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REVIEW 3 major objections 5 minor 81 references

MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read MGM borrows labels from globally similar news outlets and improves factuality and bias prediction across GNN and language-model baselines.

desk verdict Solid empirical paper; the internal GNN gains are plausible, but the new-SOTA claim rests on an unverified test-split and ensemble comparison. read the letter →

arxiv 2412.10467 v1 pith:MTM4WGAS submitted 2024-12-12 cs.LG cs.AIcs.CLstat.ML

Muhammad Arslan Manzoor , Ruihong Zeng , Dilshod Azizov , Preslav Nakov , Shangsong Liang This is my paper · ORCID
classification cs.LGcs.AIcs.CLstat.ML
keywords mediafactualitypredictionpoliticalbiasdetectiongraphneuralnetworksvariationalexpectation-maximizationglobalmemorymoduleaudienceoverlapsemi-supervisednodeclassificationnewsprofiling
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

MediaGraphMind (MGM) argues that the factuality and political bias of a news outlet can be predicted more accurately by borrowing labels from similar outlets anywhere in the audience-overlap graph, not only from immediate neighbors. The paper wraps ordinary GNNs in a variational expectation-maximization loop: node embeddings are stored in an external memory, a few globally similar labeled nodes are drawn from a sparse candidate set, and their labels are mixed with the target node's own representation before classification. Across eight GNN backbones on the ACL-2020 and EMNLP-2018 media graphs, MGM raises Macro-F1 in nearly every configuration, and combining MGM probabilities with fine-tuned language models yields new state-of-the-art scores (79.72 factuality and 93.04 bias Macro-F1 on ACL-2020). If this holds, media profiling can exploit relational structure even when the graph is fragmented and only about 1% of nodes are labeled.

What carries the argument

The load-bearing object is a variational expectation-maximization wrapper around any GNN, paired with an external memory of labeled-node embeddings. A Dirichlet prior $p_\alpha(\omega)$ over candidate-node weights encourages sparse selection, so only the top-$M$ nodes that cover 90% of the probability mass are stored and retrieved at test time. The core identity is the blended predictor $p_\theta(Y|Z,T)=\eta\,p_\theta(Y|Z)+(1-\eta)\,p_\theta(Y|T)$, where $p_\theta(Y|T)$ propagates labels from the $K$ globally similar nodes. This identity lets local GNN representations and global label evidence cooperate, and the KL divergence terms in the ELBO train the retrieval distribution to prefer genuinely similar nodes.

What would settle it

On the ACL-2020 level-3 graph, inspect the top-$K$ memory neighbors MGM retrieves for each node: if their mean label agreement is not clearly above the class base rate, or if replacing the retrieved neighbors with randomly chosen memory nodes leaves Macro-F1 unchanged, then the global-similarity mechanism is not carrying the reported gains.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a small set of global similar nodes, namely outlets whose stored embeddings resemble the target's, carries much of the relational signal that local message passing misses. MGM formalizes this by treating the indicator of which similar nodes to consult as a latent variable $T$ drawn from a multinomial over memory embeddings, and by predicting each label as $\eta\,p_\theta(Y|Z)+(1-\eta)\,p_\theta(Y|T)$: a blend of the GNN's own prediction and the labels of the $K$ retrieved lookalikes. With that two-term predictor inside a variational EM loop, the paper reports consistent Macro-F1 gains over eight GNN backbones (e.g., GCN factuality Macro-F1 rising from 25.55 to 43.05 on ACL-2020), and combining MGM probabilities with fine-tuned language models achieves 79.72 factuality and 93.04 bias Macro-F1 on ACL-2020, which the paper identifies as new state-of-the-art results.

Load-bearing premise

The load-bearing premise is homophily: outlets whose stored embeddings are similar actually tend to share factuality and bias labels, so the label-propagation term $p_\theta(Y|T)$ adds signal rather than noise; if the overlap graph or embedding similarity disagrees with label similarity, the global term injects errors and the reported gains would shrink.

