# Fake News Detection via Intermediate-Layer Emotional Representations

Yassine Toughrai, David Langlois, Kamel Smaïli

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Yassine Toughrai, David Langlois, Kamel Smaïli. Fake News Detection via Intermediate-Layer Emotional Representations. Companion Proceedings of the ACM on Web Conference 2025, Association for Computing Machinery, Apr 2025, Sydney, Australia. pp.2680-2684, 10.1145/3701716.3717537. hal-04977819

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Distributed under a Creative Commons Attribution 4.0 International License
Fake News Detection via Intermediate-Layer Emotional Representations

Yassine Toughrai

David Langlois

Kamel Smaïli

Laboratoire lorrain de recherche en
informatique et ses applications

Laboratoire lorrain de recherche en
informatique et ses applications

Laboratoire lorrain de recherche en
informatique et ses applications

Vandœuvre-lès-Nancy, France

Vandœuvre-lès-Nancy, France

Vandœuvre-lès-Nancy, France

Université de Lorraine

Université de Lorraine

Université de Lorraine

Nancy, France

Nancy, France

Nancy, France

yassine.toughrai@loria.fr

david.langlois@loria.fr

smaili@loria.fr

Abstract

This paper introduces Intermediate-Layer Emotional Representations (ILER), a novel emotional embedding technique designed to capture nuanced emotional patterns in textual data. While we demonstrate its utility in the domain of fake news detection, ILER was conceived as a generalizable framework to be applied across various tasks requiring emotion or sentiment analysis.

We aim to leverage the abstract and rich representations offered by ILER by combining publisher-driven emotions embedded within news content and social emotions derived from user comments. This approach seeks to capture the intricate emotional nuances often associated with fake news dissemination. Evaluations on benchmark datasets emulating live systems show a 5-point increase in F1-score and a 3.9-point increase in accuracy compared to state-of-the-art emotion representations, including Dual Emotion Features. Our findings underscore the potential of abstracted emotional representations to deepen context understanding, enabling more robust and precise emotionally assisted fake news detection.

CCS Concepts

* Information systems → Information retrieval; * Computing methodologies → Natural language processing; Neural networks.

Keywords

Fake News Detection; Emotion; Emotion Representation; Intermediate Layer Representations; Social Media

ACM Reference Format:

Yassine Toughrai, David Langlois, and Kamel Smaïli. 2025. Fake News Detection via Intermediate-Layer Emotional Representations. In *Companion Proceedings of the ACM Web Conference 2025 (WWW Companion '25)*, April 28-May 2, 2025, Sydney, NSW, Australia. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3701716.3717537

1 Introduction

The role of emotions in detecting fake news on social media is
increasingly recognized as vital, as emotions can influence both the

spread of false information and our ability to discern its veracity. Studies show that emotionally charged content is more likely to be shared and believed, regardless of its accuracy [8]. This is in part because elevated emotions can disrupt critical thinking, making people less likely to carefully examine content [10]. Furthermore, people are more susceptible to fake news that aligns with their emotional biases, indicating that strong emotional reactions to a topic can diminish the critical evaluation of its precision [12].

Interestingly, analyzing emotional reactions to content through comments, likes, and shares can also help detect fake news. Studies indicate that fake news often triggers intense, polarized responses, which can serve as red flags [2]. For instance, exaggerated emotional reactions can indicate content intentionally designed to manipulate, serving as important signals for both algorithms and human reviewers in detecting misinformation. Therefore, understanding the emotional triggers associated with fake news can help both platforms and users develop better tools and strategies to identify and prevent the spread of misinformation. To improve fake news detection, the combination of emotions expressed in both news content and user reactions, such as comments, has shown promise, as demonstrated by Zhang et al. [13]. Our approach incorporates emotional signals from both the article and the responses on social media using higher-order abstract emotional representations to capture intricate emotional nuances within content and user reactions. We argue that, our approach, called Intermediate-Layer Emotional Representation (ILER) generates more refined emotional representations, enhancing contextual comprehension and facilitating the detection of fake news on social media platforms.

