Quantifying Political Partisanship for Cross-Platform Analyses

Christopher G. Healey; Fathima Ameen

REVIEW 3 major objections 4 minor 203 references

Quantifying Political Partisanship for Cross-Platform Analyses

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read A single text-based pipeline can measure political partisanship on different social media platforms using only post text and external media-bias ratings.

desk verdict A clean, worthwhile extension of Waller & Anderson to cross-platform text, but the validation is circular and the seed pairs mix topics, so the portability claim doesn't hold as written. read the letter →

arxiv 2607.21842 v1 pith:ZO7DSQB6 submitted 2026-07-23 cs.SI cs.LG

Fathima Ameen , Christopher G. Healey This is my paper

classification cs.SIcs.LG

keywords politicalpartisanshipsocialmediacross-platformanalysisembeddingspacebiasratingsBlueskyTruthdimension

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 tries to establish that political partisanship in social media posts can be measured with one portable, text-only pipeline, so scores mean the same thing on different platforms. The method embeds posts into a semantic space, groups them into topics, labels clusters using media-bias ratings of cited news sources, and builds a partisanship axis from opposing clusters. Posts are then scored by projecting onto that axis. The paper shows the scores correlate with held-out media-bias ratings on the platforms used to build the axis and on an unseen Twitter corpus, and that the scores track event-driven shifts in sentiment, such as Democrats' fading support for Biden on Bluesky. If true, this gives researchers a general tool for comparing polarization, echo chambers, and radicalization across the fragmented social media ecosystem.

What carries the argument

The load-bearing object is the partisanship axis: a direction in the embedding space defined as the average difference between centroid pairs of semantically similar but ideologically opposite topic clusters. It is built from clusters labeled left or right via Empirical-Bayes-shrunk media-bias scores of cited news sources, and each post receives a partisanship score by projecting its normalized embedding onto the axis. The axis is what makes scores comparable across platforms, because it is fit once on text alone and then applied to any post.

What would settle it

Take the fixed partisanship axis and apply it to posts from a platform or community whose dominant topics differ from those in the seed clusters (for example, local news or non-political content that still cites rated outlets); if the correlation between projected scores and media-bias ratings disappears or reverses, the axis is measuring topic separation rather than general partisanship.

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Extended reading notes

Core claim

The central claim is that a partisanship score can be defined purely by text, anchored by external media-bias ratings, and transferred across platforms. After embedding posts and clustering them into topics, the method labels each cluster by the aggregated bias scores of news outlets cited within it. It then selects pairs of left- and right-labeled clusters whose centroids are most similar—reasoning that such pairs differ mainly in political framing, not topic—and forms the partisanship axis as the mean difference between their centroids. Every post's score is the projection of its embedding onto that axis. Validation shows the axis correlates with held-out media-bias scores both within the

Load-bearing premise

The axis assumes that pairs of topic clusters whose embeddings are similar differ mainly in political ideology rather than in subject matter, style, or platform dialect; if that assumption fails, the axis measures something other than partisanship.

Editorial extensions

If this is right

Researchers can compare partisanship distributions across platforms sharing the same axis, making cross-platform polarization studies feasible without re-training classifiers.
The method works on any text-based platform where users share rated news outlets at non-trivial rates, lowering barriers for studying fringe alt-tech platforms.
Because scores are continuous and platform-agnostic, they enable temporal tracking of partisan sentiment toward candidates and issues within a single platform (for example, the observed leftward shift for Harris and rightward drift for Biden on Bluesky).
The validation approach—checking projected scores against independent media-bias ratings—offers a general template for trusting embedding-based political dimensions.

Reading between the lines

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

The method's correlation on X (r≈0.19) is weaker than in-distribution; a fair reading suggests the axis transfers direction but the magnitude of scores may be platform-specific, so cross-platform score distributions may need calibration even if rank order is meaningful.
A natural testable extension is to apply the same pipeline to multi-party or non-U.S. contexts using local bias-rating tools; the paper's own limitation discussion suggests the axis assumption is U.S.-centric, and the cluster-pair reasoning may need topic-matching refinements to avoid conflating partisanship with topic differences.
If the method is applied to platforms with different language conventions (for example, non-English), both the transformer encoder and the bias-anchoring signal would need to be language-matched; the paper's claims are scoped to English-language political discourse.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Dear colleague,

The short version: this is a well-written, clearly described extension of Waller & Anderson's social-dimension idea from subreddit embeddings to transformer text embeddings, applied to Bluesky and Truth Social. The reported validation, though, does not support the portability claim as stated, because the AllSides signal does triple duty (cluster labeling, seed-pair selection, and validation target), and the seed pairs are not topic-matched.

What's genuinely new: the pipeline is text-only and platform-agnostic in principle; it provides the first joint embedding of Bluesky and Truth Social with a common partisanship axis; and the out-of-distribution transfer test to X is a good idea even if the result is weak. The sensitivity analysis over all 630 cluster pairs is the right kind of check and shows the chosen axis is not arbitrary.

Where it gets soft. First, the circularity: clusters are labeled by AllSides scores of cited sources, seed pairs are chosen as high-similarity left/right clusters, and validation correlates post projections with those same AllSides scores. That alone would be a moderate concern, but it's compounded by selecting the number of seed pairs (4) after testing 1–9 and picking the 'most robust output.' That is validation-based selection, so the in-distribution r=0.365 is optimistic.

Second, and more serious, the stress-test note is right: Table 3's second seed pair is Pro-Palestine vs. Immigration & border security outrage, and the fourth is Trump controversies vs. Conspiracy theories. Those are different topics, not the same topic framed from opposite sides. The axis is then a sum of vector differences that include topic differences. Since left- and right-leaning outlets systematically cover different topics, a positive correlation with AllSides scores can arise from topic separation alone. The X transfer (r=0.193) is consistent with topic differences that carry across platforms. The paper asserts topic coherence, but its own qualitative summaries contradict it.

Also, the abstract promises a 'cross-platform comparison of partisanship distributions,' but the results section has no distributional comparison—just mean scores by keyword and month. That is a mismatch between claim and content.

On the plus side, the method is described in enough detail to reproduce, the data work is substantial, and the limitations section is honest about U.S.-centricity and clustering sensitivity. The authors don't oversell the X correlation.

Who this is for: computational social scientists working on partisanship measurement, and anyone teaching validation pitfalls. It's a good paper to assign in a methods seminar to discuss circularity.

My recommendation: send it to peer review, but with a request for major revision. The load-bearing evidence needs independent labels (e.g., human-annotated post stance), topic-matched seed-pair construction or a pre-registered seed-pair count, and an actual distributional comparison. Without those, the portability claim is not established.

Best,

Referee Report

3 major / 4 minor

Summary. The paper proposes a text-based, platform-portable method for measuring political partisanship in social media posts. Posts are embedded with a transformer encoder, clustered into topic groups, labeled using aggregated AllSides media-bias scores of cited news outlets, and a partisanship axis is constructed as the difference between centroids of left- and right-labeled cluster pairs. Individual posts are scored by projection onto this axis. The method is applied to ~1.3M posts from Bluesky and Truth Social from May–October 2024. The authors report significant correlations between projected scores and AllSides bias scores on held-out posts from the same platforms (r=0.365) and on an independent X/Twitter corpus (r=0.193), and they present within-platform temporal patterns as evidence that the axis captures partisanship beyond platform identity.