Editorial extensions

If this is right

  • MGM can be added to a GNN without changing the base architecture; the paper reports gains for GCN, GAT, GraphSAGE, SGC, DNA, FiLM, FAGCN, and GATv2 on both factuality and bias tasks.
  • The label-mixing term helps in the low-label regime: with roughly 1% of graph nodes labeled, MGM outperforms the vanilla GNNs, and the advantage grows as the training-label fraction increases from 60% to 100%.
  • MGM supplies a probability even when no text is available, so it can fill missing Article and Wikipedia entries for outlets that could not be scraped, replacing zero-probability placeholders and lifting language-model accuracy.
  • The 90%-memory variant performs on par with the full-memory variant, indicating that the sparse Dirichlet candidate selection preserves most of the benefit while reducing storage.
  • The same augmented GNNs also improve on the Ogbn-mag large graph, showing the mechanism is not specific to media graphs.

Reading between the lines

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

  • Beyond the paper's media domain, the same memory-plus-label-mixing recipe could be applied to any semi-supervised node-classification problem with disconnected components, such as citation networks or social graphs, where global lookalikes are the only bridge between components.
  • The interpolation coefficient $\eta$ creates a testable continuum: setting $\eta=1$ recovers the vanilla GNN, so tracing performance as $\eta$ decreases isolates how much of the gain comes from global labels versus from the extra capacity of the memory module.
  • A practical extension the paper does not run is to feed MGM's predicted probabilities as soft priors to a second-stage language-model verification system, potentially reducing the need to scrape full article text.
  • Because the audience-overlap graph was built from a now-unavailable ranking service, the paper's gains rest on one graph construction; re-running MGM on a freshly built overlap graph from newer audience data would test whether the global-similarity signal persists as the edge set changes.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces MediaGraphMind (MGM), a variational Expectation-Maximization framework that augments Graph Neural Networks for news-media factuality and political-bias classification by retrieving information from globally similar training nodes stored in a sparse external memory. MGM is also combined with fine-tuned PLM probabilities through a logistic-regression meta-learner. Experiments cover eight GNN backbones on the ACL-2020 and EMNLP-2018 media graphs, memory-usage ablations, varying label proportions, and a large-graph sanity check on Ogbn-mag. The central claims are that MGM substantially improves all base GNNs and that its PLM combination sets a new state of the art.

Significance. If the reported gains hold under a matched evaluation protocol, MGM is a useful and broadly applicable module: it is orthogonal to the choice of GNN backbone, it is evaluated on two real media graphs plus one large benchmark, it includes ablation studies for the memory mechanism and for label sparsity, and the authors release code and data. The variational-EM framing and the sparse-memory mechanism are sensible extensions of prior memory-augmented GNN work. However, the headline 'new state-of-the-art' result currently rests on an incompletely specified comparison protocol, so the significance of the SOTA claim cannot be assessed without a revision.

