Pith. sign in

REVIEW 4 major objections 6 minor 54 references

Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read Topic-aware prompting lets large language models generate multi-platform social media posts that stay close to real data.

desk verdict Useful benchmark of LLM synthetic multi-platform data, but the new MPTM mechanism is untested—no evidence that the random BERTopic cluster it samples from is a real cross-platform topic. read the letter →

arxiv 2505.02858 v1 pith:VUVHPEHA submitted 2025-05-02 cs.CL cs.CY

Henry Tari , Nojus Sereiva , Rishabh Kaushal , Thales Bertaglia , Adriana Iamnitchi This is my paper · ORCID
classification cs.CLcs.CY
keywords syntheticdatalargelanguagemodelsmulti-platformsocialmediatopic-basedpromptingfidelityBERTopicfew-shotnamedentityanalysis
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

The paper asks whether a large language model, given a real corpus of posts from several social media platforms, can generate a synthetic corpus that a researcher could share or reuse without violating platform rules. It proposes MPTM, a prompting strategy that chooses few-shot examples from topic clusters spanning multiple platforms, and tests it with GPT-4o, Gemini 2.0 Flash, and Claude 3.5 Haiku on two real datasets that together cover Twitter, Facebook, Reddit, Instagram, TikTok, and YouTube. On lexical features, sentiment, topics, embeddings, and named-entity structure, the generated posts are often close to the real ones, but no model matches real data on every metric, so the authors describe the result as promising rather than production-ready. The paper also contributes fidelity metrics designed for multi-platform data, including a named-entity bipartite-graph comparison. In the discussion section, the authors note the evaluation is limited to three models from early 2025 and does not yet test downstream task utility or privacy guarantees.

What carries the argument

The load-bearing mechanism is Multi-Platform Topic Model (MPTM) prompting: all posts from the different platforms are embedded with a MiniLM sentence transformer, reduced with UMAP, clustered with HDBSCAN, and labeled with c-TFIDF topic keywords; one topic cluster is then selected at random and a few posts from each platform in that cluster are used as few-shot examples in the prompt. This makes the examples topically coherent across platforms, so the model sees the shared-topic structure that defines a multi-platform corpus, rather than independent platform silos. The comparison baseline, per-platform prompting, draws random examples from a single platform instead. The paper's multi-platform fidelity metrics, especially the named-entity bipartite graph that links posts on different platforms to common entities, are designed to detect whether that cross-platform structure survives generation.

What would settle it

Run MPTM on a synthetic test corpus in which the same topic is discussed on each platform using deliberately disjoint vocabularies, and check whether the generated posts follow the shared topic or drift to platform-specific wording; if they drift, the cross-platform clusters are artifacts of wording. A cheaper check is a randomized control that replaces topic-selected few-shot examples with the same number of examples sampled uniformly from all platforms; if topic-overlap fidelity does not drop, the topic-modeling step is not carrying the argument.

Watch

Extended reading notes

Core claim

The central claim is that current LLMs, when prompted with examples drawn from topics that appear across several platforms, produce synthetic multi-platform posts whose platform-specific lexical habits, sentiment tone, topic structure, embedding similarity, and named-entity distributions approximate the real corpora closely enough to be useful as shareable research data. The authors stop short of claiming parity: GPT-4o underplays hashtags and negative sentiment, Claude-3.5 and Gemini-2.0 overproduce hashtags, none of the models reproduces YouTube's URL density, and frequently mentioned named entities are preserved worse than rare ones. The paper presents MPTM as an improvement over its earlier per-platform prompting because it keeps cross-platform topic overlap in the generated data, and the overall conclusion is that multi-platform synthetic generation is feasible today, with post-processing likely needed for high fidelity.

Load-bearing premise

The entire prompting method depends on the assumption that clustering posts from all platforms together finds topics that are genuinely shared across platforms, not clusters formed by each platform's typical wording or by topic-modeling artifacts; if that assumption fails, MPTM no longer preserves the cross-platform structure it is designed to protect.

