Pith. sign in

REVIEW 4 major objections 6 minor 50 references

Explicit and Implicit Data Augmentation for Social Event Detection

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read A dual augmentation framework combining LLM text rewrites with feature-space perturbations reports average F1 of 91.28 on Twitter2012 and 88.02 on Twitter2018, beating the best baselines by roughly 15–17%.

desk verdict The augmentation effect is real but modest; the paper's 17.67% headline is mostly the base model's strength, not the augmentation's. read the letter →

arxiv 2509.04202 v1 pith:ZTPFYKQK submitted 2025-09-04 cs.CL cs.SI

Congbo Ma , Yuxia Wang , Jia Wu , Jian Yang , Jing Du , Zitai Qiu , Qing Li , Hu Wang
show 1 more author
Preslav Nakov
This is my paper · ORCID
classification cs.CLcs.SI
keywords socialeventdetectiondataaugmentationlargelanguagemodelsfeature-spacegraphneuralnetworksTwitterbenchmarkslow-resourcelearningclassimbalance
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

Social event detection needs labeled tweets, which are expensive to annotate. This paper proposes SED-Aug, a plug-and-play augmentation framework that attacks label scarcity from two directions at once: explicit augmentation uses a large language model to rewrite each message in five ways, and implicit augmentation perturbs structure-fused message embeddings in feature space with five noise schemes. On the Twitter2012 and Twitter2018 benchmarks, the augmented model reports average F1 scores of 91.28 and 88.02, respectively, about 17.67% and 15.57% above the best published baselines. The paper further shows that both augmentation halves help, that gains are largest under class imbalance and limited data, and that the LLM cost is paid offline so the final detector needs no LLM at inference.

What carries the argument

The carrying mechanism is the dual augmentation loop on top of a graph-based event classifier. Explicit augmentation (one-stage: paraphrasing, adding context, style transfer, entity-preserving paraphrase; two-stage: extract keywords/entities/knowledge graph and rewrite) is applied to raw messages before embedding. Implicit augmentation then operates on the structure-fused message embeddings, using five perturbation schemes—Gaussian Perturbation, Proportional Gaussian Perturbation, In-Distribution Gaussian Perturbation, Clipped Gaussian Perturbation, and Frequency-Domain Perturbation—selected with probability threshold alpha. The point of the dual design is that LLMs handle text diversity whi

What would settle it

Run the exact SED-Aug pipeline with both augmentation stages switched off on the full Twitter2012 and Twitter2018 training sets and compare its F1 against the augmented version. If the unaugmented model already matches or beats the published baselines, the headline improvement is not attributable to augmentation; if the augmented model's gain over the same-base-model control equals the reported gain, the dual augmentation claim is confirmed.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that data scarcity in social event detection can be substantially relieved by combining two complementary augmentation types rather than choosing one. Explicit augmentation enriches the surface text with LLM-generated paraphrases, context, style shifts, entity-preserving rewrites, and extract-and-rewrite variants; implicit augmentation then adds controlled noise to the structure-fused message embeddings produced by a graph-based SED model. Together these transformations keep semantic and relational content intact while increasing training diversity, and the paper reports best-published average F1 scores on Kawarith6, Twitter2012, and Twitter2018

Load-bearing premise

The paper's central improvement claim treats the gap to published baselines as the effect of augmentation, although its own Table 7 shows the unaugmented base model on Twitter2012 already reaches 85.71 average F1—well above the 77.57 best baseline—so part of the headline gain is inherited from the base model rather than caused by augmentation.

Editorial extensions

If this is right

  • Plug-and-play augmentation works across three different event-detection datasets, including an Arabic crisis corpus, with no re-annotation cost.
  • LLM augmentation can be done in advance; inference remains cheap because the detector never calls the LLM.
  • Feature-space augmentation helps most when classes are imbalanced, as shown by the large Macro F1 gains on Twitter2018.
  • Dual augmentation is most valuable under data scarcity: using 10% of Twitter2012, it adds 10.29 points of average F1 over the unaugmented model.
  • All five explicit strategies beat the no-explicit baseline, with entity-preserving paraphrase the strongest overall, while keyword-based rewriting dominates on two of three datasets.