Significance. If the method and validation were sound, this would be a valuable contribution: a text-only pipeline that could measure partisanship comparably across platforms without platform-specific features would enable cross-platform polarization research in the fragmented alt-tech ecosystem. The application to Bluesky and Truth Social is timely, and the detailed pipeline is clearly described. However, the current evidence for the central claim is substantially weakened by circularity in the validation design and by a topic confound in the seed-pair construction. The contribution therefore remains promising but not yet established by the manuscript as written.

major comments (3)

[3 (Cluster Labeling; Social Dimension Construction) and 4 (Results)] The validation is circular in two ways. First, the same AllSides ratings are used to label clusters, to define the left/right pools for seed selection, and as the outcome variable in both validation correlations. A post's AllSides score influences the axis indirectly through the cluster labels, so the reported 'held-out' correlation is not an independent check. Second, the number of seed pairs (4) was chosen after testing 1–9 and selecting the 'most robust output,' which is model selection on the validation metric. Consequently the reported r=0.365 and the 100th-percentile result in Figure 3 are optimistically biased. The authors should prespecify the number of pairs or use a nested validation procedure, and validate against an external partisan signal that does not feed into clustering/labeling.
[3 (Social Dimension Construction) and Table 3] The central assumption behind the axis is that high centroid similarity pairs differ primarily in partisanship rather than content. Table 3 contradicts this: pair 2 is 'Pro-Palestine, Biden-Israel criticism' versus 'Immigration & border security outrage'; pair 4 is 'Trump controversies and criticism' versus 'Conspiracy theories & Extremism.' The resulting axis is a linear combination of topic differences, not a pure partisan dimension. Because left- and right-leaning outlets systematically cover different topics, the positive validation correlations can arise from topic separation alone. The X-transfer correlation (r=0.193) is weak and does not rule out this alternative. The authors should require seed pairs to be topically matched (e.g., same issue discussed from opposing stances) or otherwise control for topic in validation.
[4 (Sensitivity to Seed Pair Selection)] The exhaustive-pair baseline does not address the topic confound. Many arbitrary cluster pairs may yield positive correlations simply because they separate news sources by topic. Showing that the chosen axis outperforms the majority of arbitrary pairs is consistent with the axis capturing a topic dimension that happens to align with AllSides bias. A more informative baseline would match seed pairs on topic or hold topic fixed. Additionally, because the number of pairs was selected using the same validation data, the percentile comparison is not an honest null distribution.

minor comments (4)

[2 (Literature Review)] Typo: 'mainstream platformsm' should be 'mainstream platforms.'
[4 (Figure 3)] The caption says 'Each bar represents the number of cluster pairs' but does not specify the binning for this histogram. If the selected axis is marked, clarify how its location is indicated.
[3 (Validation)] The term 'held-out' is misleading for the in-distribution validation: posts in the seed clusters are excluded, but their AllSides scores were used to label clusters. Consider using 'excluded from axis construction' or similar.
[3 (Cluster Labeling)] Equation for Empirical Bayes shrinkage: define σ̂²_c precisely; it appears to be the variance of bias scores across posts cited within cluster c, but this is not stated in the text.

Circularity Check

3 steps flagged · score 6.0 of 10

The AllSides signal used to build the axis is also used as the validation ground truth, and the seed-pair count is selected on the same metric; the in-distribution validation is therefore partly circular, though the X transfer test provides some independent support.

self definitional [Section 3, Cluster Labeling using News Domains; Section 3, Validation]
"We validate the partisanship axis in two ways, both of which exploit the fact that posts citing news sources carry an independent ground-truth signal—the AllSides bias score of the cited outlet—against which our projection-based partisanship scores can be compared."

The 'independent ground-truth signal' is the same AllSides score used to label clusters ('Clusters are then labeled right-leaning if θc ≥ 0.5, left-leaning if θc ≤ −0.5') and to select the seed pairs ('we select the top 4 seed pairs based on cosine similarity' among left/right clusters). The axis d is the mean of differences between centroids of AllSides-labeled clusters, so projected post scores are a direct function of AllSides labels. The in-distribution validation therefore compares the method's output to its own labeling input; a positive correlation is an internal consistency check, not an independent test that the axis measures partisanship.
fitted input called prediction [Section 3, Social Dimension Construction]
"We chose four pairs after testing between one and nine seed pairs. Our analysis showed four pairs produced the most robust output."

The number of seed pairs is a hyperparameter selected by testing candidate settings and picking the one with the 'most robust output'—in context, the validation correlation with AllSides scores. The reported in-distribution correlation (r = 0.365) is then presented as validation evidence, but the chosen configuration was selected on that same metric. The 'prediction' is partly a fitted value; the paper does not report whether the selected k=4 is held out from a separate model-selection procedure.

1 more flagged steps

other [Section 3, Social Dimension Construction; Table 3]
"Pairs with high centroid similarity are presumed to differ primarily along the partisan dimension rather than in content, making them ideal seed pairs. ... Table 3: 'Pro-Palestine, Biden-Israel criticism' vs 'Immigration & border security outrage'"

The axis is defined as the difference between centroids of such seed pairs, so the partisanship score is, by construction, a linear contrast between those cluster topics. The paper's own Table 3 shows that the second pair contrasts Pro-Palestine discourse with immigration discourse—different topics, not the same topic framed from opposing partisan perspectives. Since left- and right-leaning news outlets systematically emphasize different issues, the AllSides validation correlation can arise from topic separation alone, without the axis measuring general partisanship. The validation cannot distinguish topic-contrast from partisan valence, so the central portability claim is not independently established by the in-distribution correlation.

full rationale

The derivation chain is not fully circular: the axis is built from cluster centroids, the in-distribution evaluation uses held-out posts not in the seed clusters, and the out-of-distribution X test is genuinely unseen during axis construction. The exhaustive sensitivity analysis also shows that most arbitrary cluster-pair axes do not correlate, so the selected axis carries real information. However, the AllSides signal enters both as the labeling/seed-selection input and as the validation outcome. The in-distribution correlation therefore measures how well the axis reproduces the same external label source it was built from; it is a consistency check rather than an independent confirmation. The seed-pair count (k=4) was selected by testing 1-9 and choosing the 'most robust output,' which is effectively tuning on the validation metric. Finally, the assumption that high-similarity left/right cluster pairs differ 'primarily along the partisan dimension rather than in content' is contradicted by Table 3's own topic summaries (e.g., Pro-Palestine vs. immigration), so the axis may encode topic differences that correlate with AllSides because left- and right-leaning outlets cover different topics. The X transfer result (r=0.193) provides some independent support, but it is weak and could also reflect cross-platform topic differences. Overall, the central in-distribution validation is partially circular and the method's portability claim is not as strongly established as presented. Score 6 reflects partial circularity, not full equivalence.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central method rests on AllSides as ground truth, on embedding-geometry assumptions, and on a linear axis fitted through the same external label source. No independent human-annotated labels or external benchmarks are used. The free parameters (seed-pair count, k values, thresholds, PCA cutoff) are methodologically consequential and several are chosen by inspecting the output.

free parameters (5)

Number of seed pairs = 4
Authors tested 1-9 seed pairs and chose 4 based on 'most robust output,' which is a validation-based selection that inflates the reported correlation.
MiniBatch k-means k = 15 (dedup); 36 (topic clustering)
k=15 is used for per-platform-month semantic deduplication; k=36 is chosen by elbow/kneed. Both determine which clusters become seed candidates and shape the axis.
Near-duplicate cosine threshold = 0.90
Arbitrary threshold that removes posts closer to the cluster centroid when pairwise similarity exceeds 0.90; directly affects which posts remain.
PCA variance retention = 95% (104 components)
Component count changes the geometry of the embedding space and therefore the clusters and centroids used to build the axis.
Cluster label thresholds = ±0.5
Clusters with shrunken mean bias ≥0.5 are labeled right, ≤-0.5 left, else neutral/no signal; no external justification for these cutoffs.

assumptions (5)

domain assumption AllSides media-bias ratings are a valid external ground truth for partisanship of news domains.
Cluster labels, seed-pair selection, and all validation correlations depend on AllSides scores; the paper does not independently validate this rating source.
domain assumption Qwen3 embedding cosine similarity aligns with political ideology and topic structure across platforms.
The whole pipeline assumes that semantically similar posts (and centroids) in this embedding space are informative about partisanship, not just about platform or style.
ad hoc to paper High centroid-similarity cluster pairs differ primarily in partisanship, not in topic.
Explicitly stated in Section 3 as the seed-pair selection premise; if false, the axis captures topic or style rather than partisanship.
domain assumption Posts without news citations can be scored using an axis built from clusters labeled by news-citing posts.
The method applies the axis to all posts, but the labels and centroid differences come only from posts that cite AllSides-rated domains; generalizing to non-citing posts is assumed.
domain assumption Keyword/hashtag-filtered posts are representative of each platform's political discourse.
Truth Social was collected via trending hashtags and political keywords, while Bluesky used a keyword search; different sampling strategies may confound cross-platform comparisons.

invented entities (1)

Partisanship axis d
purpose: A latent linear dimension in embedding space used to score all posts by projection; defined as the mean of centroid differences of left/right seed clusters.
The axis is constructed using AllSides-labeled clusters and validated against the same AllSides signal, so it does not have a falsifiable handle outside the paper's correlation framework.