2 Related Works

Traditionally, fake news detection methods focused primarily on linguistic characteristics, metadata, or network-based detection strategies. However, incorporating an emotional dimension into detection systems has proven to enhance their effectiveness in identifying and categorizing fake content. Misinformation often carries distinct emotional signals, such as increased fear, anger, or enthusiasm, that separate it from truthful content. By analyzing these emotional signals, systems can better understand the psychological and social mechanisms behind the spread of fake news.

Ajao et al. [1] highlighted a relationship between news veracity and the sentiments expressed within text, incorporating a sentiment-based feature, Emoratio, specifically the ratio of negative to positive words, to enhance the accuracy of text-only fake news detectors. Moving beyond including a single sentiment metric, Giachanou et

<figure><img src="image_2.png" /></figure>

This work is licensed under a Creative Commons Attribution 4.0 International License.
*WWW Companion '25, Sydney, NSW, Australia*
© 2025 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-1331-6/2025/04
https://doi.org/10.1145/3701716.3717537
al. [4] proposed extracting more comprehensive emotional features, EmoCred, from news content using emotional lexicons to improve fake news detection performance. Despite these advancements, most existing works have primarily focused on the emotional signals conveyed by the publishers within fake news content, while the emotional reactions expressed in user comments have often been overlooked. Zhang et al. [13] came up with the Dual Emotion Feature that leverages both the publisher's emotion and the emotional reaction elicited in the comments. Since fake news often seeks to spread virally by evoking high-arousal emotions among readers [11], examining both the emotions embedded in the news content and those expressed in user comments, as well as the relation between these two emotional dimensions, is crucial to improve fake news detection systems.

## 3 Emotion Enhanced Fake News Classification

Social media news content often provokes various emotional reactions, especially in user comments. These emotional signals, when analyzed effectively, can serve as critical markers for detecting fake news. In this work, we use contextual representations from news content alongside the emotional reactions it elicits, leveraging intermediate-layer outputs from a BERT-based emotion classifier. By combining content-driven and user-driven emotional cues, this approach enhances the robustness and accuracy of fake news detection systems.

We propose a model that combines a multi-layer perceptron (MLP) classifier with a BERT-based classifier at the logits level to enhance fake news detection. In this setup, the BERT [3] classifier processes the linguistic content to capture contextual and semantic nuances, while the MLP classifier leverages emotional embeddings derived from both the content and user reactions. By merging the logits from both classifiers, our model benefits from a richer understanding of the text's emotional and contextual signals, allowing it to capture patterns characteristic of fake news more effectively. This logit-level combination enables the model to achieve higher accuracy and robustness by integrating the complementary strengths of both classifiers.

In order to obtain our emotional embeddings, we train a BERT classifier on an emotion classification task. We construct a classification head with a single intermediate layer (Figure 1) of size 64, which we place on top of a pretrained BERT model to obtain intermediate representations of emotions. This 64-unit dense layer bridges the BERT embeddings and the final output, allowing us to capture nuanced, high-level features specific to our classification task. Using the output of this intermediate layer, we obtain representations that retain abstract representations of the data, especially the emotional and contextual cues necessary to distinguish real and fake news.

BERT was selected to ensure comparability with prior work, such as Dual Emotion Features that uses BERT as a baseline. While advanced models like RoBERTa and DeBERTa may offer additional performance gains, this study focuses on isolating ILER's contributions. Future work will explore ILER's integration with these advanced models.

<figure><img src="image_3.png" /><figcaption>Figure 1: Classification Head with Intermediate Layer</figcaption></figure>

### 3.1 Emotion Modeling via Intermediate Network Representations

To generate intermediate representations within the classification head, we process the output from the BERT model through a fully connected neural network.

Let $H_{\text{BERT}} \in \mathbb{R}^{l \times h}$ represent the output of the BERT model and $H_{\text{CLS}} \in \mathbb{R}^h$ represent the BERT output for the '[CLS]' token, where $l$ is the length of the encoded sequence and $h$ is the hidden dimension of the BERT output.

$$H_{\text{CLS}} = H_{\text{BERT}}[0] \qquad (1)$$

We propose a custom BERT classification head for the emotion classification task, consisting of a fully connected two-layer perceptron to transform the outputs.