Reading between the lines

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

  • The paper's headline percentages compare SED-Aug to published baselines rather than to its own unaugmented twin; a direct matched-base-model test on the full Twitter splits would reveal how much of the stated gain is due to augmentation and how much to the base graph model and BERT embeddings.
  • The five feature-space perturbations could be combined explicitly rather than selected one at a time, since they act on different aspects (scale, distribution, clip bounds, frequency); an ensemble or learned mixture may yield further diversity gains.
  • If the plug-and-play claim holds, the same dual augmentation should transfer to streaming or open-set event detection, where labels are even scarcer and new event types appear over time; a natural test is to attach SED-Aug to an incremental SED model and measure forgetting.
  • The LLM-based explicit augmentation strategies should be sensitive to model choice; the paper validates GPT-4o-mini against stronger LLMs, but a broader sweep could show whether cheaper or domain-tuned models preserve the gains.
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 / 6 minor

Summary. The paper proposes SED-Aug, a dual augmentation framework for social event detection. Explicit augmentation uses LLM-based generation strategies (paraphrasing, adding context, style transfer, entity-preserving paraphrase, extract-and-rewrite) to create diversified text, while implicit augmentation applies five perturbation methods (GP, PGP, IDGP, CGP, FDP) to structural fused embeddings in feature space. The framework is evaluated on Kawarith6, Twitter2012, and Twitter2018 against a set of published baselines. The authors report state-of-the-art average F1 scores, with claimed improvements over the best baseline of about 17.67% on Twitter2012 and 15.57% on Twitter2018. Ablation studies, limited-data experiments, and distribution visualizations are provided to support the contribution.

Significance. If the augmentation effect were cleanly established, SED-Aug would be a practically useful and reproducible recipe for low-resource social event detection: it moves LLM cost to augmentation time, combines text-level and feature-level diversity, and includes public code. The paper also ships a relatively broad set of ablations and a candid limitations section. However, the central quantitative claim is currently confounded by the strength of the unaugmented base model, so the significance cannot be assessed until the augmentation-specific gain is isolated.

major comments (4)
  1. [§1, §4.2, Table 1 vs. Table 7] The headline claim that SED-Aug outperforms the best baseline by 17.67% on Twitter2012 is not attributable to the augmentation framework. Table 7 reports the framework's unaugmented base model at the 70% training ratio with Micro F1 87.72, Macro F1 83.69, and average 85.71. Table 1 lists GraphHAM as the best baseline with average F1 77.57 on Twitter2012. The base model alone therefore exceeds the best baseline by 8.14 points absolute (10.49% relative), while augmentation adds only 5.57 points absolute (6.50% relative) over the base. The abstract and conclusion credit the full 17.67% relative improvement to the dual augmentation, which overstates the augmentation effect. The unaugmented base model must be included in Table 1 (or a directly comparable table), and the text should separate the base-model advantage from the augmentation gain.
  2. [§3, §4.1, Figure 1] The paper describes SED-Aug as 'plug-and-play' and claims it can be integrated into a SED model, but the base model used in all experiments is never identified. The pipeline mentions a pre-trained language model, social graph construction, and graph aggregation, but the specific SED classifier/backbone is not named or described. Without this, the reader cannot tell whether the strong unaugmented performance comes from the backbone architecture, preprocessing, or the augmentation. To support the plug-and-play claim, the authors should name the base model and run the augmentation on at least one additional existing SED model (e.g., GraphHAM or GraphMSE).
  3. [Appendix A.4] The probability threshold α and other implicit-augmentation hyperparameters (σ, c, r, η) appear to be tuned directly on test-set performance. Appendix A.4 reports test Micro/Macro F1 for α = 0.1, 0.3, 0.6 on all three datasets, and the final choice α = 0.6 coincides with the highest test scores per dataset. No validation-set-based selection or nested evaluation is described. This protocol can inflate the reported absolute numbers and weakens the comparison. The authors should either select hyperparameters on the validation split and report test results for the selected configuration, or explicitly discuss the resulting optimism.
  4. [Eq. (9), §3.2] The definition of FDP is not implementable as written. In Eq. (9), N is defined earlier as the number of message samples, but the slice F_i[N - r*N : N] is applied to the frequency-domain representation of a single embedding, whose length should be the embedding dimension D, not N. The high-frequency/low-frequency split must be specified with respect to the feature dimension. Eq. (10) also mixes a complex Gaussian notation with a scalar 'noise level η' without defining the elementwise operation. This is a reproducibility issue for one of the five proposed implicit augmentation methods.
minor comments (6)
  1. [Table 3 and §4.3] The claim that implicit augmentation 'consistently provides additional benefits' is not directly supported: Table 3 has no row for 'no implicit augmentation' within the same table. The baseline value is inferred from Table 2. Add an explicit 'w/o implicit' row to Tables 3 and 4.
  2. [Appendix A.5] The zero/few-shot LLM results on Kawarith6 (few-shot Micro F1 98.1, Macro F1 98.0) are very close to SED-Aug (98.41/98.29). The paper should discuss more carefully why this does not undermine the claimed advantage of the augmentation framework over a direct LLM classifier, beyond the cost argument.
  3. [§3.2, Eq. (5)] The notation n_PGP ∼ N(0, σ²)·G is ambiguous: G is a set of embeddings, not a scalar; the intended operation is presumably an elementwise product with the current embedding magnitude. Clarify the formula.
  4. [§3.2, Eq. (6)] The symbol α is used for the probability threshold in Eq. (2) and for the variance control parameter in Eq. (6). These are different hyperparameters; use distinct notation.
  5. [§4.7, Figures 3–4] The distribution visualizations are qualitative. Reporting a quantitative distribution dissimilarity measure (e.g., KL divergence or Wasserstein distance) before and after augmentation would strengthen the claim that the perturbation preserves the overall distribution.
  6. [General] No standard deviations or significance tests are reported for any of the tables. Given the small gaps between some configurations (e.g., Twitter2012 Micro F1 92.89 vs. 93.03), error bars or repeated-run statistics would help assess robustness.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; minor non-load-bearing self-citations and a base-model attribution concern, but no derivation reduces to its inputs.