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Cite this review

Pith. "Pith review of Quantifying Political Partisanship for Cross-Platform Analyses." pith.science (2026). https://pith.science/paper/ZO7DSQB6

@misc{pith2026260721842,
  author       = {Pith},
  title        = {Pith review of: Quantifying Political Partisanship for Cross-Platform Analyses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZO7DSQB6}},
  note         = {Machine review of arXiv:2607.21842}
}

read the original abstract

Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content. However, existing approaches are typically tailored to platform-specific properties, such as structural affordances or linguistic conventions, which hurts generalizability across platforms. This limitation is increasingly consequential as the social media ecosystem fragments and fringe, alt-tech platforms emerge alongside mainstream ones. We propose a text-based, platform-portable methodology for measuring political partisanship in social media posts, anchored by an external news-credibility signal. Posts are embedded using a transformer-based sentence encoder and clustered into topic groups, which are labeled using the aggregated AllSides media bias scores of cited news outlets. A partisanship axis is then constructed in the embedding space as the difference between centroids of oppositely labeled clusters, and individual posts are scored by projection onto this axis. We apply the method to a corpus of approximately 1.3 million posts collected from Bluesky and Truth Social during the six months preceding the 2024 U.S. presidential election, providing the first cross-platform comparison of partisanship distributions on these two ideologically asymmetric platforms. The resulting partisanship scores correlate significantly with held-out AllSides media bias scores both in-distribution and out-of-distribution on an independent Twitter corpus, and recover within-platform partisan dynamics that platform identity alone cannot explain.

Figures

Figures reproduced from arXiv: 2607.21842 by the authors.

**Figure 2.** Monthly share of total posts and average engage [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Sensitivity of the partisan axis to seed pair selec [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: (a) Mean partisanship score (zp) of posts containing each keyword, stratified by platform and month. Negative scores (blue) indicate left-leaning partisan alignment; positive scores (red) indicate right-leaning alignment. Keywords are grouped into candidate names (top)…

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Works this paper leans on

203 extracted references · 74 canonical work pages

[1]

Proceedings of the first instructional conference on machine learning , volume=

Using tf-idf to determine word relevance in document queries , author=. Proceedings of the first instructional conference on machine learning , volume=. 2003 , organization=

2003
[2]

Advances in Neural Information Processing Systems , volume=

Matryoshka representation learning , author=. Advances in Neural Information Processing Systems , volume=
[3]

arXiv preprint arXiv:2410.21276 , year=

Gpt-4o system card , author=. arXiv preprint arXiv:2410.21276 , year=

arXiv
[4]

Proceedings of the Second Workshop on NLP and Computational Social Science , pages=

community2vec: Vector representations of online communities encode semantic relationships , author=. Proceedings of the Second Workshop on NLP and Computational Social Science , pages=
[5]

Vaccines , volume=

\# Scamdemic,\# Plandemic, or\# Scaredemic: what Parler social media platform tells us about COVID-19 vaccine , author=. Vaccines , volume=. 2021 , publisher=

2021
[6]

Proceedings of the ACM on Human-Computer Interaction , volume=

Content Moderation and Hate Speech on Alternative Platforms: A Case Study of BitChute , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2025 , publisher=

2025
[7]

2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) , pages=

Inside the right-leaning echo chambers: Characterizing gab, an unmoderated social system , author=. 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) , pages=. 2018 , organization=

2018
[8]

Addawood, Aseel and Bashir, Masooda , year =. ". Proceedings of the. doi:10.18653/v1/W16-2801 , abstract =

work page doi:10.18653/v1/w16-2801

Show all 203 references

[9]

Annotation and

Schaefer, Robin and Stede, Manfred , file =. Annotation and
[10]

it - Information Technology , author =

Argument. it - Information Technology , author =. 2021 , pages =. doi:10.1515/itit-2020-0053 , abstract =

2021 doi
[11]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Online. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , pages =. doi:10.1609/icwsm.v18i1.31383 , abstract =

2024 doi
[12]

Investigating

Zhang, Bowen and Fu, Xianghua and Ding, Daijun and Huang, Hu and Dai, Genan and Yin, Nan and Li, Yangyang and Jing, Liwen , month = oct, year =. Investigating. doi:10.48550/arXiv.2304.03087 , abstract =

doi arXiv
[13]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Stance. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , pages =. doi:10.1609/icwsm.v18i1.31360 , abstract =

2024 doi
[14]

IEEE Transactions on Computational Social Systems , author =

Improved. IEEE Transactions on Computational Social Systems , author =. 2023 , pages =. doi:10.1109/TCSS.2023.3320723 , language =

2023
[15]

Yuan, Jiaqing and Xi, Ruijie and Singh, Munindar P , file =. A
[16]

Information Processing & Management , author =

Is my stance the same as your stance?. Information Processing & Management , author =. 2022 , pages =. doi:10.1016/j.ipm.2022.103070 , abstract =

2022
[17]

2024 , pages =

Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , pages =. doi:10.1609/icwsm.v18i1.31424 , abstract =

2024 doi
[18]

Li, Hao and Schlegel, Viktor and Sun, Yizheng and Batista-Navarro, Riza and Nenadic, Goran , month = nov, year =. Large. doi:10.48550/arXiv.2506.16383 , abstract =

doi
[19]

Exploring the

Chen, Guizhen and Cheng, Liying and Luu, Anh Tuan and Bing, Lidong , year =. Exploring the. Proceedings of the 62nd. doi:10.18653/v1/2024.acl-long.126 , language =

2024 doi
[20]

ACM Transactions on Intelligent Systems and Technology , author =

Prompting and. ACM Transactions on Intelligent Systems and Technology , author =. 2025 , pages =. doi:10.1145/3725816 , abstract =

2025 doi
[21]

Reasoner

Yuan, Jiaqing and Xi, Ruijie and Singh, Munindar P , month = sep, year =. Reasoner. Proceedings of the 36th. doi:10.1145/3720553.3746668 , abstract =
[22]

How would

Zhang, Bowen and Ding, Daijun and Jing, Liwen and Dai, Genan and Yin, Nan , month = aug, year =. How would. doi:10.48550/arXiv.2212.14548 , abstract =

doi arXiv
[23]

Körner, Erik and Wiedemann, Gregor and Hakimi, Ahmad Dawar and Heyer, Gerhard and Potthast, Martin , year =. On. Proceedings of the 2021. doi:10.18653/v1/2021.emnlp-main.795 , abstract =

2021 doi
[24]

Neural Computing and Applications , author =

A systematic review of machine learning techniques for stance detection and its applications , volume =. Neural Computing and Applications , author =. 2023 , pages =. doi:10.1007/s00521-023-08285-7 , abstract =

2023 doi
[25]

Zhang, Bowen and Dai, Genan and Niu, Fuqiang and Yin, Nan and Fan, Xiaomao and Wang, Senzhang and Cao, Xiaochun and Huang, Hu , month = nov, year =. A. doi:10.48550/arXiv.2409.15690 , abstract =

doi arXiv
[26]