Editorial extensions

If this is right

  • Researchers could release synthetic versions of privacy-restricted multi-platform corpora without exposing original user text, lowering the cost of reproducibility checks.
  • MPTM-style prompting is most effective when a corpus has clearly shared topics, as in the influencer dataset; topically diverse corpora like the election dataset lose more unique topics in generation.
  • Model choice is a fidelity lever: GPT-4o tracks real mention patterns, Claude-3.5 and Gemini-2.0 bias hashtags and sentiment in opposite directions, and no tested model reproduces YouTube's high URL count.
  • Long-form posts from Reddit and YouTube are the easiest to replicate in embedding space, while frequently occurring named entities are the hardest to preserve.
  • No prompting strategy dominates on all metrics, so a post-processing stage will likely be part of any high-fidelity synthetic dataset pipeline.

Reading between the lines

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

  • If the fidelity trend continues, the main bottleneck to shareable synthetic datasets shifts from generation to validation: a cheap automatic gate that scores a synthetic corpus against the real one on these metrics could let researchers publish synthetic versions of datasets they cannot redistribute.
  • The paper fixes one randomly chosen topic cluster per run; varying the number of clusters per prompt or the number of examples per platform would test whether MPTM's benefit comes from topic coherence or simply from more diverse few-shot examples.
  • A natural control experiment, not run in the paper, is to compare MPTM against random cross-platform example selection without clustering; if both perform equally, the shared-topic assumption is not the active ingredient.
  • The named-entity adherence analysis suggests a testable hypothesis for coordinated-account detection: synthetic multi-platform corpora that preserve frequent-entity sharing could serve as safe stand-ins when studying cross-platform information operations.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 investigates whether LLMs can generate synthetic multi-platform social media datasets with lexical and semantic fidelity to real data. It proposes Multi-Platform Topic Model (MPTM) prompting (Algorithm 1, Section 4.1): posts from all platforms are jointly embedded and clustered with BERTopic, a random topic cluster is selected, m posts per platform from that cluster form a sample pool, and few-shot examples from the pool prompt three LLMs (GPT-4o, Gemini 2.0 Flash, Claude 3.5 Haiku) to generate synthetic posts. Two real datasets are used: US 2022 midterm election posts from Twitter, Facebook, and Reddit, and Dutch influencer posts from Instagram, TikTok, and YouTube. Fidelity is evaluated through platform lexical traits, sentiment distributions, topic overlap, embedding similarity, and named-entity bipartite graph statistics, and compared against per-platform prompting from the authors' prior work. The paper concludes that LLM-generated multi-platform synthetic data is 'promising,' that models differ in fidelity, and that post-processing may be needed.

Significance. If the results hold, the paper provides a useful step toward shareable synthetic versions of privacy-restricted multi-platform social media corpora, which is a real need in computational social science. The proposed multi-platform fidelity metrics, especially the named-entity bipartite graph degree-vector adherence in Section 5.5 and Figure 8, are thoughtful and go beyond simple lexical similarity. The empirical scope is broad: six platforms, two collection paradigms, and three LLMs. The authors are also appropriately cautious in their conclusions, explicitly reporting that neither prompting strategy consistently outperformed the other and listing limitations in Section 6. However, the central methodological novelty, MPTM, rests on an unverified assumption that a randomly selected BERTopic cluster constitutes a genuine cross-platform topic, and the manuscript provides no cluster diagnostics. The absence of statistical tests, confidence intervals, and sensitivity analyses further limits the strength of the 'promising' conclusion.