* $W_1 \in \mathbb{R}^{d \times h}$ be the weight matrix for the intermediate layer, where $d$ is the layer size (In our case $d = 64$),

* $b_1 \in \mathbb{R}^d$ be the bias vector.

* $W_2 \in \mathbb{R}^{c \times d}$ be the weight matrix for the outer layer, where $c$ is the output size,

* $b_2 \in \mathbb{R}^c$ be the bias vector.

The output of the two-layer classification head is defined as:

$$\text{logits} = W_2 \cdot \text{ReLU}(W_1 H_{\text{CLS}} + b_1) + b_2 \qquad (2)$$

Therefore, the output from the intermediate layer $H_{\text{int}}$, which we aim to use, is:

$$H_{\text{int}} = \text{ReLU}(W_1 H_{\text{CLS}} + b_1) \in \mathbb{R}^d \qquad (3)$$

### 3.2 Emotion-Driven Fake News Classification through Intermediate Representations

Let the set of comments associated with the news article C be represented as:

$$\mathcal{R} = [\mathcal{R}_1, \mathcal{R}_2, \dots, \mathcal{R}_{L_R}]$$

where $\mathcal{R}_i$ denotes the $i^{th}$ comment and $L_R$ is the total number of comments. The emotional representation $H_{\text{int}}$ is calculated for each comment. These individual emotional representations are
<table><caption>Table 1: Results on RumorEval19. C indicates that prediction is based on the piece of news, and R indicates that prediction is also based on comments.</caption><thead><tr><th rowspan="2">Source</th><th rowspan="2">Method</th><th rowspan="2">Macro F1-Score</th><th colspan="3">F1-Score</th></tr><tr><th>Fake News</th><th>Real News</th><th>Unverified News</th></tr></thead><tbody><tr><td>C</td><td>Emoratio</td><td>0.185</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C</td><td>EmoCred</td><td>0.253</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C + R</td><td>Dual Emotion Features</td><td>0.337</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C</td><td>BERT</td><td>0.272</td><td>0.533</td><td>0.105</td><td>0.176</td></tr><tr><td>C</td><td>BERT + Emoratio</td><td>0.271</td><td>0.466</td><td>0.240</td><td>0.167</td></tr><tr><td>C</td><td>BERT + EmoCred</td><td>0.309</td><td>0.370</td><td>0.367</td><td>0.189</td></tr><tr><td>C + R</td><td>BERT + Dual Emotion Features</td><td>0.346</td><td><strong>0.557</strong></td><td>0.244</td><td>0.238</td></tr><tr><td>C + R</td><td>ILER</td><td><strong>0.371</strong></td><td>0.406</td><td><strong>0.394</strong></td><td><strong>0.312</strong></td></tr></tbody></table>

then stacked to form the complete emotion matrix for the set of comments $\overline{H}_{\text{int}}^{\mathcal{R}}$.

$$ \overline{H}_{\text{int}}^{\mathcal{R}}, \mathcal{T} = H_{\text{int}}^{\mathcal{R}_1}, \mathcal{T} \oplus H_{\text{int}}^{\mathcal{R}_2}, \mathcal{T} \oplus \cdots \oplus H_{\text{int}}^{\mathcal{R}_{L_R}}, \mathcal{T} \quad (4) $$

Where $\oplus$ is the concatenation operator and $\overline{H}_{\text{int}}^{\mathcal{R}} \in \mathbb{R}^{d \times L_R}$.

We then utilize max pooling to aggregate the matrix into a single representative vector of size $d$, thus reducing its dimensionality.

$$ \overline{H}_{\text{int}}^{\mathcal{R}_{\text{pooled}}} = \text{pool}(\overline{H}_{\text{int}}^{\mathcal{R}}) \quad (5) $$

After training an MLP $MLP_1$ for the classification task, we obtain the resulting logits:

$$ \text{logits}_{\text{MLP}_1} = \text{MLP}_1(\overline{H}_{\text{int}}^{\mathcal{R}_{\text{pooled}}} \oplus H_{\text{int}}^{\mathcal{C}}) \quad (6) $$