Aker, Ahmet and Derczynski, Leon and Bontcheva, Kalina , month = sep, year =. Simple. doi:10.48550/arXiv.1708.05286 , abstract =

doi arXiv
[27]

Predicting

Poddar, Lahari and Hsu, Wynne and Lee, Mong Li and Subramaniyam, Shruti , month = nov, year =. Predicting. 2018. doi:10.1109/ICTAI.2018.00021 , abstract =

2018
[28]

Liang, Bin and Chen, Zixiao and Gui, Lin and He, Yulan and Yang, Min and Xu, Ruifeng , month = apr, year =. Zero-. Proceedings of the. doi:10.1145/3485447.3511994 , abstract =
[29]

2024 , pages =

Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , pages =. doi:10.1609/icwsm.v18i1.31342 , abstract =

2024 doi
[30]

Examining the

Ng, Lynnette Hui Xian and Cruickshank, Iain J and Lee, Roy , file =. Examining the
[31]

Integrated or

Xia, Yan and Monti, Corrado and Keller, Barbara and Kivelä, Mikko , file =. Integrated or
[32]

, month = aug, year =

Qin, Libo and Chen, Qiguang and Feng, Xiachong and Wu, Yang and Zhang, Yongheng and Li, Yinghui and Li, Min and Che, Wanxiang and Yu, Philip S. , month = aug, year =. Large. doi:10.48550/arXiv.2405.12819 , abstract =

doi arXiv
[33]

Minaee, Shervin and Mikolov, Tomas and Nikzad, Narjes and Chenaghlu, Meysam and Socher, Richard and Amatriain, Xavier and Gao, Jianfeng , month = mar, year =. Large. doi:10.48550/arXiv.2402.06196 , abstract =

doi arXiv
[34]

Jamuie, Adelani and Blessing, Funke , file =. Natural
[35]

and Sap, Maarten , year =

Antoniak, Maria and Field, Anjalie and Mun, Jimin and Walsh, Melanie and Klein, Lauren F. and Sap, Maarten , year =. Riveter:. Proceedings of the 61st. doi:10.18653/v1/2023.acl-demo.36 , abstract =

2023 doi
[36]

Sentence-

Reimers, Nils and Gurevych, Iryna , month = aug, year =. Sentence-. doi:10.48550/arXiv.1908.10084 , abstract =

1908 doi arXiv
[37]

Proceedings of the 16th

Alhuzali, Hassan and Ananiadou, Sophia , year =. Proceedings of the 16th. doi:10.18653/v1/2021.eacl-main.135 , abstract =

2021 doi
[38]

Unfiltered

Shah, Kashish and Gerard, Patrick and Luceri, Luca and Ferrara, Emilio , year =. Unfiltered. doi:10.2139/ssrn.5018881 , abstract =

doi
[39]

Balduf, Leonhard and Sokoto, Saidu and Ascigil, Onur and Tyson, Gareth and Scheuermann, Björn and Korczyński, Maciej and Castro, Ignacio and Król, Michaŀ , month = nov, year =. Looking. Proceedings of the 2024. doi:10.1145/3646547.3688407 , abstract =

2024
[40]

IEEE Data Descriptions , author =

Descriptor:. IEEE Data Descriptions , author =. 2024 , pages =. doi:10.1109/IEEEDATA.2024.3474640 , abstract =

2024
[41]

Emergent: a novel data-set for stance classification , shorttitle =

Ferreira, William and Vlachos, Andreas , year =. Emergent: a novel data-set for stance classification , shorttitle =. Proceedings of the 2016. doi:10.18653/v1/N16-1138 , abstract =

2016 doi
[42]

doi:10.48550/arXiv.2506.07606 , abstract =

Rostami, Peyman and Rahimzadeh, Vahid and Adibi, Ali and Shakery, Azadeh , month = jun, year =. doi:10.48550/arXiv.2506.07606 , abstract =

doi arXiv
[43]

Politics and polarization on

Salloum, Ali and Quelle, Dorian and Iannucci, Letizia and Bovet, Alexandre and Kivelä, Mikko , month = jun, year =. Politics and polarization on. doi:10.48550/arXiv.2506.03443 , abstract =

doi arXiv
[44]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Truth. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2023 , pages =. doi:10.1609/icwsm.v17i1.22211 , abstract =

2023 doi
[45]

Balasubramanian, Ashiwin and Zou, Vito and Narayana, Hitesh and You, Christina and Ferrara, Emilio , year =. A. doi:10.2139/ssrn.5018883 , language =

doi
[46]

Allaway, Emily and McKeown, Kathleen , month = oct, year =. Zero-. doi:10.48550/arXiv.2010.03640 , abstract =

2010 doi arXiv
[47]

PLOS ONE , author =

". PLOS ONE , author =. 2024 , note =. doi:10.1371/journal.pone.0310330 , abstract =

2024 doi
[48]

doi:10.5281/ZENODO.14653265 , abstract =

Gildersleve, Patrick and Beers, Anna and Ito, Viviane and Orozco, Agustin and Tripodi, Francesca , month = jan, year =. doi:10.5281/ZENODO.14653265 , abstract =

doi
[49]

Shah, Chaitya and Konka, Ritesh and Malpani, Gautam and Mehta, Swapneel and Ng, Lynnette Hui Xian , month = jun, year =. Can. doi:10.48550/arXiv.2406.03354 , abstract =

doi arXiv
[50]

IEEE Transactions on Neural Networks and Learning Systems , author =

A. IEEE Transactions on Neural Networks and Learning Systems , author =. 2021 , pages =. doi:10.1109/TNNLS.2020.2978386 , abstract =

2021
[51]

2017 , pages =

Communications of the ACM , author =. 2017 , pages =. doi:10.1145/3065386 , abstract =

2017 doi
[52]

Teng, Fengwei and Shi, Quan and Yu, Zhaoyang and Zhang, Jiayi and Luo, Yuyu and Wu, Chenglin and Guo, Zhijiang , month = dec, year =. Atom of. doi:10.48550/arXiv.2502.12018 , abstract =

doi
[53]

Symmetry , author =

Convolutional. Symmetry , author =. 2024 , pages =. doi:10.3390/sym16111516 , abstract =

2024 doi
[54]

Behaviormetrika , author =

Can’t see the forest for the trees:. Behaviormetrika , author =. 2024 , pages =. doi:10.1007/s41237-023-00205-2 , abstract =

2024 doi
[55]

Chain-of-

Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed H and Le, Quoc V and Zhou, Denny , file =. Chain-of-
[56]

Wang, Xuezhi and Wei, Jason and Schuurmans, Dale and Le, Quoc and Chi, Ed and Narang, Sharan and Chowdhery, Aakanksha and Zhou, Denny , month = mar, year =. Self-. doi:10.48550/arXiv.2203.11171 , abstract =

doi arXiv
[57]

Baldock, Robert J N and Maennel, Hartmut , file =. Deep
[58]

Albalak, Alon and Elazar, Yanai and Xie, Sang Michael and Longpre, Shayne and Lambert, Nathan and Wang, Xinyi and Muennighoff, Niklas and Hou, Bairu and Pan, Liangming and Jeong, Haewon and Raffel, Colin and Chang, Shiyu and Hashimoto, Tatsunori and Wang, William Yang , month ...

doi arXiv
[59]

, month = feb, year =

Han, Song and Mao, Huizi and Dally, William J. , month = feb, year =. Deep. doi:10.48550/arXiv.1510.00149 , abstract =

doi arXiv
[60]

and Wilson, Andrew Gordon , month = dec, year =

Stanton, Samuel and Izmailov, Pavel and Kirichenko, Polina and Alemi, Alexander A. and Wilson, Andrew Gordon , month = dec, year =. Does. doi:10.48550/arXiv.2106.05945 , abstract =

doi arXiv
[61]