full rationale

This paper is an empirical systems paper: it does not contain a derivation chain that could collapse into its own inputs. Explicit augmentation (Eq. 1) and implicit perturbations (Eqs. 4-11) are recipes whose reported F1 numbers come from experiments, not from those equations alone. The headline 17.67%/15.57% improvements are computed directly from Table 1 against GraphHAM (Qiu et al. 2024a), a baseline co-authored by some of the present authors; using it as a comparator is not circular because the comparison is to a fixed published system, not to a parameter fitted from the same data. The strongest correctness concern is attribution: Table 7 shows the unaugmented base already reaches 85.71 average F1 on Twitter2012 at 70% data, versus 77.57 for GraphHAM in Table 1, so much of the gap over baselines is inherited from the base model rather than from augmentation. This is a reporting/experimental-design weakness, not a circular reduction. Self-citations (Ma et al. 2024, 2025; Qiu et al. 2024a,b; Zhuang et al. 2022) occur in related work, definitions, baselines, and one factuality-citation, but none carries a load-bearing argument; the augmentation benefit is supported by within-paper ablations (Tables 2,4,7). Hyperparameter values (α, σ, c, r, η) are dataset-specific, and Appendix A.4 shows α selected by test performance, which raises a test-set-overfitting concern but does not make any reported quantity equal to an input by construction. Section 6 itself concedes there is no criterion for optimal augmentation amount, a stated limitation rather than a circular step. Overall, no step reduces Eq. X to Eq. Y or renames a fitted value as a prediction. Score 2 for minor non-load-bearing self-citations; the central empirical claim has independent content.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The framework introduces no new entities, only tuned noise magnitudes and an LLM prompt recipe. The main free parameters are the per-dataset hyperparameters and the final selection of the best implicit method, all chosen after observing test-set performance. The unspecified base model is an unstated dependency that the central comparison relies on.

