# French Fake News Propagation : The dual aspect of spurious information spreading online

Matthieu Bachelot, Thomas A. Epalle, Romain Billot, Inna Lyubareva, Raphaël-David Lasseri

► To cite this version:

Matthieu Bachelot, Thomas A. Epalle, Romain Billot, Inna Lyubareva, Raphaël-David Lasseri. French Fake News Propagation : The dual aspect of spurious information spreading online. Complex Network 2023, Nov 2023, Menton, France. COMPLEX NETWORKS 2023 - Book of Abstracts, 2023. hal-05005277

HAL Id: hal-05005277

https://hal.science/hal-05005277v1

Submitted on 25 Mar 2025

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<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

## Objectives and Motivations

Fake news propagation network study
for both Twitter and articles Data

Creation of a French Fake news
event-based dataset due to lack of data [1]

Comparative analysis with prior work on
English datasets

## Data Collection

1. French event-based dataset between 06/2021 and 12/2022

2. 3600 articles from mainstream (National and Local), partisan and specialized media and blogs

3. 1 Millions Tweets (Retweets / Replies / Quoted)

<table><thead><tr><th>Type</th><th># Event</th><th># Articles</th><th># Tweets</th></tr></thead><tbody><tr><td>Fake</td><td>5</td><td>852</td><td>433896</td></tr><tr><td>Rumor</td><td>3</td><td>753</td><td>135829</td></tr><tr><td>News</td><td>5</td><td>956</td><td>205736</td></tr><tr><td>Stories</td><td></td><td></td><td></td></tr><tr><td>Scandal</td><td>2</td><td>1018</td><td>381693</td></tr></tbody></table>

Dataset Overview

## Methodology

<figure><img src="image_3.png" /><figcaption>Graph Generation Methods</figcaption></figure>

**Graph characteristics** : Weighted by delays & undirected.
**Three levels of analysis** : Microscopic (Centrality, 4 features) / Mesoscopic (Community, 4 features) / Macroscopic (Graph level, 7 features for Twitter, 4 for Articles)

## Hypotheses based on English Literature's Results

True News spread in a "centralized" manner [2]

Fake News graphs are more connected [3]

## Twitter Graph Visualisation

**Twitter interactions visualisation:** Highlights the preferred user' interactions (quote, retweet or retweet) given the event type.

**Influential users definition** [3]

1. They have at least 1000 followers
2. They have more followers than following

<figure><img src="image_4.png" /><figcaption>Graph visualisation with respect to the event type</figcaption></figure>

## Results : Twitter Graphs

**News Consumer Dynamic** :

1. Gives an overview of information spreading by users and the communities dynamics

2. Who, where and how many influential users there are in the event

<figure><img src="image_5.png" /><figcaption>Box plots of Twitter propagation graphs features</figcaption></figure>

## Results : Articles Graphs

**News Production Dynamic** :

1. Closeness indicates time-until-arrival of the information

<figure><img src="image_6.png" /><figcaption>Focus on communities (media categories) and centralities</figcaption></figure>

<table><thead><tr><th>Type</th><th>Average edge to node ratio</th></tr></thead><tbody><tr><td>Fake</td><td>7.6</td></tr><tr><td>Rumor</td><td>6.5</td></tr><tr><td>News Stories</td><td>5.2</td></tr><tr><td>Scandal</td><td>13.43</td></tr></tbody></table>

Article graphs semantic analysis

## Conclusions

For all event types, the producer dynamic and the consumers dynamic are different.

Hypotheses are verified for True and Fake news propagation on social media, but a fine-grained event type definition slightly alleviates the results.

For articles propagation, results indicate that Fake news express less diverse content over a longer period of time. True news events draw more attention overall, and express more diverse content from different sources types.

## References

[1] Taichi Murayama. Dataset of Fake News Detection and Fact Verification: A Survey, November 2021. Number: arXiv:2111.03299 arXiv:2111.03299 [cs].
[2] Amirhosein Bodaghi and Jonice Oliveira. The theater of fake news spreading, who plays which role? A study on real graphs of spreading on Twitter. *Expert Systems with Applications*, 189:116110, March 2022.
[3] Mansour Davoudi, Mohammad R. Moosavi, and Mohammad Hadi Sadreddini. DSS: A hybrid deep model for fake news detection using propagation tree and stance network. *Expert Systems with Applications*, 198:116635, July 2022.