repeated estimators in context-aware monitoring of social media discourse.

Figures

Figures reproduced from arXiv: 2607.14957 by the authors.

Figure 1
Figure 1. Global Recognition Mode pipeline with local LLM-based chunk assessment (Stage 1) and global thread-level classification (Stage 2). 12,000 characters each. This segmentation proceeds comment by comment in chronological order, preserving local discourse coherence within each chunk while ensuring full coverage of the thread. Research on chunk-based representations confirms that segmentation can preserve essential seman… view at source ↗

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Works this paper leans on

40 extracted references · 10 linked inside Pith

  1. [8]

    Establishing social media firestorm scale via large dataset media analytics,

    K. Nuortimo, E. Karvonen, and J. H ¨ark¨onen, “Establishing social media firestorm scale via large dataset media analytics,”Journal of Marketing Analytics, vol. 8, no. 4, pp. 224–233, 2020

    2020

  2. [1]

    Understanding online firestorms: Negative word-of-mouth dynamics in social media networks,

    J. Pfeffer, T. Zorbach, and K. M. Carley, “Understanding online firestorms: Negative word-of-mouth dynamics in social media networks,”Journal of Marketing Communications, vol. 20, no. 1–2, pp. 117–128, 2014

    2014

  3. [2]

    Digital social norm enforcement: Online firestorms in social media,

    K. Rost, L. Stahel, and B. S. Frey, “Digital social norm enforcement: Online firestorms in social media,”PLOS ONE, vol. 11, no. 6, p. e0155923, 2016

    2016

  4. [3]

    Brand crises in the digital age: The short- and long-term effects of social media firestorms on consumers and brands,

    N. Hansen, A.-K. Kupfer, and T. Hennig-Thurau, “Brand crises in the digital age: The short- and long-term effects of social media firestorms on consumers and brands,”International Journal of Research in Marketing, vol. 35, no. 4, pp. 557–574, 2018

    2018

  5. [4]

    Understanding the impact of online firestorms on financial performance in corporate social responsibility campaigns,

    F. Dias, P. Rita, N. Ant ´onio, and C. V ong, “Understanding the impact of online firestorms on financial performance in corporate social responsibility campaigns,”Marketing Intelligence & Planning, vol. 43, no. 6, pp. 1199–1219, 2025

    2025

  6. [5]

    Silence is golden? Mitigating different types of online firestorms of Fortune 100 corporations on Twitter,

    J. G. Qu, J. Yi, W. J. Zhang, and C. Y . Yang, “Silence is golden? Mitigating different types of online firestorms of Fortune 100 corporations on Twitter,”Public Relations Review, vol. 49, no. 5, p. 102391, 2023

    2023

  7. [6]

    Does my social media burn? – identify features for the early detection of company-related online firestorms on Twitter,

    K. Koch, A. Dippel, and M. Schumann, “Does my social media burn? – identify features for the early detection of company-related online firestorms on Twitter,”Online Social Networks and Media, vol. 25, p. 100151, 2021

    2021

  8. [7]

    Detect- ing, preventing, and mitigating online firestorms in brand communities,

    D. Herhausen, S. Ludwig, D. Grewal, J. Wulf, and M. Schoegel, “Detect- ing, preventing, and mitigating online firestorms in brand communities,” Journal of Marketing, vol. 83, no. 3, pp. 1–21, 2019

    2019

Show all 40 references
  1. [9]

    A survey on LLM-as-a-Judge,

    J. Gu, X. Jiang, Z. Shi, H. Tan, X. Zhai, C. Xu, W. Li, Y . Shen, S. Ma, H. Liu, S. Wang, K. Zhang, Y . Wang, W. Gao, L. Ni, and J. Guo, “A survey on LLM-as-a-Judge,”arXiv preprint arXiv:2411.15594, 2024

    2024 arXiv

  2. [10]

    The digital outcry: What incites participation behavior in an online firestorm?

    M. Johnen, M. Jungblut, and M. Ziegele, “The digital outcry: What incites participation behavior in an online firestorm?”New Media & Society, vol. 20, no. 9, pp. 3140–3160, 2018

    2018

  3. [11]

    What drives people to participate in online firestorms?