Let $\text{logits}_{\text{BERT}}$ be the logits of a BERT Classifier trained to classify $C$. We combine the BERT classifier logits and our $MLP_1$ logits and pass them onto a second MLP $\text{MLP}_2$:

Let $\text{logits}_{\text{BERT}}$ represent the logits of a BERT classifier trained to classify $R$. We then combine the logits from the BERT classifier with those of our $MLP_1$, and pass the combined logits into a second MLP, denoted as $\text{MLP}_2$:

$$ \hat{y} = \text{Softmax}(\text{MLP}_2(\text{logits}_{\text{MLP}_1} \oplus \text{logits}_{\text{BERT}})) \quad (7) $$

Here $\hat{y}$ denotes the predicted labels.

Logit-level fusion was chosen for its effectiveness in integrating complementary signals from ILER and BERT. The high dimensionality of BERT representations compared to ILER's emotional embeddings makes direct combination challenging, often leading to redundancy or overfitting. Logit-level fusion mitigates these issues by enabling efficient and balanced integration of emotional and contextual cues. This strategy was selected based on extensive experimentation, although detailed results are omitted here due to space constraints.

# 4 Experiments and Results

## 4.1 Datasets

We will use the **Weibo-20** and **RumorEval2019** datasets as benchmarks for our method, comparing it with state-of-the-art approaches, as these datasets are widely recognized in the literature.

RumorEval2019, introduced at SemEval-2019 [5] (ACL), is widely recognized as a benchmark for the detection of fake news. Its use ensures comparability with previous work while demonstrating the robustness of ILER in resource-constrained scenarios. The dataset contains 446 real, fake, and unverified English news pieces and 7,993 associated comments, focusing on conversations primarily from Twitter.

Weibo-20 contains a selection of real and fake news pieces from Weibo, each labeled for veracity and accompanied by user comments, enabling analysis based on both content and social responses. We also use a temporal split of Weibo-20 where the most recent 20% of news pieces form the test set, simulating a real-world scenario with new unseen content. This setup tests a model's ability to generalize and handle emerging news topics.

## 4.2 Experimental Setup

We use two distinct BERT models, one for English<fnref n="1" /> and one for Chinese<fnref n="2" />, both available on Hugging Face. For English emotion classification, we trained a BERT classifier that supports six emotion categories following Parrot's emotion framework [9]: happiness, sadness, anger, fear, surprise, and disgust. For Chinese, the classification is simplified to three sentiment labels: positive, negative, and neutral, reflecting the commonly used sentiment classification scheme in Chinese datasets. Each news article is limited to a maximum of 512 tokens, while comments are limited to 150 tokens, with a maximum of 100 comments per news article.

## 4.3 Results

To evaluate our method, we compare it with the top-performing state-of-the-art emotion representation method, **Dual Emotion Feature**, introduced by X. Zhang et al. [13]. In addition, we include the baseline methods, described briefly below, used in their study to provide a more comprehensive comparison of the results.

* **Emoratio [1]**: an emotion feature that determines the emotional tone by calculating the ratio of negative to positive emotional words in the content of news pieces.

* **EmoCred [4]**: uses emotional lexicons and intensity features of the content text based on the frequency of lexicon occurrences.

<footnote n="1">¹https://huggingface.co/google-bert/bert-base-uncased</footnote>

<footnote n="2">²https://huggingface.co/google-bert/bert-base-chinese</footnote>
* Dual Emotion Feature [13]: leverages both Publisher Emotion and Social Emotion. This dual approach aims to capture emotion resonance and emotion dissonance. It includes three key components:

- Publisher Emotion: represented through diverse emotional features such as emotion categories, intensity, and sentiment scores;

- Social Emotion: aggregated from comment emotions using mean and max pooling techniques;

- Emotion Gap: which quantifies the degree of resonance or dissonance between publisher and social emotions.