major comments (3)
  1. [§5.5, Tables 7, 8, 10; Appendices C and D] The paper never states which test split is used for the Stage 3/4 state-of-the-art comparisons in Table 7. The GNN experiments are reported on the full 859-node graph with a 687/172 split (Table 8 and Appendix C), whereas the PLM experiments are conducted on the 472 outlets for which Articles and Wikipedia text could be scraped, with a 387/85 split (Table 10 and Appendix D). The factuality label distributions of these two test populations are very different (text-subset counts: high 295 / mixed 119 / low 58; full graph: high 162 / mixed 249 / low 453). If Table 7 uses the 85-node text-available test set while the cited prior results of Panayotov et al. (2022) and Mehta et al. (2022) were obtained on the full 172-node split, the comparison is invalid. Please state the exact test nodes for every row of Table 7, rerun the cited baselines on the same split, or restrict the SOTA claim to a matched comparison.
  2. [§5.5, Table 7, Figure 3] The Stage 4 result is a five-model ensemble (two PLM variants plus three MGM-GNN variants) combined by a learned logistic-regression meta-learner, whereas the prior systems cited in Table 7 are single models. The reported improvement over those systems therefore conflates the contribution of MGM with the contribution of ensembling. To make the SOTA claim interpretable, the authors should report the best single-model result under the same protocol, and should also apply the same logistic-regression ensemble procedure to the output probabilities of the prior systems (or otherwise isolate the ensemble effect).
  3. [Abstract and §1, Table 1] The abstract and introduction state that MGM 'delivers a 10% increase across all evaluation measures' on the ACL-2020 and EMNLP-2018 datasets. Table 1 does not support a uniform 10% increase: for example, on Bias-2020, GraphSAGE improves Macro-F1 from 39.35 to 46.77 (7.42 points) but Average Recall only from 49.09 to 50.18 (1.09 points), and GAT on Fact-2020 improves Accuracy by only 0.93 points. The claim should be replaced by precise per-model, per-metric percentage-point changes or by a clearly defined aggregate (e.g., mean absolute improvement across all metrics).
minor comments (5)
  1. [Eq. (9), §3.3] The variational factorization is written as 'qλ(ω)qϕ(T, Z, ω | Yl)qϕ(T | Yl)qϕ(Z | T, Yl)', which contains both a joint qϕ(T,Z,ω|Yl) and its marginals; this is redundant and appears to be a typo. Please state the intended mean-field factorization cleanly.
  2. [Table 1] Several entries report zero standard deviation (e.g., FAGCN+MGM Fact-2020 Macro-F1 48.77±0.00 and Average Recall 49.19±0.00) while other rows in the same table show variability across seeds. Since Appendix C says five random seeds are used, please clarify whether these entries are deterministic or whether the standard deviation was omitted.
  3. [Tables 4 and 5] The Fact-2018 results are inconsistent between Table 4 and Table 5 for several models: for example, GraphSAGE base Macro-F1 is 41.77 in Table 5 and GraphSAGE+MGM is 47.86, while Table 4 reports 46.54/47.86 for the full/reduced memory comparison; GCN and SGC show similar discrepancies. Please reconcile the two tables and clearly mark which column corresponds to the base model, full memory, and 90% memory.
  4. [Appendix C and Table 8] Appendix C says the data are randomly split into 70% training, 10% validation, and 20% test, and that training and validation are later combined into a larger final training set; Table 8 reports an 80% training / 20% test split. Please clarify the exact split used for the reported numbers, including how hyperparameter selection on the validation set interacts with the final training set.
  5. [Figure 2 and §5.2–5.3] The text says Figure 2 shows Macro-F1 'on the test set' for different values of K and η, while Appendix C states that K and η are tuned on the validation set. Reporting test performance across a hyperparameter grid can be misleading; please state whether these curves are validation curves or test curves, and whether the final K and η were selected before or after evaluating on the test set.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: MGM's label-propagation term is a standard transductive mechanism, and the only same-author citation is non-load-bearing.

full rationale

Walking the claimed derivation chain, MGM's core equations are internally defined rather than imported from the conclusion. Equation (6), pθ(Y|T) ∝ Σ T_NM · Y_M, is a label-propagation term over globally similar training/memory nodes; this is a standard transductive mechanism, not a circular identity, because the labels used are those of candidate training nodes stored in memory, not the label of the node being predicted, and test labels are not fed back into T. The variational EM objective in Equation (9) is a standard ELBO optimized against the training labels, so the reported gains over GCN, GAT, GraphSAGE, etc. are empirical benchmark outcomes rather than consequences of a fitted equality. The only same-author citation, Zeng et al. (2024), appears in a general list of message-passing GNN works and is not invoked to justify MGM's equations or to rule out alternatives, so it is not load-bearing. A separate evaluation-validity concern—the GNN experiments use a 687/172 split of 859 outlets (Table 8) while the PLM experiments use a 387/85 split of 472 text-available outlets (Table 10)—could affect whether the Table 7 state-of-the-art comparison is apples-to-apples, but a possible test-set mismatch is an evaluation-protocol issue, not a circular derivation. The Stage 4 ensemble uses a logistic-regression meta-learner over model probabilities, which is standard stacking and does not make the prediction identical to its training input. Accordingly, no circular step is identified.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard ML machinery (variational EM, Dirichlet priors, multinomial sampling) plus a domain assumption of homophily in audience-overlap graphs. Three hyperparameters are tuned on validation. No new physical or conceptual entities are introduced.