Neural Networks , author =

Towards dropout training for convolutional neural networks , volume =. Neural Networks , author =. 2015 , pages =. doi:10.1016/j.neunet.2015.07.007 , abstract =

2015 doi
[62]

Going deeper with convolutions , isbn =

Szegedy, Christian and. Going deeper with convolutions , isbn =. 2015. 2015 , pages =. doi:10.1109/CVPR.2015.7298594 , abstract =

2015
[63]

Tsitsulin, Anton and Palowitch, John and Perozzi, Bryan and Müller, Emmanuel , file =. Graph
[64]

Compressing

Chen, Wenlin and Wilson, James T and Tyree, Stephen and Weinberger, Kilian Q and Chen, Yixin , file =. Compressing
[65]

Importance

Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan , month = jun, year =. Importance. doi:10.48550/arXiv.1906.10771 , abstract =

1906 doi arXiv
[66]

Distilling the

Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff , month = mar, year =. Distilling the. doi:10.48550/arXiv.1503.02531 , abstract =

doi arXiv
[67]

Liebenwein, Lucas and Baykal, Cenk and Carter, Brandon and Gifford, David and Rus, Daniela , month = mar, year =. Lost in. doi:10.48550/arXiv.2103.03014 , abstract =

doi arXiv
[68]

Language

Brown, Tom B and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom a...
[69]

Frankle, Jonathan and Carbin, Michael , month = mar, year =. The. doi:10.48550/arXiv.1803.03635 , abstract =

doi arXiv
[70]

Wu, Haibing and Gu, Xiaodong , editor =. Max-. Neural. 2015 , note =. doi:10.1007/978-3-319-26532-2_6 , abstract =

2015 doi
[71]

Dropout:

Srivastava, Nitish and Hinton, Geoﬀrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan , file =. Dropout:
[72]

Lin, Zhenghao and Gou, Zhibin and Gong, Yeyun and Liu, Xiao and Shen, Yelong and Xu, Ruochen and Lin, Chen and Yang, Yujiu and Jiao, Jian and Duan, Nan and Chen, Weizhu , file =. Not
[73]

Fantastically

Lu, Yao and Bartolo, Max and Moore, Alastair and Riedel, Sebastian and Stenetorp, Pontus , year =. Fantastically. Proceedings of the 60th. doi:10.18653/v1/2022.acl-long.556 , abstract =

2022 doi
[74]

Sahoo, Pranab and Singh, Ayush Kumar and Saha, Sriparna and Jain, Vinija and Mondal, Samrat and Chadha, Aman , month = mar, year =. A. doi:10.48550/arXiv.2402.07927 , abstract =

doi arXiv
[75]

He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian , month = jun, year =. Deep. 2016. doi:10.1109/CVPR.2016.90 , abstract =

2016 doi
[76]

Mulchandani, Varun and Kim, Jung-Eun , year =
[77]

Lee, Namhoon and Ajanthan, Thalaiyasingam and Torr, Philip H. S. , month = feb, year =. doi:10.48550/arXiv.1810.02340 , abstract =

doi arXiv
[78]

Li, Hao and Kadav, Asim and Durdanovic, Igor and Samet, Hanan and Graf, Hans Peter , month = mar, year =. Pruning. doi:10.48550/arXiv.1608.08710 , abstract =

doi arXiv
[79]

Dong, Qingxiu and Li, Lei and Dai, Damai and Zheng, Ce and Ma, Jingyuan and Li, Rui and Xia, Heming and Xu, Jingjing and Wu, Zhiyong and Liu, Tianyu and Chang, Baobao and Sun, Xu and Li, Lei and Sui, Zhifang , month = oct, year =. A. doi:10.48550/arXiv.2301.00234 , abstract =

doi arXiv
[80]

Carlini, Nicholas and Jagielski, Matthew and Papernot, Nicolas and Terzis, Andreas and Tramer, Florian and Zhang, Chiyuan , file =. The
[81]

, month = oct, year =

Han, Song and Pool, Jeff and Tran, John and Dally, William J. , month = oct, year =. Learning both. doi:10.48550/arXiv.1506.02626 , abstract =

doi arXiv
[82]

Simonyan, Karen and Zisserman, Andrew , month = apr, year =. Very. doi:10.48550/arXiv.1409.1556 , abstract =

doi arXiv
[83]

Zico , month = may, year =

Sun, Mingjie and Liu, Zhuang and Bair, Anna and Kolter, J. Zico , month = may, year =. A. doi:10.48550/arXiv.2306.11695 , abstract =

doi arXiv
[84]

Liu, Jiachang and Shen, Dinghan and Zhang, Yizhe and Dolan, Bill and Carin, Lawrence and Chen, Weizhu , month = jan, year =. What. doi:10.48550/arXiv.2101.06804 , abstract =

doi arXiv
[85]

Rethinking the

Min, Sewon and Lyu, Xinxi and Holtzman, Ari and Artetxe, Mikel and Lewis, Mike and Hajishirzi, Hannaneh and Zettlemoyer, Luke , year =. Rethinking the. Proceedings of the 2022. doi:10.18653/v1/2022.emnlp-main.759 , abstract =

2022 doi
[86]

Mason-Williams, Gabryel and Dahlqvist, Fredrik , file =
[87]

, month = dec, year =

Ghahramani, Zoubin and Jordan, Michael I. , month = dec, year =. Learning from. doi:10.21236/ADA295618 , abstract =

doi
[88]

Investigating

Phang, Jason and Lampe, Michael and Ahmad, Lama and Agarwal, Sandhini and Fang, Cathy Mengying and Liu, Auren R and Danry, Valdemar and Lee, Eunhae and Chan, Samantha W T and Pataranutaporn, Pat and Maes, Pattie , file =. Investigating
[89]

Proceedings of the International AAAI Conference on Web and Social Media , author =

“. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2025 , pages =. doi:10.1609/icwsm.v19i1.35816 , abstract =

2025 doi
[90]

Discourse, Context & Media , author =

Applying conversation analysis methods to online talk:. Discourse, Context & Media , author =. 2016 , pages =. doi:10.1016/j.dcm.2016.04.001 , abstract =

2016 doi
[91]

Çetinkaya, Yusuf Mücahit and Ghafouri, Vahid and Suarez-Tangil, Guillermo and Such, Jose and Elmas, Tuğrulcan , file =. Cross-
[92]

Social Media + Society , author =

Politicization and. Social Media + Society , author =. 2022 , pages =. doi:10.1177/20563051221113075 , abstract =

2022 doi
[93]

Campello, Ricardo J. G. B. and Moulavi, Davoud and Sander, Joerg , editor =. Density-. Advances in. 2013 , note =. doi:10.1007/978-3-642-37456-2_14 , abstract =

2013 doi
[95]

Archives of Computational Methods in Engineering , author =

Mental. Archives of Computational Methods in Engineering , author =. 2023 , pages =. doi:10.1007/s11831-022-09863-z , abstract =

2023 doi
[96]

Bavel, Jay J Van and Robertson, Claire E , file =. Social
[97]

Engel, Kristen and Mitra, Tanushree and Spiro, Emma S , file =. Social
[98]

Alyahya, Ghadi and AlDayel, Abeer , file =. Hatred
[99]

Polarized

Rao, Ashwin and Chang, Rong-Ching and Zhong, Qiankun and Lerman, Kristina and Wojcieszak, Magdalena , file =. Polarized
[100]

Annals of the International Communication Association , author =

The role of (social) media in political polarization: a systematic review , volume =. Annals of the International Communication Association , author =. 2021 , keywords =. doi:10.1080/23808985.2021.1976070 , abstract =

2021
[101]

Brown, Joseph and Baggili, Ibrahim , editor =. Retruth. Digital. 2024 , note =. doi:10.1007/978-3-031-56580-9_6 , abstract =

2024 doi
[102]