major comments (4)
  1. [Section 4.1, Algorithm 1] The MPTM mechanism's load-bearing assumption is that the randomly selected BERTopic cluster (tr) is a coherent topic shared across platforms. The manuscript reports no diagnostics for this: no distribution of cluster sizes, no share of posts assigned to the HDBSCAN noise cluster (label -1), no per-platform composition of the sample pool SP, no value for the parameter m, and no statement of how many random topic selections were used to produce the reported ~999-post synthetic sets. Without these, SP could be dominated by a single platform's style or by noise, in which case MPTM does not actually preserve cross-platform topic structure. Please provide cluster-size distributions, noise-cluster proportions, per-platform composition of SP for each dataset, the chosen m, and results across multiple random topic selections to demonstrate stability.
  2. [Section 5.3, Figures 2 and 3] The topic-overlap conclusion depends on an 'empirically determined threshold of 0.7' for pairwise cosine similarity between topic vectors, but no sensitivity analysis is reported. Because the central descriptive finding that LLMs replicate influencer topics better than election topics depends directly on which topics count as matched, the paper should report overlap values for a range of thresholds (e.g., 0.5 to 0.9) and specify the number of topics, the matching algorithm, and how the greedy matching handles one-to-many overlaps. As written, the threshold choice is a free parameter that could qualitatively change the conclusions.
  3. [Tables 2, 3, and 7; Section 5.4] All fidelity comparisons are reported as point estimates from what appears to be a single generation run, with no confidence intervals, standard errors, or statistical tests. Statements such as 'differences are too small to matter much' (Section 5.4) and 'GPT-4o matches the mention patterns found in real data' (Section 5.1) are therefore unsupported. At minimum, the authors should report variability across multiple generation runs or bootstrap resamples, and for the embedding-similarity recall counts should provide uncertainty bounds, especially since the absolute average similarities (around 0.2) are low and their practical significance is unclear.
  4. [Section 5.1 and Tables 2, 3, 8, 9] The paper alternates between comparing MPTM-generated data to the 'Original Data' and to the 'Sample Pool,' but it is not always clear which reference is used for each conclusion. Because the Sample Pool is a subset of real posts selected by the topic model, comparing synthetic MPTM output to the Sample Pool is the more appropriate reference; comparing to the Original Data can conflate topic-model selection effects with generation fidelity. Please state explicitly, for every table and figure, which real reference set is used and justify the choice.
minor comments (6)
  1. [Section 4.2] The choice of nine input examples and five generated posts per prompt is described as chosen because it 'provided the maximum amount of context,' but no token counts, pilot results, or ablation are reported; please clarify whether this ratio affects the fidelity measures.
  2. [Figure 4] Figure 4 contains apparent Unicode rendering artifacts in the topic labels (strings such as '/uni00000017' and '/uni0000004f'); the figure should be regenerated with readable text.
  3. [Section 5.3] The sentence 'in the elections dataset, large proportions of topics (30, 27, and 32) are unique in real data but not replicated in synthetic data' does not indicate which LLM each number corresponds to; please label them or add a supporting table.
  4. [Section 6 and Table 3 caption] There are small copyediting issues: 'top of the art' should be 'state of the art' in Section 6, and the Table 3 caption reads 'Negative sentiment content in downplayed' and should be corrected.
  5. [General] No data or code availability statement is included. Given that the paper is motivated by reproducibility, please add a statement about whether the synthetic datasets, prompts, and generation code will be released.
  6. [Tables 2 and 3] The nested table headers make Tables 2 and 3 difficult to read; splitting each into separate tables per dataset or using clear group separators would improve readability.

Circularity Check

0 steps flagged · score 2.0 of 10

No material circularity: the central comparison is measured against external real datasets, and the self-citations are motivational rather than load-bearing.

full rationale

The paper's central claim is an empirical feasibility result: LLM-generated synthetic multi-platform posts are compared, through lexical traits, sentiment, topic overlap, embedding similarity, and named-entity bipartite graphs, against two externally collected real datasets (Aiyappa et al. [3] and Gui et al. [16]). None of the fidelity numbers is defined in terms of a parameter fitted to produce that number; the empirically determined 0.7 threshold for topic matching is an analytical decision rule, not a fitted output, and the synthetic outputs are not fed back into the prompt-selection procedure. MPTM does construct its few-shot sample pool from one BERTopic cluster, and topic overlap is also measured with BERTopic, so the injected topic can reappear in generated data by design; however, the paper does not present that reappearance as an independent prediction, and its own conclusion that neither prompting strategy consistently outperformed the other shows the comparison is not forced. The self-citations ([43], [44], [7]) provide a prior baseline, a prior GPT-3.5-Turbo observation, and a related Instagram augmentation result; these are empirical, externally checkable studies, and the current headline comparisons are recomputed on new models and a new dataset, so the results do not reduce to those citations. The limitations stated in Section 6 are explicit scope restrictions, not circular moves. The derivation chain is self-contained with respect to external data; only minor self-citational framing keeps the score above zero.