free parameters (3)
  • K (number of global similar nodes) = tuned in [1,7]
    Controls how many similar nodes are sampled from memory; selected on validation set (Appendix C).
  • η (trade-off between local and global predictions) = tuned in [0.6,1]
    Blends p(Y|Z) and p(Y|T) in Eq. 7; selected on validation set (Appendix C).
  • α (Dirichlet concentration) = 0.1
    Encourages sparse node selection; fixed in Appendix C.
assumptions (4)
  • standard math Variational EM and ELBO decomposition are valid for optimizing the marginal likelihood with latent variables.
    Used in Section 3.3 (Eq. 9); these are established mathematical tools.
  • domain assumption Homophily in media graphs: outlets with overlapping audiences have similar factuality and bias labels.
    Invoked in Section 2.1 and used in Eq. 6 for label prediction from similar nodes; not independently validated in the paper.
  • domain assumption The level-3 Alexa audience-overlap graph accurately represents relationships between media outlets.
    The graph is taken from Panayotov et al. (2022) and used as the sole structural input; the tool is now unavailable.
  • ad hoc to paper A Dirichlet prior with α<1 yields a sparse candidate-node distribution that preserves useful global information.
    Set to 0.1 in Appendix C following He et al. (2020); this is a modeling choice not derived from first principles.

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Cite this review

Pith. "Pith review of MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media." pith.science (2026). https://pith.science/paper/MTM4WGAS

@misc{pith2026241210467,
  author       = {Pith},
  title        = {Pith review of: MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MTM4WGAS}},
  note         = {Machine review of arXiv:2412.10467}
}
read the original abstract

In the current era of rapidly growing digital data, evaluating the political bias and factuality of news outlets has become more important for seeking reliable information online. In this work, we study the classification problem of profiling news media from the lens of political bias and factuality. Traditional profiling methods, such as Pre-trained Language Models (PLMs) and Graph Neural Networks (GNNs) have shown promising results, but they face notable challenges. PLMs focus solely on textual features, causing them to overlook the complex relationships between entities, while GNNs often struggle with media graphs containing disconnected components and insufficient labels. To address these limitations, we propose MediaGraphMind (MGM), an effective solution within a variational Expectation-Maximization (EM) framework. Instead of relying on limited neighboring nodes, MGM leverages features, structural patterns, and label information from globally similar nodes. Such a framework not only enables GNNs to capture long-range dependencies for learning expressive node representations but also enhances PLMs by integrating structural information and therefore improving the performance of both models. The extensive experiments demonstrate the effectiveness of the proposed framework and achieve new state-of-the-art results. Further, we share our repository1 which contains the dataset, code, and documentation

Figures

Figures reproduced from arXiv: 2412.10467 by the authors.