, month = mar, year =

Abbas, Amro and Tirumala, Kushal and Simig, Dániel and Ganguli, Surya and Morcos, Ari S. , month = mar, year =. doi:10.48550/arXiv.2303.09540 , abstract =

doi arXiv
[103]

ACM Computing Surveys , author =

A. ACM Computing Surveys , author =. 2023 , pages =. doi:10.1145/3482853 , abstract =

2023 doi
[104]

Journal of Information Technology & Politics , author =

Trump,. Journal of Information Technology & Politics , author =. 2025 , pages =. doi:10.1080/19331681.2024.2328156 , abstract =

2025
[105]

Zhang, Yixuan and Gaggiano, Joseph D and Yongsatianchot, Nutchanon and Suhaimi, Nurul M and Kim, Miso and Sun, Yifan and Griffin, Jacqueline and Parker, Andrea G , month = apr, year =. What. Proceedings of the 2023. doi:10.1145/3544548.3581019 , abstract =

2023
[106]

Communication Research , author =

“. Communication Research , author =. 2022 , keywords =. doi:10.1177/00936502211042516 , abstract =

2022 doi
[107]

American Political Science Review , author =

A. American Political Science Review , author =. 2024 , pages =. doi:10.1017/S0003055423001041 , abstract =

2024 doi
[108]

Proceedings of the ACM on Human-Computer Interaction , author =

Modeling. Proceedings of the ACM on Human-Computer Interaction , author =. 2019 , pages =. doi:10.1145/3359253 , abstract =

2019 doi
[109]

Nature , author =

Quantifying social organization and political polarization in online platforms , volume =. Nature , author =. 2021 , keywords =. doi:10.1038/s41586-021-04167-x , language =

2021 doi
[110]

Human Behavior and Emerging Technologies , author =

Political polarization drives online conversations about. Human Behavior and Emerging Technologies , author =. 2020 , keywords =. doi:10.1002/hbe2.202 , abstract =

2020 doi
[111]

IEEE Access , author =

Learning. IEEE Access , author =. 2020 , keywords =. doi:10.1109/ACCESS.2020.2978950 , abstract =

2020
[112]

Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren , month = jun, year =. Qwen3. doi:10.48550/arXiv.2506.05176 , abstract =

doi arXiv
[113]

Jiang, Ting and Huang, Shaohan and Luan, Zhongzhi and Wang, Deqing and Zhuang, Fuzhen , year =. Scaling. Findings of the. doi:10.18653/v1/2024.findings-emnlp.181 , abstract =

2024 doi
[114]

Sentence-

Reimers, Nils and Gurevych, Iryna , year =. Sentence-. Proceedings of the 2019. doi:10.18653/v1/D19-1410 , abstract =

2019 doi
[115]

Exploring the

Al-Daoud, Essam and Samara, Ghassan and Sara, Mutaz Rsmi Abu and Taqatqa, Sameh and Kanan, Mohammad , editor =. Exploring the. Artificial. 2024 , keywords =. doi:10.1007/978-3-031-56586-1_18 , abstract =

2024 doi
[116]

Yuan, Chunyuan and Ma, Qianwen and Zhou, Wei and Han, Jizhong and Hu, Songlin , month = nov, year =. Jointly. 2019. doi:10.1109/ICDM.2019.00090 , abstract =

2019
[117]

Divergences in

Fulay, Suyash and Gillani, Nabeel and Roy, Deb , month = jun, year =. Divergences in. Proceedings of the International AAAI Conference on Web and Social Media , publisher =. doi:10.1609/icwsm.v17i1.22220 , abstract =

doi
[118]

Khatua, Aparup and Zagheni, Emilio and Weber, Ingmar , month = jun, year =. Host-. Proceedings of the International AAAI Conference on Web and Social Media , publisher =. doi:10.1609/icwsm.v17i1.22224 , abstract =

doi
[119]

Proceedings of the International AAAI Conference on Web and Social Media , publisher =

Boesinger, Léopaul and Ribeiro, Manoel Horta and Veselovsky, Veniamin and West, Robert , month = may, year =. Proceedings of the International AAAI Conference on Web and Social Media , publisher =. doi:10.1609/icwsm.v18i1.31450 , abstract =

doi
[120]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Opposites. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2025 , pages =. doi:10.1609/icwsm.v19i1.35967 , abstract =

2025 doi
[121]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Pathways to. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2025 , pages =. doi:10.1609/icwsm.v19i1.35965 , abstract =

2025 doi
[122]

Quantifying ideological polarization on a network using generalized

Hohmann, Marilena and Devriendt, Karel and Coscia, Michele , month = mar, year =. Quantifying ideological polarization on a network using generalized. Science Advances , publisher =. doi:10.1126/sciadv.abq2044 , abstract =

doi
[123]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Generalized. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2020 , pages =. doi:10.1609/icwsm.v14i1.7284 , abstract =

2020 doi
[124]

IEEE Transactions on Big Data , author =

A. IEEE Transactions on Big Data , author =. 2023 , keywords =. doi:10.1109/TBDATA.2022.3177455 , abstract =

2023
[125]

Expert Systems with Applications , author =

A review on sentiment analysis from social media platforms , volume =. Expert Systems with Applications , author =. 2023 , keywords =. doi:10.1016/j.eswa.2023.119862 , abstract =

2023
[126]

ACM Comput

Network. ACM Comput. Surv. , author =. 2022 , pages =. doi:10.1145/3491206 , abstract =

2022 doi
[127]

and Guess, Andrew and Barbera, Pablo and Vaccari, Cristian and Siegel, Alexandra and Sanovich, Sergey and Stukal, Denis and Nyhan, Brendan , month = mar, year =

Tucker, Joshua A. and Guess, Andrew and Barbera, Pablo and Vaccari, Cristian and Siegel, Alexandra and Sanovich, Sergey and Stukal, Denis and Nyhan, Brendan , month = mar, year =. Social. doi:10.2139/ssrn.3144139 , abstract =

doi
[128]

Lee, Sangwon and Rojas, Hernando and Yamamoto, Masahiro , month = sep, year =. Social. Mass Communication and Society , publisher =. doi:10.1080/15205436.2021.1953534 , abstract =

2021
[129]

Current Opinion in Psychology , author =

Conspiracy theories and social media platforms , volume =. Current Opinion in Psychology , author =. 2022 , keywords =. doi:10.1016/j.copsyc.2022.101407 , abstract =

2022
[130]

A review and provocation:

Kreiss, Daniel and McGregor, Shannon C , month = jan, year =. A review and provocation:. New Media & Society , publisher =. doi:10.1177/14614448231161880 , abstract =

doi
[131]

ACM Trans

Comparing. ACM Trans. Web , author =. 2025 , keywords =. doi:10.1145/3707701 , abstract =

2025 doi
[132]

Proceedings of the International AAAI Conference on Web and Social Media , author =

The. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , keywords =. doi:10.1609/icwsm.v18i1.31380 , abstract =

2024 doi
[133]

and Bossetta, Michael , month = nov, year =

Boulianne, Shelley and Hoffmann, Christian P. and Bossetta, Michael , month = nov, year =. Social media platforms for politics:. New Media & Society , publisher =. doi:10.1177/14614448241262415 , abstract =

doi
[134]

Political

Yarchi, Moran and Baden, Christian and Kligler-Vilenchik, Neta , month = mar, year =. Political. Political Communication , publisher =. doi:10.1080/10584609.2020.1785067 , abstract =

2020
[135]

, month = may, year =

Iyengar, Shanto and Lelkes, Yphtach and Levendusky, Matthew and Malhotra, Neil and Westwood, Sean J. , month = may, year =. The. Annual Review of Political Science , publisher =. doi:10.1146/annurev-polisci-051117-073034 , abstract =

doi
[136]

In different worlds:

Overgaard, Christian Staal Bruun and Collier, Jessica R , month = jan, year =. In different worlds:. New Media & Society , publisher =. doi:10.1177/14614448231176551 , abstract =

doi
[137]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Differences in the. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , keywords =. doi:10.1609/icwsm.v18i1.31402 , abstract =

2024 doi
[138]

Proceedings of the International AAAI Conference on Web and Social Media , author =

A. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2024 , keywords =. doi:10.1609/icwsm.v18i1.31356 , abstract =

2024 doi
[139]

Modeling

Kamal, Sadia and Gullic, Jade and Bagavathi, Arunkumar , editor =. Modeling. Foundations of. 2022 , keywords =. doi:10.1007/978-3-031-16564-1_4 , abstract =

2022 doi
[140]

PNAS Nexus , author =

Troll and divide: the language of online polarization , volume =. PNAS Nexus , author =. 2022 , keywords =. doi:10.1093/pnasnexus/pgac019 , abstract =

2022 doi
[141]

Sol and Chinn, Sedona and Soroka, Stuart , month = oct, year =

Hart, P. Sol and Chinn, Sedona and Soroka, Stuart , month = oct, year =. Politicization and. Science Communication , publisher =. doi:10.1177/1075547020950735 , abstract =

doi
[142]

Finding a "

Satopaa, Ville and Albrecht, Jeannie and Irwin, David and Raghavan, Barath , month = jun, year =. Finding a ". 2011 31st. doi:10.1109/ICDCSW.2011.20 , abstract =

2011 doi
[143]

Toward a

Morini, Virginia and Pollacci, Laura and Rossetti, Giulio , month = jun, year =. Toward a. Applied Sciences , publisher =. doi:10.3390/app11125390 , abstract =

doi
[144]

Royal Society open science , author =

Multifaceted polarization and information reliability in climate change discussions on social media platforms , volume =. Royal Society open science , author =. 2025 , keywords =. doi:10.1098/rsos.241974 , abstract =

2025 doi
[145]

Chaos: An Interdisciplinary Journal of Nonlinear Science , author =

Measuring political polarization:. Chaos: An Interdisciplinary Journal of Nonlinear Science , author =. 2015 , pages =. doi:10.1063/1.4913758 , abstract =

2015 doi
[146]

Proceedings of the International AAAI Conference on Web and Social Media , author =

A. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2013 , keywords =. doi:10.1609/icwsm.v7i1.14421 , abstract =

2013 doi
[147]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Political. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2021 , pages =. doi:10.1609/icwsm.v5i1.14126 , abstract =

2021 doi
[148]

Growing polarization around climate change on social media , volume =

Falkenberg, Max and Galeazzi, Alessandro and Torricelli, Maddalena and Di Marco, Niccolò and Larosa, Francesca and Sas, Madalina and Mekacher, Amin and Pearce, Warren and Zollo, Fabiana and Quattrociocchi, Walter and Baronchelli, Andrea , month = dec, year =. Growing polarizat...

doi
[149]

The echo chamber effect on social media , volume =

Cinelli, Matteo and De Francisci Morales, Gianmarco and Galeazzi, Alessandro and Quattrociocchi, Walter and Starnini, Michele , month = mar, year =. The echo chamber effect on social media , volume =. Proceedings of the National Academy of Sciences , publisher =. doi:10.1073/p...

doi
[150]

Anatomy of elite and mass polarization in social networks , volume =

Salloum, Ali and Chen, Ted Hsuan Yun and Kivelä, Mikko , year =. Anatomy of elite and mass polarization in social networks , volume =. Network Science , publisher =
[151]

Quantifying

Garimella, Kiran and. Quantifying. ACM Transactions on Social Computing , publisher =. 2018 , keywords =. doi:10.1145/3140565 , abstract =

2018 doi
[152]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Falling into the. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2020 , pages =. doi:10.1609/icwsm.v14i1.7285 , abstract =

2020 doi
[153]

Separating. Proc. ACM Hum.-Comput. Interact. , author =. 2022 , pages =. doi:10.1145/3512962 , abstract =

2022 doi
[154]

Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries , volume =

Falkenberg, Max and Zollo, Fabiana and Quattrociocchi, Walter and Pfeffer, Jürgen and Baronchelli, Andrea , month = nov, year =. Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries , volume =. Nature Commu...

doi
[155]

Charting multidimensional ideological polarization across demographic groups in the

Ojer, Jaume and Cárcamo, David and Pastor-Satorras, Romualdo and Starnini, Michele , month = oct, year =. Charting multidimensional ideological polarization across demographic groups in the. Nature Human Behaviour , publisher =. doi:10.1038/s41562-025-02251-0 , abstract =

doi
[156]

Dimant, Eugen , month = jan, year =. Hate. Management Science , publisher =. doi:10.1287/mnsc.2023.4701 , abstract =

2023
[157]

Information Sciences , author =

The drivers of online polarization:. Information Sciences , author =. 2023 , pages =. doi:10.1016/j.ins.2023.119152 , abstract =

2023
[158]

PNAS Nexus , author =

The social media discourse of engaged partisans is toxic even when politics are irrelevant , volume =. PNAS Nexus , author =. 2023 , pages =. doi:10.1093/pnasnexus/pgad325 , abstract =

2023 doi
[159]

Chaos, Solitons & Fractals , author =

Multipolar social systems:. Chaos, Solitons & Fractals , author =. 2023 , keywords =. doi:10.1016/j.chaos.2023.113244 , abstract =

2023
[160]

Social Indicators Research , author =

Social. Social Indicators Research , author =. 2025 , keywords =. doi:10.1007/s11205-024-03367-y , abstract =

2025 doi
[161]

Tracking patterns in toxicity and antisocial behavior over user lifetimes on large social media platforms , volume =

Blumer, Katy and Kleinberg, Jon , month = jul, year =. Tracking patterns in toxicity and antisocial behavior over user lifetimes on large social media platforms , volume =. Scientific Reports , publisher =. doi:10.1038/s41598-025-07086-3 , abstract =

doi
[162]

Understanding

Lyu, Hanjia and Luo, Jiebo , month = oct, year =. Understanding. Proceedings of the 30th. doi:10.1145/3503161.3547898 , abstract =
[163]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Echo. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2023 , keywords =. doi:10.1609/icwsm.v17i1.22177 , abstract =

2023 doi
[164]

Political polarization in social media:

Takikawa, Hiroki and Nagayoshi, Kikuko , month = dec, year =. Political polarization in social media:. 2017. doi:10.1109/BigData.2017.8258291 , abstract =

2017
[165]

ResearchGate , file =

Depolarization through social media use:. ResearchGate , file =. doi:10.1177/1461444820910124 , abstract =

doi
[166]

and Argyle, Lisa P

Bail, Christopher A. and Argyle, Lisa P. and Brown, Taylor W. and Bumpus, John P. and Chen, Haohan and Hunzaker, M. B. Fallin and Lee, Jaemin and Mann, Marcus and Merhout, Friedolin and Volfovsky, Alexander , month = sep, year =. Exposure to opposing views on social media can ...

doi
[167]

Vaccine , author =

Polarization of the vaccination debate on. Vaccine , author =. 2018 , keywords =. doi:10.1016/j.vaccine.2018.05.040 , abstract =

2018 doi
[168]

ACM Trans

Polarization and. ACM Trans. Web , author =. 2019 , pages =. doi:10.1145/3316809 , abstract =

2019 doi
[169]

and Milner, Helen V

Levin, Simon A. and Milner, Helen V. and Perrings, Charles , month = dec, year =. The dynamics of political polarization , volume =. Proceedings of the National Academy of Sciences , publisher =. doi:10.1073/pnas.2116950118 , number =

doi
[170]

and Kertész, János and Iñiguez, Gerardo , month = dec, year =

Peralta, Antonio F. and Kertész, János and Iñiguez, Gerardo , month = dec, year =. Opinion dynamics in social networks: from models to data , isbn =. Handbook of
[171]