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

The central results rest on the validity of the topic model, sentiment classifier, and embedding model as fidelity measurements, and on the representativeness of the sampled real datasets. These are standard tools but they are applied beyond their original domains without validation. No invented entities are introduced.

free parameters (4)
  • Topic overlap threshold = 0.7
    Empirically determined threshold of cosine similarity used to decide whether topics in synthetic and real data match (Section 5.3). No sensitivity analysis is reported, and conclusions about topic overlap depend on this cutoff.
  • Prompt input-output ratio = 9 examples to 5 outputs
    Hand-picked ratio chosen because it provides maximum context without exceeding token limits (Section 4.2). Affects generation and the breadth of examples seen by the LLM.
  • Minimum topic size = 10 posts
    Only topics appearing in at least 10 posts on a platform are considered (Section 5.3). This choice affects topic overlap and distance computations.
  • t-SNE K-means centroid count = 50
    K=50 used to reduce embedding vectors to cluster centroids for t-SNE visualization (Section 5.4). The visualization is qualitative, but centroid count affects the plots.
assumptions (4)
  • domain assumption BERTopic with all-MiniLM-L6-v2 embeddings yields meaningful topic clusters for short, noisy social media posts across six platforms.
    The entire MPTM sample pool construction depends on these topics being valid shared topics. Invoked in Section 4.1 and Algorithm 1.
  • domain assumption The cardiffnlp Twitter sentiment model provides valid sentiment labels for posts from all platforms, including YouTube, TikTok, Facebook, and Instagram.
    Sentiment fidelity comparisons in Section 5.2 assume the classifier transfers beyond Twitter. No platform-specific validation is provided.
  • domain assumption OpenAI text-embedding-3-large cosine similarity is a valid proxy for semantic fidelity of synthetic posts.
    Embedding similarity in Section 5.4 assumes that higher cosine similarity implies higher fidelity, but no direct semantic equivalence measure is used.
  • domain assumption The sampled subsets of the Aiyappa et al. and Gui et al. datasets are representative of their respective full multi-platform corpora.
    The paper works with samples of the real datasets (for example, 999 posts per platform) and treats them as ground truth without modeling sample bias. Section 3.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models." pith.science (2026). https://pith.science/paper/VUVHPEHA

@misc{pith2026250502858,
  author       = {Pith},
  title        = {Pith review of: Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VUVHPEHA}},
  note         = {Machine review of arXiv:2505.02858}
}
read the original abstract

Social media datasets are essential for research on a variety of topics, such as disinformation, influence operations, hate speech detection, or influencer marketing practices. However, access to social media datasets is often constrained due to costs and platform restrictions. Acquiring datasets that span multiple platforms, which is crucial for understanding the digital ecosystem, is particularly challenging. This paper explores the potential of large language models to create lexically and semantically relevant social media datasets across multiple platforms, aiming to match the quality of real data. We propose multi-platform topic-based prompting and employ various language models to generate synthetic data from two real datasets, each consisting of posts from three different social media platforms. We assess the lexical and semantic properties of the synthetic data and compare them with those of the real data. Our empirical findings show that using large language models to generate synthetic multi-platform social media data is promising, different language models perform differently in terms of fidelity, and a post-processing approach might be needed for generating high-fidelity synthetic datasets for research. In addition to the empirical evaluation of three state of the art large language models, our contributions include new fidelity metrics specific to multi-platform social media datasets.

Figures

Figures reproduced from arXiv: 2505.02858 by the authors.