Figure 1
Figure 1. Key components of our proposed approach. The sections highlighted with a grey background represent [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. MGM performance across all GNNs for both [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. The pipeline of integrating MGM with PLMs. [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

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Reference graph

Works this paper leans on

81 extracted references · 58 canonical work pages

  1. [1]

    URL: " 'urlintro :=

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

  2. [2]

    write newline

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

  3. [3]

    Sadia Afroz, Michael Brennan, and Rachel Greenstadt. 2012. Detecting hoaxes, frauds, and deception in writing style online. In 2012 IEEE Symposium on Security and Privacy, pages 461--475. IEEE

    work page 2012

  4. [4]

    Jisun An, Meeyoung Cha, Krishna Gummadi, Jon Crowcroft, and Daniele Quercia. 2012. Visualizing media bias through T witter. In AAAI ICWSM, volume 6

    work page 2012

  5. [5]

    Dilshod Azizov, S Liang, and P Nakov. 2023. Frank at checkthat! 2023: Detecting the political bias of news articles and news media. Working Notes of CLEF

    work page 2023

  6. [6]

    Dilshod Azizov, Zain Mujahid, Hilal AlQuabeh, Preslav Nakov, and Shangsong Liang. 2024. Safari: Cross-lingual bias and factuality detection in news media and news articles. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12217--12231

    work page 2024

  7. [7]

    Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. https://doi.org/10.18653/v1/D18-1389 Predicting factuality of reporting and bias of news media sources . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3528--3539, Brussels, Belgium. Association for Computational Linguistics

    doi:10.18653/v1/d18-1389 2018

  8. [8]

    Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, and Preslav Nakov. 2020 a . What was written vs. who read it: news media profiling using text analysis and social media context. arXiv preprint arXiv:2005.04518

    work page Pith review arXiv 2020

Show all 81 references
  1. [9]

    Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, and Preslav Nakov. 2020 b . https://doi.org/10.18653/v1/2020.acl-main.308 What was written vs. who read it: News media profiling using text analysis and social media context . In Proceedi...

    2020 doi

  2. [10]

    Yejin Bang, Delong Chen, Nayeon Lee, and Pascale Fung. 2024. Measuring political bias in large language models: What is said and how it is said. arXiv preprint arXiv:2403.18932

    2024 arXiv

  3. [11]

    Yejin Bang, Nayeon Lee, and Pascale Fung. 2023. Mitigating framing bias with polarity minimization loss. arXiv preprint arXiv:2311.01817

    2023 arXiv

  4. [12]

    Alberto Barr \'o n-Cede \ n o, Firoj Alam, Tommaso Caselli, Giovanni Da San Martino, Tamer Elsayed, Andrea Galassi, Fatima Haouari, Federico Ruggeri, Julia Maria Stru , Rabindra Nath Nandi, et al. 2023 a . The clef-2023 checkthat! lab: Checkworthiness, subjectivity, political ...

    2023

  5. [13]

    Alberto Barr \'o n-Cede \ n o, Firoj Alam, Andrea Galassi, Giovanni Da San Martino, Preslav Nakov, Tamer Elsayed, Dilshod Azizov, Tommaso Caselli, Gullal S Cheema, Fatima Haouari, et al. 2023 b . Overview of the clef--2023 checkthat! lab on checkworthiness, subjectivity, polit...

    2023

  6. [14]

    Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261

    2018 arXiv

  7. [15]

    Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. 2021. Beyond low-frequency information in graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3950--3957

    2021

  8. [16]

    Aleksandar Bojchevski and Stephan G \"u nnemann. 2018. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In International Conference on Learning Representations

    2018

  9. [17]

    Marc Brockschmidt. 2020. Gnn-film: Graph neural networks with feature-wise linear modulation. In International Conference on Machine Learning, pages 1144--1152. PMLR

    2020

  10. [18]

    Shaked Brody, Uri Alon, and Eran Yahav. 2021. How attentive are graph attention networks? In International Conference on Learning Representations

    2021

  11. [19]

    Shaked Brody, Uri Alon, and Eran Yahav. 2022. http://arxiv.org/abs/2105.14491 How attentive are graph attention networks?