Policy & Internet , author =

Measuring. Policy & Internet , author =. 2020 , note =. doi:10.1002/poi3.222 , abstract =

2020 doi
[172]

Birds of a feather don’t fact-check each other:

Allen, Jennifer and Martel, Cameron and Rand, David G , month = apr, year =. Birds of a feather don’t fact-check each other:. Proceedings of the 2022. doi:10.1145/3491102.3502040 , abstract =

2022
[173]

Political

Rao, Ashwin and Morstatter, Fred and Hu, Minda and Chen, Emily and Burghardt, Keith and Ferrara, Emilio and Lerman, Kristina , month = jun, year =. Political. Journal of Medical Internet Research , publisher =. doi:10.2196/26692 , abstract =

doi
[174]

EPJ Data Science , author =

Partisan. EPJ Data Science , author =. 2012 , file =. doi:10.1140/epjds6 , abstract =

2012 doi
[175]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Political. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2021 , keywords =. doi:10.1609/icwsm.v15i1.18104 , abstract =

2021 doi
[176]

Proceedings of the International AAAI Conference on Web and Social Media , author =

Towards. Proceedings of the International AAAI Conference on Web and Social Media , author =. 2020 , pages =. doi:10.1609/icwsm.v14i1.7298 , abstract =

2020 doi
[177]

Characterizing

Hua, Yiqing and Naaman, Mor and Ristenpart, Thomas , month = apr, year =. Characterizing. Proceedings of the 2020. doi:10.1145/3313831.3376548 , abstract =

2020
[178]

and Evans, James A

Shi, Feng and Shi, Yongren and Dokshin, Fedor A. and Evans, James A. and Macy, Michael W. , month = apr, year =. Millions of online book co-purchases reveal partisan differences in the consumption of science , volume =. Nature Human Behaviour , publisher =. doi:10.1038/s41562-...

doi
[179]

Del Vicario, Michela and Vivaldo, Gianna and Bessi, Alessandro and Zollo, Fabiana and Scala, Antonio and Caldarelli, Guido and Quattrociocchi, Walter , month = dec, year =. Echo. Scientific Reports , publisher =. doi:10.1038/srep37825 , abstract =

doi
[180]

and Nagler, Jonathan and Tucker, Joshua A

Barberá, Pablo and Jost, John T. and Nagler, Jonathan and Tucker, Joshua A. and Bonneau, Richard , month = oct, year =. Tweeting. Psychological Science , publisher =. doi:10.1177/0956797615594620 , abstract =

doi
[181]

Trends in Cognitive Sciences , author =

How social media shapes polarization , volume =. Trends in Cognitive Sciences , author =. 2021 , keywords =. doi:10.1016/j.tics.2021.07.013 , abstract =

2021 doi
[182]

and Abrams, Samuel J

Fiorina, Morris P. and Abrams, Samuel J. and Pope, Jeremy , year =. Culture
[183]

Public Opinion Quarterly , author =

The. Public Opinion Quarterly , author =. 2011 , pages =. doi:10.1093/poq/nfr042 , abstract =

2011 doi
[184]

Hughes, Emelia May and Weninger, Tim , month = oct, year =. Echo. Companion. doi:10.1145/3715070.3749241 , abstract =
[185]

arXiv.org , author =

Beyond the ". arXiv.org , author =. 2026 , file =

2026
[186]

Ann Rev Polit Sci , year=

Political polarization in the American public , author=. Ann Rev Polit Sci , year=
[187]

new media & society , volume=

Far-right populism in alt-tech: A challenge for democracy? , author=. new media & society , volume=. 2025 , publisher=

2025
[188]

Political Analysis , volume=

Measuring political positions from legislative speech , author=. Political Analysis , volume=. 2016 , publisher=

2016
[189]

Convergence: The International Journal of Research into New Media Technologies , volume=

Far-right conspiracy groups on fringe platforms: A longitudinal analysis of radicalization dynamics on Telegram , author=. Convergence: The International Journal of Research into New Media Technologies , volume=. 2022 , publisher=

2022
[190]

Nature human behaviour , volume=

Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic , author=. Nature human behaviour , volume=. 2020 , publisher=

2020
[191]

Nature , volume=

Shifting attention to accuracy can reduce misinformation online , author=. Nature , volume=. 2021 , publisher=

2021
[192]

Telematics and Informatics , volume=

Social media and online political participation: The mediating role of exposure to cross-cutting and like-minded perspectives , author=. Telematics and Informatics , volume=. 2016 , publisher=

2016
[193]

AFFECT, NOT IDEOLOGY: A SOCIAL IDENTITY PERSPECTIVE ON POLARIZATION , urldate =

Iyengar, Shanto and Sood, Gaurav and Lelkes, Yphtach , journal =. AFFECT, NOT IDEOLOGY: A SOCIAL IDENTITY PERSPECTIVE ON POLARIZATION , urldate =
[194]

and Abrams, Samuel J

Fiorina, Morris P. and Abrams, Samuel J. and Pope, Jeremy. Culture war? : the myth of a polarized America. 2011

2011
[195]

2026 , eprint=

TruthStance: An Annotated Dataset of Conversations on Truth Social , author=. 2026 , eprint=

2026
[196]

Presidential Election Discourse on Truth Social , author=

Unfiltered Conversations: A Dataset of 2024 U.S. Presidential Election Discourse on Truth Social , author=. 2024 , eprint=

2024
[197]

arXiv preprint arXiv:2506.05176 , year=

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models , author=. arXiv preprint arXiv:2506.05176 , year=

arXiv
[198]

McCain, Miles and Thiel, David , title =
[199]

Nature human behaviour , volume=

A consensus-based transparency checklist , author=. Nature human behaviour , volume=. 2020 , publisher=

2020
[200]

Communications of the ACM , volume=

Datasheets for datasets , author=. Communications of the ACM , volume=. 2021 , publisher=

2021
[201]

Centre for the Governance of AI

A guide to writing the NeurIPS impact statement , author=. Centre for the Governance of AI. URL: https://perma. cc/B5R8-2B9V , year=
[202]

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts , pages=

Understanding Ethics in NLP Authoring and Reviewing , author=. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts , pages=
[203]

NeurIPS 2021 Paper Checklist Guidelines

NeurIPS. NeurIPS 2021 Paper Checklist Guidelines

2021
[204]

The FAIR Data principles

FORCE11. The FAIR Data principles

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

Proceedings of the first instructional conference on machine learning , volume=

Using tf-idf to determine word relevance in document queries , author=. Proceedings of the first instructional conference on machine learning , volume=. 2003 , organization=

2003

[2] [2]

Advances in Neural Information Processing Systems , volume=

Matryoshka representation learning , author=. Advances in Neural Information Processing Systems , volume=

[3] [3]

arXiv preprint arXiv:2410.21276 , year=

Gpt-4o system card , author=. arXiv preprint arXiv:2410.21276 , year=

arXiv

[4] [4]

Proceedings of the Second Workshop on NLP and Computational Social Science , pages=

community2vec: Vector representations of online communities encode semantic relationships , author=. Proceedings of the Second Workshop on NLP and Computational Social Science , pages=

[5] [5]

Vaccines , volume=

\# Scamdemic,\# Plandemic, or\# Scaredemic: what Parler social media platform tells us about COVID-19 vaccine , author=. Vaccines , volume=. 2021 , publisher=

2021

[6] [6]

Proceedings of the ACM on Human-Computer Interaction , volume=

Content Moderation and Hate Speech on Alternative Platforms: A Case Study of BitChute , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2025 , publisher=

2025

[7] [7]

2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) , pages=

Inside the right-leaning echo chambers: Characterizing gab, an unmoderated social system , author=. 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) , pages=. 2018 , organization=

2018

[8] [8]

Addawood, Aseel and Bashir, Masooda , year =. ". Proceedings of the. doi:10.18653/v1/W16-2801 , abstract =

work page doi:10.18653/v1/w16-2801