    M. Gruber, C. Mayer, and S. A. Einwiller, “What drives people to participate in online firestorms?”Online Information Review, vol. 44, no. 3, pp. 563–581, 2020

    2020

  4. [12]

    How harmful are online firestorms for brands? An approach to the phenomenon from the participant level,

    E. Delgado-Ballester, I. L ´opez-L´opez, and A. Bernal-Palaz ´on, “How harmful are online firestorms for brands? An approach to the phenomenon from the participant level,”Spanish Journal of Marketing – ESIC, vol. 24, no. 1, pp. 133–151, 2019

    2019

  5. [13]

    Judging the wrongness of firms in social media firestorms: The heuristic and systematic information processing perspective,

    T. K. H. Chan, Z. W. Y . Lee, D. Skoumpopoulou, and F. Situmeang, “Judging the wrongness of firms in social media firestorms: The heuristic and systematic information processing perspective,”Journal of the Association for Information Systems, vol. 25, no. 2, pp. 463–500, 2024

    2024

  6. [14]

    Detecting online firestorms in social media,

    B. Drasch, J. Huber, S. Panz, and F. Probst, “Detecting online firestorms in social media,” inProceedings of the 23rd European Conference on Information Systems (ECIS), 2015

    2015

  7. [15]

    Identifying lexical change in negative word-of-mouth on social media,

    W. Strathern, R. Ghawi, M. Sch ¨onfeld, and J. Pfeffer, “Identifying lexical change in negative word-of-mouth on social media,”Social Network Analysis and Mining, vol. 12, no. 1, p. 59, 2022

    2022

  8. [16]

    Developing a social media firestorm scale: From conceptualization to AI-assisted validation,

    K. Nuortimo, J. Harkonen, K. Breznik, and R. Hannes, “Developing a social media firestorm scale: From conceptualization to AI-assisted validation,”Journal of Marketing Analytics, 2025

    2025

  9. [17]

    Emotion shapes the diffusion of moralized content in social networks,

    W. J. Brady, J. A. Wills, J. T. Jost, J. A. Tucker, and J. J. Van Bavel, “Emotion shapes the diffusion of moralized content in social networks,” Proceedings of the National Academy of Sciences, vol. 114, no. 28, pp. 7313–7318, 2017

    2017

  10. [18]

    Against the others! Detecting moral outrage in social media networks,

    W. Strathern, M. Schoenfeld, R. Ghawi, and J. Pfeffer, “Against the others! Detecting moral outrage in social media networks,” in2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020, pp. 322–326

    2020

  11. [19]

    Collective empowerment and connective outcry: What legitimize netizens to engage in negative word-of-mouth of online firestorms?

    J. G. Qu, C. Y . Yang, A. A. Chen, and S. Kim, “Collective empowerment and connective outcry: What legitimize netizens to engage in negative word-of-mouth of online firestorms?”Public Relations Review, vol. 50, no. 2, p. 102438, 2024

    2024

  12. [20]

    Understanding the drivers and outcomes of ideologically charged social media firestorms: The sociotechnical and social learning perspectives,

    T. K. H. Chan, Z. W. Y . Lee, M. Pan, and K. Sun, “Understanding the drivers and outcomes of ideologically charged social media firestorms: The sociotechnical and social learning perspectives,”Journal of Manage- ment Information Systems, vol. 42, no. 3, pp. 737–766, 2025

    2025

  13. [21]

    The use of Reddit as an inexpensive source for high-quality data,

    M. R. Jamnik and D. J. Lane, “The use of Reddit as an inexpensive source for high-quality data,” University of Massachusetts Amherst, Tech. Rep., 2017

    2017

  14. [22]

    Passive data collection on Reddit: A practical approach,

    T. Rocha-Silva, C. Nogueira, and L. Rodrigues, “Passive data collection on Reddit: A practical approach,”Research Ethics, vol. 20, no. 3, pp. 453–470, 2024

    2024

  15. [23]

    Purposive sampling: Complex or simple? Research case examples,

    S. Campbell, M. Greenwood, S. Prior, T. Shearer, K. Walkem, S. Young, D. Bywaters, and K. Walker, “Purposive sampling: Complex or simple? Research case examples,”Journal of Research in Nursing, vol. 25, no. 8, pp. 652–661, 2020

    2020

  16. [24]

    Inter-coder agreement for computational linguistics,

    R. Artstein and M. Poesio, “Inter-coder agreement for computational linguistics,”Computational Linguistics, vol. 34, no. 4, pp. 555–596, 2008