    2022 arXiv

  12. [20]

    Ceren Budak, Sharad Goel, and Justin M Rao. 2016. Fair and balanced? quantifying media bias through crowdsourced content analysis. Public Opinion Quarterly, 80(S1):250--271

    2016

  13. [21]

    Sonia Castelo, Thais Almeida, Anas Elghafari, A \'e cio Santos, Kien Pham, Eduardo Nakamura, and Juliana Freire. 2019. A topic-agnostic approach for identifying fake news pages. In Companion proceedings of the 2019 World Wide Web conference, pages 975--980

    2019

  14. [22]

    April Chen, Ryan A Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, and Nesreen K Ahmed. 2024. Fairness-aware graph neural networks: A survey. ACM Transactions on Knowledge Discovery from Data, 18(6):1--23

    2024

  15. [23]

    Djork-Arn \'e Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2016. Fast and accurate deep network learning by exponential linear units (elus). In International Conference on Learning Representations

    2016

  16. [24]

    Nadia K Conroy, Victoria L Rubin, and Yimin Chen. 2015. Automatic deception detection: Methods for finding fake news. Proceedings of the association for information science and technology, 52(1):1--4

    2015

  17. [25]

    Giovanni Da San Martino, Firoj Alam, Maram Hasanain, Rabindra Nath Nandi, Dilshod Azizov, and Preslav Nakov. 2023. Overview of the CLEF -2023 CheckThat ! lab task 3 on political bias of news articles and news media. In Working Notes of CLEF 2023--Conference and Labs of the Eva...

    2023

  18. [26]

    Rohan Das, Aditya Chandra, I-Ta Lee, and Maria Leonor Pacheco. 2024. Media framing through the lens of event-centric narratives. arXiv preprint arXiv:2410.03151

    2024 arXiv

  19. [27]

    Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, and Dan Jurafsky. 2019. Analyzing polarization in social media: Method and application to tweets on 21 mass shootings. arXiv preprint arXiv:1904.01596

    2019 arXiv

  20. [28]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

    2018 arXiv

  21. [29]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Associat...

    2019 doi

  22. [30]

    James Fairbanks, Natalie Fitch, Nathan Knauf, and Erica Briscoe. 2018. Credibility assessment in the news: do we need to read. In Proc. of the MIS2 Workshop held in conjuction with 11th Int’l Conf. on Web Search and Data Mining, pages 799--800. ACM

    2018

  23. [31]

    Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. arXiv preprint arXiv:1909.02670

    2019 arXiv

  24. [32]

    Matthias Fey. 2019. Just jump: Dynamic neighborhood aggregation in graph neural networks. arXiv preprint arXiv:1904.04849

    2019 arXiv

  25. [33]

    Matthias Fey, Jan E Lenssen, Frank Weichert, and Jure Leskovec. 2021. Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings. In International Conference on Machine Learning, pages 3294--3304

    2021

  26. [34]

    Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with pytorch geometric. In ICLR 2019 (RLGM Workshop)

    2019

  27. [35]

    Xiaobo Guo, Weicheng Ma, and Soroush Vosoughi. 2022. Measuring media bias via masked language modeling. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 1404--1408

    2022

  28. [36]

    Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30

    2017

  29. [37]

    Junxian He, Taylor Berg-Kirkpatrick, and Graham Neubig. 2020. Learning sparse prototypes for text generation. Advances in Neural Information Processing Systems, 33:14724--14735

    2020

  30. [38]

    Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021. Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing. arXiv preprint arXiv:2111.09543

    2021 arXiv

  31. [39]

    Austin Hounsel, Jordan Holland, Ben Kaiser, Kevin Borgolte, Nick Feamster, and Jonathan Mayer. 2020. Identifying disinformation websites using infrastructure features. In 10th USENIX Workshop on Free and Open Communications on the Internet (FOCI 20)

    2020

  32. [40]

    Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. https://api.semanticscholar.org/CorpusID:218487328 Open graph benchmark: Datasets for machine learning on graphs . ArXiv, abs/2005.00687

    2020 arXiv

  33. [41]

    Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros. 2018. Fighting fake news: Image splice detection via learned self-consistency. In Proceedings of the European conference on computer vision (ECCV), pages 101--117

    2018

  34. [42]

    Lalitha Kameswari and Radhika Mamidi. 2021. Towards quantifying magnitude of political bias in news articles using a novel annotation schema. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 671--678

    2021

  35. [43]

    Juho Kim and Michael Guerzhoy. 2024. Observing the southern us culture of honor using large-scale social media analysis. arXiv preprint arXiv:2410.13887

    2024 arXiv

  36. [44]

    Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations

    2015

  37. [45]

    Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

    2016 arXiv

  38. [46]

    Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. Neus: Neutral multi-news summarization for mitigating framing bias. arXiv preprint arXiv:2204.04902

    2022 arXiv

  39. [47]

    Yuanyuan Lei, Ruihong Huang, Lu Wang, and Nick Beauchamp. 2022. Sentence-level media bias analysis informed by discourse structures. In Proceedings of the 2022 conference on empirical methods in natural language processing, pages 10040--10050

    2022

  40. [48]

    Luyang Lin, Lingzhi Wang, Xiaoyan Zhao, Jing Li, and Kam-Fai Wong. 2024. Indivec: An exploration of leveraging large language models for media bias detection with fine-grained bias indicators. arXiv preprint arXiv:2402.00345

    2024 arXiv

  41. [49]

    Siyi Liu, Lei Guo, Kate Mays, Margrit Betke, and Derry Tanti Wijaya. 2019 a . Detecting frames in news headlines and its application to analyzing news framing trends surrounding us gun violence. In Proceedings of the 23rd conference on computational natural language learning (...

    2019

  42. [50]

    Siyi Liu, Hongming Zhang, Hongwei Wang, Kaiqiang Song, Dan Roth, and Dong Yu. 2023. Open-domain event graph induction for mitigating framing bias. arXiv preprint arXiv:2305.12835

    2023 arXiv

  43. [51]

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

    2019 arXiv

  44. [52]

    Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, and Lu Wang. 2022. POLITICS : pretraining with same-story article comparison for ideology prediction and stance detection. arXiv preprint arXiv:2205.00619

    2022 arXiv

  45. [53]

    Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Li \`o , Bruno Lepri, and Andrea Passerini. 2024. Explaining the explainers in graph neural networks: a comparative study. ACM Computing Surveys

    2024

  46. [54]

    Iffat Maab, Edison Marrese-Taylor, Sebastian Pad \'o , and Yutaka Matsuo. 2024. Media bias detection across families of language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technol...

    2024

  47. [55]

    Nikhil Mehta and Dan Goldwasser. 2023 a . An interactive framework for profiling news media sources. arXiv preprint arXiv:2309.07384

    2023 arXiv

  48. [56]

    Nikhil Mehta and Dan Goldwasser. 2023 b . Interactively learning social media representations improves news source factuality detection. arXiv preprint arXiv:2309.14966

    2023 arXiv

  49. [57]

    Nikhil Mehta, Mar \' a Leonor Pacheco, and Dan Goldwasser. 2022. Tackling fake news detection by continually improving social context representations using graph neural networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume...

    2022

  50. [58]

    Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning, pages 807--814

    2010

  51. [59]

    Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Rabindra Nath Nandi, Dilshod Azizov, and Panayot Panayotov. 2023 a . Overview of the CLEF -2023 CheckThat ! lab task 4 on factuality of reporting of news media. In Working Notes of CLEF 2023--Conference and La...

    2023

  52. [60]

    Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, RN Nandi, D Azizov, and P Panayotov. 2023 b . Overview of the clef-2023 checkthat! lab task 4 on factuality of reporting of news media. Working Notes of CLEF

    2023

  53. [61]

    Preslav Nakov, Jisun An, Haewoon Kwak, Muhammad Arslan Manzoor, Zain Muhammad Mujahid, and Husrev Taha Sencar. 2024. https://api.semanticscholar.org/CorpusID:271953866 A survey on predicting the factuality and the bias of news media . In Annual Meeting of the Association for C...