Figure 1
Figure 1. Multi Platform Topic Modeling (MPTM)-based prompting approach for generating and [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Topic Overlap between real and synthetic data generated using MPTM prompting on [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Topic overlap among platforms in per-platform prompting (as proposed by Tari et al.[44]) [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of topics generated by different LLMs using MPTM prompting using dimen [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]
Figure 5
Figure 5. Figure 5: We experimented with different perplexity values and found consistent results; the plots are [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 5
Figure 5. Figure 5: t-SNE visualizations of US and Dutch datasets across different social media platforms. [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]
Figure 6
Figure 6. Figure 6: Bipartite graphs between named entities (white nodes) and posts (grey nodes) in elections [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Bipartite graphs between named entities (white nodes) and posts (in different color nodes [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: LLM adherence to NER distributions across datasets. Each subplot shows how closely [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 32 canonical work pages

  1. [1]

    Magid: An automated pipeline for generating synthetic multi-modal datasets

    Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta, Justin Sun, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, and Saab Mansour. Magid: An automated pipeline for generating synthetic multi-modal datasets. arXiv preprint arXiv:2403.03194 , 2024

    arXiv 2024

  2. [2]

    Using large language models to simulate multiple humans and replicate human subject studies

    Gati V Aher, Rosa I Arriaga, and Adam Tauman Kalai. Using large language models to simulate multiple humans and replicate human subject studies. In International Conference on Machine Learning, pages 337–371. PMLR, 2023

    2023

  3. [3]

    Rachith Aiyappa, Matthew R. DeVerna, Manita Pote, Bao Tran Truong, Wanying Zhao, David Axelrod, Aria Pessianzadeh, Zoher Kachwala, Munjung Kim, Ozgur Can Seckin, Minsuk Kim, Sunny Gandhi, Amrutha Manikonda, Francesco Pierri, Filippo Menczer, and Kai-Cheng Yang. A multi-platform collection of social media posts about the 2022 u.s. midterm elections. Procee...

    work page 2022

  4. [4]

    Out of one, many: Using language models to simulate human samples

    Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate. Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3):337–351, 2023

    2023

  5. [5]

    Multi-modal embeddings for isolating cross-platform coordinated information campaigns on social media

    Fabio Barbero, Sander op den Camp, Kristian van Kuijk, Carlos Soto Garc´ ıa-Delgado, Gerasi- mos Spanakis, and Adriana Iamnitchi. Multi-modal embeddings for isolating cross-platform coordinated information campaigns on social media. In Davide Ceolin, Tommaso Caselli, and Marina Tulin, editors, Disinformation in Open Online Media - 5th Multidisciplinary In...

    work page 2023

  6. [6]

    Tweet- eval: Unified benchmark and comparative evaluation for tweet classification

    Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, and Luis Espinosa-Anke. Tweet- eval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421, 2020

    arXiv 2010

  7. [7]

    InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection

    Thales Bertaglia, Lily Heisig, Rishabh Kaushal, and Iamnitchi Adriana. Instasynth: Opportu- nities and challenges in generating synthetic instagram data with chatgpt for sponsored content detection. arXiv preprint arXiv:2403.15214 , 2024

    work page Pith review arXiv 2024

  8. [8]

    Leveraging llm-generated data for detecting depression symptoms on social media

    Ana-Maria Bucur. Leveraging llm-generated data for detecting depression symptoms on social media. In International Conference of the Cross-Language Evaluation Forum for European Languages, pages 193–204. Springer, 2024

    work page 2024

Show all 54 references
  1. [9]

    Auggpt: Leveraging chatgpt for text data augmentation

    Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv preprint arXiv:2302.13007 , 2023

    2023 arXiv

  2. [10]

    Offlandat: A community based implicit offensive language dataset generated by large language model through prompt engineering

    Amit Das, Mostafa Rahgouy, Dongji Feng, Zheng Zhang, Tathagata Bhattacharya, Nilanjana Raychawdhary, Mary Sandage, Lauramarie Pope, Gerry Dozier, and Cheryl Seals. Offlandat: A community based implicit offensive language dataset generated by large language model through prompt...