free parameters (6)
  • probability threshold alpha = 0.3 (Kawarith6), 0.6 (Twitter2012), 0.6 (Twitter2018)
    Controls whether augmented or original embedding is used during training; tuned per dataset.
  • noise standard deviation sigma = 0.01 (Kawarith6), 0.1 (Twitter2012), 0.1 (Twitter2018)
    Magnitude of Gaussian perturbation; tuned per dataset.
  • clipping range c = 0.005 (Kawarith6), 0.05 (Twitter2012), 0.0006 (Twitter2018)
    Bound for Clipped Gaussian Perturbation; tuned per dataset.
  • keep ratio r for FDP = 0.98 (Kawarith6), 0.95 (Twitter2012), 0.98 (Twitter2018)
    Proportion of frequency components retained in frequency-domain perturbation; tuned per dataset.
  • noise level eta for FDP = 0.02 (all datasets)
    Scaling of complex noise added in the frequency domain.
  • best implicit augmentation method = PGP (Kawarith6), CGP (Twitter2012), PGP (Twitter2018)
    The final method is selected per dataset after inspecting test-set F1 scores across all five variants.
assumptions (3)
  • domain assumption The base SED model and graph aggregation method are effective and are the same across all compared configurations.
    Section 3 describes 'a graph aggregation method' and 'a SED model' without naming a concrete architecture; the empirical results depend on this unspecified base model.
  • domain assumption LLM-generated rewrites preserve the event category label of the original message.
    Section 3.1 creates augmented texts and reuses the original labels; a bias check in Section 4.3 only spot-checks 50 examples for factuality, not label preservation.
  • ad hoc to paper In Eq. (9), the slice F i[N - r*N : N] of the Fourier-transformed embedding corresponds to high-frequency components.
    The formula indexes frequency components by the number of messages N and assumes the tail of the FFT output is the high-frequency part; this is implementation-dependent and not justified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Explicit and Implicit Data Augmentation for Social Event Detection." pith.science (2026). https://pith.science/paper/ZTPFYKQK

@misc{pith2026250904202,
  author       = {Pith},
  title        = {Pith review of: Explicit and Implicit Data Augmentation for Social Event Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZTPFYKQK}},
  note         = {Machine review of arXiv:2509.04202}
}
read the original abstract

Social event detection involves identifying and categorizing important events from social media, which relies on labeled data, but annotation is costly and labor-intensive. To address this problem, we propose Augmentation framework for Social Event Detection (SED-Aug), a plug-and-play dual augmentation framework, which combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. The explicit augmentation utilizes large language models to enhance textual information through five diverse generation strategies. For implicit augmentation, we design five novel perturbation techniques that operate in the feature space on structural fused embeddings. These perturbations are crafted to keep the semantic and relational properties of the embeddings and make them more diverse. Specifically, SED-Aug outperforms the best baseline model by approximately 17.67% on the Twitter2012 dataset and by about 15.57% on the Twitter2018 dataset in terms of the average F1 score. The code is available at GitHub: https://github.com/congboma/SED-Aug.

Figures

Figures reproduced from arXiv: 2509.04202 by the authors.

Figure 1
Figure 1. The framework of SED-Aug model. For (1), various token and sentence level modifi￾cations diversify training data. Common strategies include insertion (Xie et al., 2020), deletion (Wei and Zou, 2019), and masking (Ghosh et al., 2023; Yu et al., 2023) to improve generalization. Re￾placement techniques (Kobayashi, 2018) include synonym substitution, entity replacement (Liu and Cui, 2023), and semantic modifications (Zh… view at source ↗
Figure 2
Figure 2. Data imbalanced in the SED datasets [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Data histogram distribution before and after implicit augmentation (GP). Subfigure (a) and (c) are [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The PCA visualization before and after im [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Example of explicit augmentation. extrac_augment_prompt = """ You are asked to first extract entity, important words, sentences or knowledge graph for a social media post and then perform data augmentation by rewriting or paraphrasing. Given the post: {post}, Directly …
Figure 6
Figure 6. Figure 6: Prompt for the explict data augmentation. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 48 canonical work pages

  1. [1]

    Akiko N. Aizawa. 2003. An information-theoretic perspective of TF-IDF measures. Information Processing and Management., 39(1):45--65

    work page 2003

  2. [2]

    Alaa Alharbi and Mark Lee. 2021. Kawarith: an arabic twitter corpus for crisis events. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 42--52

    work page 2021

  3. [3]

    Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S. Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous GNNs . In The Web Conference 2021, pages 3383--3395, Virtual Event / Ljubljana, Slovenia

    work page 2021

  4. [4]

    Yuwei Cao, Hao Peng, Zhengtao Yu, and Philip S. Yu. Hierarchical and incremental structural entropy minimization for unsupervised social event detection. In Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advan...