    2024

  54. [62]

    Panayot Panayotov, Utsav Shukla, Husrev Taha Sencar, Mohamed Nabeel, and Preslav Nakov. 2022. GREENER : Graph neural networks for news media profiling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP '22, Abu Dhabi, UAE

    2022

  55. [63]

    Ver \'o nica P \'e rez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2017. Automatic detection of fake news. arXiv preprint arXiv:1708.07104

    2017 arXiv

  56. [64]

    Meng Qu, Yoshua Bengio, and Jian Tang. 2019. Gmnn: Graph markov neural networks. In International Conference on Machine Learning, pages 5241--5250

    2019

  57. [65]

    Meng Qu, Huiyu Cai, and Jian Tang. 2021. Neural structured prediction for inductive node classification. In International Conference on Learning Representations

    2021

  58. [66]

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108

    2019 arXiv

  59. [67]

    Michael Schlichtkrull. 2024. Generating media background checks for automated source critical reasoning. arXiv preprint arXiv:2409.00781

    2024 arXiv

  60. [68]

    Jayaram Sethuraman. 1994. A constructive definition of dirichlet priors. Statistica sinica, pages 639--650

    1994

  61. [69]

    Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, and Akiko Aizawa. 2022. Neural media bias detection using distant supervision with BABE --bias annotations by experts. arXiv preprint arXiv:2209.14557

    2022 arXiv

  62. [70]

    Peter Stefanov, Kareem Darwish, Atanas Atanasov, and Preslav Nakov. 2020. https://doi.org/10.18653/v1/2020.acl-main.50 Predicting the topical stance and political leaning of media using tweets . In Proceedings of the 58th Annual Meeting of the Association for Computational Lin...

    2020 doi

  63. [71]

    Dahai Tang, Jiali Wang, Rong Chen, Lei Wang, Wenyuan Yu, Jingren Zhou, and Kenli Li. 2024. Xgnn: Boosting multi-gpu gnn training via global gnn memory store. Proceedings of the VLDB Endowment, 17(5):1105--1118

    2024

  64. [72]

    Filip Trhlik and Pontus Stenetorp. 2024. Quantifying generative media bias with a corpus of real-world and generated news articles. arXiv preprint arXiv:2406.10773

    2024 arXiv

  65. [73]

    Petar Veli c kovi \'c , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li \`o , and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations

    2018

  66. [74]

    Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. science, 359(6380):1146--1151

    2018

  67. [75]

    Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International Conference on Machine Learning, pages 6861--6871

    2019

  68. [76]

    Kai-Cheng Yang and Filippo Menczer. 2023. Large language models can rate news outlet credibility. arXiv preprint arXiv:2304.00228

    2023 arXiv

  69. [77]

    Mingqi Yang, Renjian Wang, Yanming Shen, Heng Qi, and Baocai Yin. 2022. Breaking the expression bottleneck of graph neural networks. IEEE Transactions on Knowledge and Data Engineering

    2022

  70. [78]

    Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Huan Xiong, and Bin Gu. 2024. Dynamic spiking graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 16495--16503

    2024

  71. [79]

    Ruihong Zeng, Jinyuan Fang, Siwei Liu, Zaiqiao Meng, and Shangsong Liang. 2024. Enhancing graph neural networks via memorized global information. ACM Transactions on the Web, 18(4):1--34

    2024

  72. [80]

    Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, and Xiaorui Liu. 2024. Linear-time graph neural networks for scalable recommendations. In Proceedings of the ACM on Web Conference 2024, pages 3533--3544

    2024

  73. [81]

    Jin Zhao, Jingxuan Tu, Han Du, and Nianwen Xue. 2024. Media attitude detection via framing analysis with events and their relations. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17197--17210

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.