    2024 arXiv

  3. [11]

    Identify- ing citizen-related issues from social media using llm-based data augmentation

    Vitor Gaboardi dos Santos, Guto Leoni Santos, Theo Lynn, and Boualem Benatallah. Identify- ing citizen-related issues from social media using llm-based data augmentation. InInternational Conference on Advanced Information Systems Engineering , pages 531–546. Springer, 2024. 23

    2024

  4. [12]

    emojinal intelligence

    Michael Dunn and Kenneth Hopkinson. How good is gpt’s “emojinal intelligence”? investi- gating emoji patterns in llm-generated social media text. In Proceedings of the International Conference on AI Research. Academic Conferences and publishing limited, 2024

    2024

  5. [13]

    Socially aware synthetic data generation for suicidal ideation detection using large language models

    Hamideh Ghanadian, Isar Nejadgholi, and Hussein Al Osman. Socially aware synthetic data generation for suicidal ideation detection using large language models. IEEE Access, 2024

    2024

  6. [14]

    Bertopic: Neural topic modeling with a class-based tf-idf procedure

    Maarten Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 , 2022

    2022 arXiv

  7. [15]

    Ai and the transformation of social science research

    Igor Grossmann, Matthew Feinberg, Dawn C Parker, Nicholas A Christakis, Philip E Tetlock, and William A Cunningham. Ai and the transformation of social science research. Science, 380(6650):1108–1109, 2023

    2023

  8. [16]

    Across platforms and languages: Dutch influencers and legal disclosures on instagram, youtube and tiktok

    Haoyang Gui, Thales Bertaglia, Catalina Goanta, Sybe de Vries, and Gerasimos Spanakis. Across platforms and languages: Dutch influencers and legal disclosures on instagram, youtube and tiktok. In International Conference on Advances in Social Networks Analysis and Mining , pag...

    2024

  9. [17]

    Evaluating large language models in generating synthetic hci research data: a case study

    Perttu H¨ am¨ al¨ ainen, Mikke Tavast, and Anton Kunnari. Evaluating large language models in generating synthetic hci research data: a case study. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems , pages 1–19, 2023

    2023

  10. [18]

    Happenstance: utilizing semantic search to track russian state media narratives about the russo-ukrainian war on reddit

    Hans WA Hanley, Deepak Kumar, and Zakir Durumeric. Happenstance: utilizing semantic search to track russian state media narratives about the russo-ukrainian war on reddit. In Proceedings of the international AAAI conference on web and social media , volume 17, pages 327–338, 2023

    2023

  11. [19]

    Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

    Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection. arXiv preprint arXiv:2203.09509 , 2022

    2022 arXiv

  12. [20]

    Using twitter data to understand public perceptions of approved versus off-label use for covid-19-related medications

    Yining Hua, Hang Jiang, Shixu Lin, Jie Yang, Joseph M Plasek, David W Bates, and Li Zhou. Using twitter data to understand public perceptions of approved versus off-label use for covid-19-related medications. Journal of the American Medical Informatics Associa- tion, 29(10):16...

    2022

  13. [21]

    Cross-cultural inspiration detec- tion and analysis in real and llm-generated social media data.arXiv preprint arXiv:2404.12933, 2024

    Oana Ignat, Gayathri Ganesh Lakshmy, and Rada Mihalcea. Cross-cultural inspiration detec- tion and analysis in real and llm-generated social media data.arXiv preprint arXiv:2404.12933, 2024

    2024 arXiv

  14. [22]

    Employing large language models in survey research

    Bernard J Jansen, Soon-gyo Jung, and Joni Salminen. Employing large language models in survey research. Natural Language Processing Journal, 4:100020, 2023

    2023

  15. [23]

    Exploiting asymmetry for synthetic training data generation: Synthie and the case of information extraction

    Martin Josifoski, Marija Sakota, Maxime Peyrard, and Robert West. Exploiting asymmetry for synthetic training data generation: Synthie and the case of information extraction. arXiv preprint arXiv:2303.04132, 2023

    2023 arXiv

  16. [24]

    Synthetic vs

    Arefeh Kazemi, Sri Balaaji Natarajan Kalaivendan, Joachim Wagner, Hamza Qadeer, and Brian Davis. Synthetic vs. gold: The role of llm-generated labels and data in cyberbullying detection. arXiv preprint arXiv:2502.15860 , 2025. 24

    2025 arXiv

  17. [25]

    David M. J. Lazer, Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Con- tractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, and Claudia Wagner. Com- putational ...