    work page 2024

  5. [5]

    Terrance DeVries and Graham W. Taylor. 2017. Dataset augmentation in feature space. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings

    work page 2017

  6. [6]

    Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard H

    Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard H. Hovy. 2021. A survey of data augmentation approaches for NLP . In Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021 , volume ACL/IJCNLP 2021 of Findings of ACL , pages 968--988

    work page 2021

  7. [7]

    Sreyan Ghosh, Utkarsh Tyagi, Manan Suri, Sonal Kumar, Ramaneswaran S., and Dinesh Manocha. 2023. ACLM: a selective-denoising based generative data augmentation approach for low-resource complex NER . In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 202...

    work page 2023

  8. [8]

    Hamilton, Zhitao Ying, and Jure Leskovec

    William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, NeurIPS 2017, pages 1024--1034, Long Beach, USA

    work page 2017

Show all 50 references
  1. [9]

    Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous graph transformer. In The Web Conference 2020, pages 2704--2710, Taipei, China

    2020

  2. [10]

    Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, and Jiawei Han. 2024. Large language models on graphs: A comprehensive survey. IEEE Transactions on Knowledge and Data Engineering

    2024

  3. [11]

    Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tom \' a s Mikolov. 2017. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, pages 427--431, Valencia, Spain

    2017

  4. [12]

    Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, volume 1, page 2

    2019

  5. [13]

    Kipf and Max Welling

    Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France

    2017

  6. [14]

    Sosuke Kobayashi. 2018. Contextual augmentation: data augmentation by words with paradigmatic relations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orlea...

    2018

  7. [15]

    Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, and Philip S Yu. 2024. Relational prompt-based pre-trained language models for social event detection. ACM Transactions on Information Systems

    2024

  8. [16]

    Yi Li, Yilun Jin, Guojie Song, Zihao Zhu, Chuan Shi, and Yiming Wang. 2021. Graphmse: efficient meta-path selection in semantically aligned feature space for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI 2021, volume 35, pages 4206--4214

    2021

  9. [17]

    Wenzhong Liu and Xiaohui Cui. 2023. Improving named entity recognition for social media with data augmentation. Applied Sciences, 13(9):5360

    2023

  10. [18]

    Ang Lv, Jinpeng Li, Yuhan Chen, Gao Xing, Ji Zhang, and Rui Yan. 2023. Dialogps: Dialogue path sampling in continuous semantic space for data augmentation in multi-turn conversations. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V...

    2023

  11. [19]

    Congbo Ma, Zitai Qiu, Hu Wang, Jing Du, Shan Xue, Jia Wu, and Jian Yang. 2025. Enhanced social event detection through dynamically weighted meta-paths modeling. In Companion Proceedings of the ACM on Web Conference 2025, pages 1184--1188

    2025

  12. [20]

    Congbo Ma, Hu Wang, Zitai Qiu, Shan Xue, Jia Wu, Jian Yang, Preslav Nakov, and Quan Z Sheng. 2024. Learning to sample the meta-paths for social event detection. arXiv preprint arXiv:2411.12588

    2024 arXiv

  13. [21]

    Pablo Mart \' , Leticia Serrano-Estrada, and Almudena Nolasco-Cirugeda. 2019. Social media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74:161--174

    2019

  14. [22]

    B \' e atrice Mazoyer, Julia Cag \' e , Nicolas Herv \' e , and C \' e line Hudelot. 2020. A french corpus for event detection on twitter. In Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, pages 6220--6227, Marseille, France

    2020

  15. [23]

    Andrew J McMinn, Yashar Moshfeghi, and Joemon M Jose. 2013. Building a large-scale corpus for evaluating event detection on twitter. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pages 409--418

    2013

  16. [24]

    Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In The 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings, Scottsdale, Arizona, USA

    2013

  17. [25]

    Tahir M Nisar and Man Yeung. 2018. Twitter as a tool for forecasting stock market movements: a short-window event study. The journal of finance and data science, 4(2):101--119

    2018

  18. [26]

    Viktor Pekar, Jane Binner, Hossein Najafi, Chris Hale, and Vincent Schmidt. 2020. Early detection of heterogeneous disaster events using social media. Journal of the Association for Information Science and Technology, 71(1):43--54

    2020

  19. [27]