The results in Table 1 on the RumourEval2019 dataset<fnref n="3" /> indicate that ILER method achieves a Macro F1-score of 0.371, outperforming baseline approaches, including Dual Emotion Features, which scored 0.346. Notably, BERT alone performs relatively poorly with a Macro F1-score of 0.272, highlighting its limitations in this context. We suspect that BERT's lower performance may be attributed to the small size of the RumourEval2019 dataset, which comprises only 446 instances. In contrast, emotion-driven representations, such as ILER, appear to leverage richer and more nuanced emotional cues, providing a better fit for this limited dataset size. These findings underscore the potential advantage of incorporating emotional representations in resource-constrained scenarios.

ILER's intermediate-layer representations capture nuanced emotional patterns that enhance its ability to generalize, even in resource-constrained scenarios. This capability highlights ILER's robustness and suitability for small datasets like RumourEval19.

<table><caption>Table 2: Results on Weibo-20</caption><thead><tr><th>Source</th><th>Method</th><th>Predictor</th><th>F1-Score</th><th>Acc</th></tr></thead><tbody><tr><td>C</td><td>BERT</td><td>–</td><td>90.4</td><td>90.4</td></tr><tr><td>C</td><td>Emoratio</td><td>Log Reg</td><td>53.4</td><td>54.6</td></tr><tr><td>C</td><td>EmoCred</td><td>MLP</td><td>53.6</td><td>54.9</td></tr><tr><td>C + R</td><td>Dual Emotion Feature</td><td>MLP</td><td>75.7</td><td>75.9</td></tr><tr><td>C + R</td><td>ILER</td><td>MLP</td><td>83.1</td><td>83.1</td></tr><tr><td>C</td><td>BERT + Emoratio</td><td>–</td><td>90.1</td><td>90.1</td></tr><tr><td>C</td><td>BERT + EmoCred</td><td>–</td><td>90.1</td><td>90.2</td></tr><tr><td>C + R</td><td>BERT + Dual Emotion</td><td>–</td><td>91.5</td><td>91.5</td></tr><tr><td>C + R</td><td>BERT + ILER</td><td>–</td><td>92.1</td><td>92.2</td></tr></tbody></table>

ILER applied to the Weibo-20 dataset (Table 2), outperforms all other approaches that rely solely on emotional features with an MLP classifier, achieving an F1-score and accuracy of 83.1. In comparison, the Dual Emotion Feature method reaches an F1-score of 75.7 and an accuracy of 75.9. EmoRatio and EmoCred perform at a much lower level, with F1-scores of 53.4 and 53.6 and accuracies of 54.6 and 54.9. These results highlight the superior ability of our approach to capture and utilize emotional nuances for the classification task.

The combination of ILER and a BERT classifier outperforms all other emotion representation techniques, achieving the highest F1-score and accuracy of 92.1 and 92.2, respectively. In contrast, the

Dual Emotion Feature method paired with BERT achieves an F1-score and accuracy of 91.5. The combination of BERT with EmoCred and EmoRatio results in slightly lower performance, with F1-scores of 90.1 and accuracies of 90.2 and 90.1, respectively.

<table><caption>Table 3: Results on Weibo-20 Temporal</caption><thead><tr><th>Source</th><th>Method</th><th>Predictor</th><th>F1-Score</th><th>Acc</th></tr></thead><tbody><tr><td>C</td><td>BERT</td><td>–</td><td>71.3</td><td>72.4</td></tr><tr><td>C</td><td>Emoratio</td><td>Log Reg</td><td>56.1</td><td>57.3</td></tr><tr><td>C</td><td>EmoCred</td><td>MLP</td><td>53.4</td><td>53.8</td></tr><tr><td>C + R</td><td>Dual Emotion Feature</td><td>MLP</td><td>65.9</td><td>67.1</td></tr><tr><td>C + R</td><td>ILER</td><td>MLP</td><td>70.9</td><td>71.0</td></tr><tr><td>C</td><td>BERT + Emoratio</td><td>–</td><td>71.1</td><td>72.0</td></tr><tr><td>C</td><td>BERT + EmoCred</td><td>–</td><td>71.6</td><td>72.4</td></tr><tr><td>C + R</td><td>BERT + Dual Emotion</td><td>–</td><td>72.6</td><td>72.9</td></tr><tr><td>C + R</td><td>BERT + ILER</td><td>–</td><td>74.5</td><td>75.5</td></tr></tbody></table>

The results on the Weibo-20 Temporal dataset are given in Table 3.