    2020

  18. [26]

    Data augmentation approaches in natural language processing: A survey

    Bohan Li, Yutai Hou, and Wanxiang Che. Data augmentation approaches in natural language processing: A survey. Ai Open, 3:71–90, 2022

    2022

  19. [27]

    Synthetic data generation with large language models for text classification: Potential and limitations

    Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, and Ming Yin. Synthetic data generation with large language models for text classification: Potential and limitations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages 10443–10461, 2023

    2023

  20. [28]

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

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, 55(9):1–35, 2023

    2023

  21. [29]

    Roberta: A robustly optimized bert pretraining approach

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692, 2019

    1907 arXiv

  22. [30]

    TimeLMs: Diachronic language models from Twitter

    Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho- collados. TimeLMs: Diachronic language models from Twitter. In Valerio Basile, Zornitsa Kozareva, and Sanja Stajner, editors, Proceedings of the 60th Annual Meeting of the Associa- tion ...

    2022

  23. [31]

    Coordinating a multi-platform disinformation campaign: Internet research agency activity on three u.s

    Josephine Lukito. Coordinating a multi-platform disinformation campaign: Internet research agency activity on three u.s. social media platforms, 2015 to 2017. Political Communication, 37(2):238–255, 2020

    2015

  24. [32]

    Ministerie van Onderwijs, Cultuur en Wetenschap. Wet van 23 februari 2022, houdende wi- jziging van wetten op met name het terrein van onderwijs, cultuur en media in verband met hoofdzakelijk wetstechnische en redactionele verbeteringen (verzamelwet ocw 2022). https:// zoek.of...

    2022

  25. [33]

    Is a prompt and a few samples all you need? using gpt-4 for data augmentation in low-resource classification tasks

    Anders Giovanni Møller, Jacob Aarup Dalsgaard, Arianna Pera, and Luca Maria Aiello. Is a prompt and a few samples all you need? using gpt-4 for data augmentation in low-resource classification tasks. arXiv preprint arXiv:2304.13861 , 2023

    2023 arXiv

  26. [34]

    Text and code embeddings by contrastive pre-training

    Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al. Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005 , 2022

    2022 arXiv

  27. [35]

    Multi-platform information operations: Twitter, facebook and youtube against the white helmets

    Kin Wai Ng, Sameera Horawalavithana, and Adriana Iamnitchi. Multi-platform information operations: Twitter, facebook and youtube against the white helmets. In Oana Balalau, Katherine Ognyanova, and Daniel M. Romero, editors, Workshop Proceedings of the 15th International AAAI ...

    2021

  28. [36]

    Eq-bench: An emotional intelligence benchmark for large language models

    Samuel J Paech. Eq-bench: An emotional intelligence benchmark for large language models. arXiv preprint arXiv:2312.06281 , 2023. 25

    2023 arXiv

  29. [37]

    Enhancing discourse parsing for local structures from social media with llm-generated data

    Martial Pastor, Nelleke Oostdijk, Patricia Martin-Rodilla, and Javier Parapar. Enhancing discourse parsing for local structures from social media with llm-generated data. InProceedings of the 31st International Conference on Computational Linguistics , pages 8739–8748, 2025

    2025

  30. [38]

    Ita-election-2022: A multi-platform dataset of social media conversations around the 2022 italian general election

    Francesco Pierri, Geng Liu, and Stefano Ceri. Ita-election-2022: A multi-platform dataset of social media conversations around the 2022 italian general election. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , CIKM ’23, page 53...

    2022

  31. [39]

    Llm-based synthetic datasets: Applications and limitations in toxicity detection

    Maximilian Schmidhuber and Udo Kruschwitz. Llm-based synthetic datasets: Applications and limitations in toxicity detection. LREC-COLING, 37:2024, 2024

    2024

  32. [40]

    Computational repro- ducibility in computational social science

    David Schoch, Chung-hong Chan, Claudia Wagner, and Arnim Bleier. Computational repro- ducibility in computational social science. EPJ Data Science , 13(1):75, 2024

    2024

  33. [41]

    Improving neural machine translation models with monolingual data

    Rico Sennrich, Barry Haddow, and Alexandra Birch. Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709 , 2015

    2015 arXiv

  34. [42]

    The rise of germany’s afd: A social media analysis

    Juan Carlos Medina Serrano, Morteza Shahrezaye, Orestis Papakyriakopoulos, and Simon Hegelich. The rise of germany’s afd: A social media analysis. In Proceedings of the 10th International Conference on Social Media and Society , SMSociety ’19, page 214–223, New York, NY, USA, ...