    Yu, and Lifang He

    Hao Peng, Jianxin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, and Lifang He. 2021 a . Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Trans. Knowl. Discov. Data , 15(5):89:1--89:33

    2021

  20. [28]

    Hao Peng, Jianxin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S Yu, and Lifang He. 2021 b . Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data, 15(5):1--33

    2021

  21. [29]

    Hao Peng, Ruitong Zhang, Shaoning Li, Yuwei Cao, Shirui Pan, and S Yu Philip. 2022. Reinforced, incremental and cross-lingual event detection from social messages. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):980--998

    2022

  22. [30]

    Zitai Qiu, Congbo Ma, Jia Wu, and Jian Yang. 2024 a . An efficient automatic meta-path selection for social event detection via hyperbolic space. In Proceedings of the ACM on Web Conference 2024, WWW 2024 , pages 2519--2529, Singapore

    2024

  23. [31]

    Zitai Qiu, Jia Wu, Jian Yang, Xing Su, and Charu C Aggarwal. 2024 b . Heterogeneous social event detection via hyperbolic graph representations. IEEE Transactions on Big Data

    2024

  24. [32]

    Yu, and Lifang He

    Jiaqian Ren, Lei Jiang, Hao Peng, Yuwei Cao, Jia Wu, Philip S. Yu, and Lifang He. 2022 a . From known to unknown: Quality-aware self-improving graph neural network for open set social event detection. In Proceedings of the 31st ACM International Conference on Information & Kno...

    2022

  25. [33]

    Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, and Philip S. Yu. 2022 b . Evidential temporal-aware graph-based social event detection via dempster-shafer theory. In IEEE International Conference on Web Services, ICWS 2022 , pages 331--336, Barcelona, Spain

    2022

  26. [34]

    Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving neural machine translation models with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long P...

    2016

  27. [35]

    Yizhou Sun and Jiawei Han. 2012. Mining heterogeneous information networks: principles and methodologies. Morgan & Claypool Publishers

    2012

  28. [36]

    Tuuli Toivonen, Vuokko Heikinheimo, Christoph Fink, Anna Hausmann, Tuomo Hiippala, Olle J \"a rv, Henrikki Tenkanen, and Enrico Di Minin. 2019. Social media data for conservation science: A methodological overview. Biological Conservation, 233:298--315

    2019

  29. [37]

    Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li \` o , and Yoshua Bengio. 2018. Graph attention networks. In The 6th International Conference on Learning Representations, ICLR 2018, Vancouver, Canada

    2018

  30. [38]

    Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, and Cheng Wu. 2019. Implicit semantic data augmentation for deep networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December...

    2019

  31. [39]

    Georgiev, Jiahui Geng, and Preslav Nakov

    Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, and Preslav Nakov. Openfactcheck: A unified framework for factuality evaluation of LLMs . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024-Demo, Novembe...

    2024

  32. [40]

    Zhongqing Wang and Yue Zhang. 2017. A neural model for joint event detection and summarization. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pages 4158--4164, Melbourne, Australia

    2017

  33. [41]

    Wei and Kai Zou

    Jason W. Wei and Kai Zou. 2019. EDA: easy data augmentation techniques for boosting performance on text classification tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Languag...

    2019

  34. [42]

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4--24

    2020

  35. [43]

    Dominik Wurzer, Victor Lavrenko, and Miles Osborne. 2015. Twitter-scale new event detection via k-term hashing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pages 2584--2589, Lisbon, Portugal

    2015

  36. [44]

    Hovy, Thang Luong, and Quoc Le

    Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised data augmentation for consistency training. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12...

    2020

  37. [45]

    Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. 2015. A probabilistic model for bursty topic discovery in microblogs. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015 , pages 353--359, Austin, USA

    2015

  38. [46]

    Jianfei Yu, Qiankun Zhao, and Rui Xia. 2023. Cross-domain data augmentation with domain-adaptive language modeling for aspect-based sentiment analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023,...

    2023

  39. [47]

    Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 793--803

    2019

  40. [48]

    Haojie Zhuang, Wei Emma Zhang, Jian Yang, Congbo Ma, Yutong Qu, and Quan Z Sheng. 2022. Learning from the source document: unsupervised abstractive summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4194--4205

    2022

  41. [49]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  42. [50]

    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...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.