    2008

  17. [25]

    The NLP task effectiveness of long-range transformers,

    G. Qin, Y . Feng, and B. Van Durme, “The NLP task effectiveness of long-range transformers,”arXiv preprint arXiv:2202.07856, 2022

    2022 arXiv

  18. [26]

    ChuLo: Chunk-level key information representation for long document understanding,

    Y . Li, S. C. Han, Y . Dai, and F. Cao, “ChuLo: Chunk-level key information representation for long document understanding,”arXiv preprint arXiv:2410.11119, 2024

    2024 arXiv

  19. [27]

    Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

    P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,”arXiv preprint arXiv:2107.13586, 2021

    2021 arXiv

  20. [28]

    Context-aware hierarchical merging for long document summarization,

    L. Ou and M. Lapata, “Context-aware hierarchical merging for long document summarization,”arXiv preprint arXiv:2502.00977, 2025

    2025 arXiv

  21. [29]

    Hierarchical context merging: Better long context understanding for pre-trained LLMs,

    W. Song, S. Oh, S. Mo, J. Kim, S. Yun, J.-W. Ha, and J. Shin, “Hierarchical context merging: Better long context understanding for pre-trained LLMs,”arXiv preprint arXiv:2404.10308, 2024

    2024 arXiv

  22. [30]

    Retrieval-augmented generation for large language models: A survey,

    Y . Gao, Y . Xiong, X. Gao, K. Jia, J. Pan, Y . Bi, Y . Dai, J. Sun, M. Wang, and H. Wang, “Retrieval-augmented generation for large language models: A survey,”arXiv preprint arXiv:2312.10997, 2024

    2024 arXiv

  23. [31]

    Random search for hyper-parameter opti- mization,

    J. Bergstra and Y . Bengio, “Random search for hyper-parameter opti- mization,” inJournal of Machine Learning Research, vol. 13, 2012, pp. 281–305

    2012

  24. [32]

    Optimizing F-measure: A tale of two approaches,

    Y . Nan, K. M. Chai, W. S. Lee, and H. L. Chieu, “Optimizing F-measure: A tale of two approaches,”arXiv preprint arXiv:1206.4625, 2012

    2012 arXiv

  25. [33]

    GPT-4o mini: Advancing cost-efficient intelligence,

    OpenAI, “GPT-4o mini: Advancing cost-efficient intelligence,” https: //openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence, 2024

    2024

  26. [34]

    GPT-4 technical report,

    OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altmanet al., “GPT-4 technical report,”arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  27. [35]

    The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,

    D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,”BMC Genomics, vol. 21, no. 1, p. 6, 2020

    2020

  28. [36]

    Assessing the impact of contextual information in hate speech detection,

    J. M. P ´erez, F. M. Luque, D. Zayat, M. Kondratzky, A. Moro, P. S. Serrati, J. Zajac, P. Miguel, N. Debandi, A. Gravano, and V . Cotik, “Assessing the impact of contextual information in hate speech detection,” IEEE Access, vol. 11, pp. 30 575–30 590, 2023

    2023

  29. [37]

    Quantifying limits to detection of early warning for critical transitions,

    C. Boettiger and A. Hastings, “Quantifying limits to detection of early warning for critical transitions,”Journal of The Royal Society Interface, vol. 9, no. 75, pp. 2527–2539, 2012

    2012

  30. [38]

    Studying Reddit: A systematic overview of disciplines, approaches, methods, and ethics,

    N. Proferes, N. Jones, S. Gilbert, C. Fiesler, and M. Zimmer, “Studying Reddit: A systematic overview of disciplines, approaches, methods, and ethics,”Social Media + Society, vol. 7, no. 2, p. 20563051211019004, 2021

    2021

  31. [39]

    Large language models are human-level prompt engineers,

    Y . Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba, “Large language models are human-level prompt engineers,”arXiv preprint arXiv:2211.01910, 2022

    2022 arXiv

  32. [40]

    Fueling the firestorm: Effects of social capital on users’ persuasiveness during online firestorms,

    H. J ¨ontgen, “Fueling the firestorm: Effects of social capital on users’ persuasiveness during online firestorms,” inProceedings of the 28th European Conference on Information Systems (ECIS), Jun. 2020. [Online]. Available: https://aisel.aisnet.org/ecis2020 rp/179

    2020

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