    2019

  35. [43]

    Leveraging gpt for the generation of multi-platform social media datasets for research

    Henry Tari, Danial Khan, Justus Rutten, Darian Othman, Rishabh Kaushal, Thales Bertaglia, and Adriana Iamnitchi. Leveraging gpt for the generation of multi-platform social media datasets for research. arXiv preprint arXiv:2407.08323 , 2024

    2024 arXiv

  36. [44]

    Leveraging gpt for the generation of multi-platform so- cial media datasets for research

    Henry Tari, M Danial Khan, Justus Rutten, Darian Othman, Thales Bertaglia, Rishabh Kaushal, and Adriana Iamnitchi. Leveraging gpt for the generation of multi-platform so- cial media datasets for research. In Proceedings of the 35th ACM Conference on Hypertext and Social Media,...

    2024

  37. [45]

    Simulating social media using large language models to evaluate alternative news feed algorithms.arXiv preprint arXiv:2310.05984, 2023

    Petter T¨ ornberg, Diliara Valeeva, Justus Uitermark, and Christopher Bail. Simulating social media using large language models to evaluate alternative news feed algorithms.arXiv preprint arXiv:2310.05984, 2023

    2023 arXiv

  38. [46]

    Big questions for social media big data: Representativeness, validity and other methodological pitfalls

    Zeynep Tufekci. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the International AAAI Conference on Web and Social Media, 8(1):505–514, May 2014

    2014

  39. [47]

    Zeroshotdataaug: Generating and augmenting training data with chatgpt

    Solomon Ubani, Suleyman Olcay Polat, and Rodney Nielsen. Zeroshotdataaug: Generating and augmenting training data with chatgpt. arXiv preprint arXiv:2304.14334 , 2023

    2023 arXiv

  40. [48]

    A multi-platform dataset for detect- ing cyberbullying in social media

    David Van Bruwaene, Qianjia Huang, and Diana Inkpen. A multi-platform dataset for detect- ing cyberbullying in social media. Lang. Resour. Eval., 54(4):851–874, dec 2020

    2020

  41. [49]

    Happiness and sadness in adolescents’ instagram direct messaging: A neural topic modeling approach

    Tim Verbeij, Ine Beyens, Damian Trilling, and Patti M Valkenburg. Happiness and sadness in adolescents’ instagram direct messaging: A neural topic modeling approach. Social Media+ Society, 10(1):20563051241229655, 2024

    2024

  42. [50]

    Generating faithful synthetic data with large language models: A case study in computational social science

    Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora, Martin Josifoski, Ashton Anderson, and Robert West. Generating faithful synthetic data with large language models: A case study in computational social science. arXiv preprint arXiv:2305.15041 , 2023. 26

    2023 arXiv

  43. [51]

    The power of llm-generated synthetic data for stance detection in online political discussions

    Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, and Stefan Harmeling. The power of llm-generated synthetic data for stance detection in online political discussions. arXiv preprint arXiv:2406.12480, 2024

    2024 arXiv

  44. [52]

    Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

    Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems , 33:5776–5788, 2020

    2020

  45. [53]

    Eda: Easy data augmentation techniques for boosting performance on text classification tasks

    Jason Wei and Kai Zou. Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 , 2019

    1901 arXiv

  46. [54]

    Cross-platform information operations: Mobilizing narratives & building resilience through both ’big’ & ’alt’ tech

    Tom Wilson and Kate Starbird. Cross-platform information operations: Mobilizing narratives & building resilience through both ’big’ & ’alt’ tech. Proc. ACM Hum.-Comput. Interact. , 5(CSCW2), oct 2021. 27

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.