We find that using emotional features alone yields performance levels close to those of the BERT classifier. Moreover, integrating emotional features with the BERT classifier boosts classification performance, improving both the F1-score and accuracy by more than 3 points.

This effect is evident in the combination of the Dual Emotion Feature with the BERT classifier, which increases accuracy from 72.4 to 72.9 and the F1-score from 71.3 to 72.6. When using our own emotion representations, classification performance improves even further, achieving an accuracy of 75.5 and an F1-score of 74.5.

ILER's use of intermediate-layer representations enables it to capture richer emotional signals compared to relevant and actively used techniques such as Dual Emotion Features [6, 7]. Unlike prior approaches that rely on predefined criteria or fixed features, ILER dynamically derives its representations from intermediate layers of BERT, enabling it to abstract emotional nuances which are difficult to capture with static methods.

## 5 Conclusion

Traditional emotion labels capture only simplified interpretations of abstract concepts like emotions, potentially missing the full richness and variability that neural representations can achieve. By adopting ILER, we aim to better capture these subtle nuances, which enables a more sophisticated understanding of emotional cues. Such higher-order representations allow for enhanced applications, such as improving the detection of fake news through deeper emotional insights or refining human-computer interactions to be more responsive to emotional subtleties. Future work will focus on further validating and expanding its generalizability beyond fake news detection.

## Acknowledgments

We acknowledge the AID and ANR for their invaluable financial support, which made this research for TRADEF project endeavor

<footnote n="3">³Results for methods other than ILER on RumorEval19 are reproduced from [13]</footnote>
possible. The guidance and insights provided were instrumental in
the successful execution of our study.

References

[1] Oluwaseun Ajao, Deepayan Bhowmik, and Shahrzad Zargari. 2019. Sentiment Aware Fake News Detection on Online Social Networks. In *Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*. IEEE, 2507–2511.

[2] William J Brady, Julian A Wills, John T Jost, Joshua A Tucker, and Jay J Van Bavel. 2017. Emotion shapes the diffusion of moralized content in social networks. *Proceedings of the National Academy of Sciences* 114, 28 (2017), 7313–7318.

[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In *Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*. Association for Computational Linguistics, 4171–4186.

[4] Anastasia Giachanou, Paolo Rosso, and Fabio Crestani. 2019. Leveraging Emotional Signals for Credibility Detection. In *Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)*. ACM, 877–880.

[5] Genevieve Gorrell, Elena Kochkina, Maria Liakata, Ahmet Aker, Arkaitz Zubiaga, Kalina Bontcheva, and Leon Derczynski. 2019. SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours. In *Proceedings of the 13th International Workshop on Semantic Evaluation*, Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, and Saif M. Mohammad (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, USA, 845–854. doi:10.18653/v1/S19-2147

[6] Quanjiang Guo, Zhao Kang, Ling Tian, and Zhouguo Chen. 2023. TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection. In *2023 International Joint Conference on Neural Networks (IJCNN)*. 1–7. doi:10.1109/IJCNN54540.2023.10191858

[7] Fei Liu, Xinsheng Zhang, and Qi Liu. 2024. An Emotion-Aware Approach for Fake News Detection. *IEEE Transactions on Computational Social Systems* 11, 3 (2024), 3516–3524. doi:10.1109/TCSS.2023.3335269

[8] Cameron Martel and Gordon Pennycook. 2020. Reliance on emotion promotes belief in fake news. *Cognitive research: principles and implications* (2020).

[9] W. Gerrod Parrott. 2001. *Emotions in Social Psychology: Essential Readings*. Psychology Press.

[10] Gordon Pennycook and David G. Rand. 2019. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. *Cognition* 188 (2019), 39–50. The Cognitive Science of Political Thought.

[11] Ralph L Rosnow. 1991. Inside rumor: A personal journey. *American Psychologist* 46, 5 (1991), 484.

[12] Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. *Science* (2018).

[13] Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021. Mining Dual Emotion for Fake News Detection. In *Proceedings of the Web Conference 2021 (WWW '21)*. ACM.