Analysis of Twitter Hashtags in the 2022 French Election

Belangrijkste concepten

The author proposes using semantic networks as user-level features for machine learning tasks, demonstrating their effectiveness in predicting social media responses based on Twitter hashtags related to the 2022 French presidential election.

Samenvatting

The study explores the use of semantic networks derived from Twitter hashtags to predict user response rates based on emotions like anger, enjoyment, or disgust. By transforming a bipartite graph into a maximum-spanning tree and creating vector features for each user, the semantic feature outperforms baseline methods in regression experiments.

Statistieken

A bipartite graph is formed with 1037 Twitter hashtags from 3.7 million tweets.
Most emotions have R2 above 0.5 with the semantic feature.
The dataset contains 1037 hashtags and 389,187 users.

Citaten

"The semantic feature performs well with regression, with most emotions having R2 above 0.5."
"Our semantic feature could be considered for use in further experiments predicting social media response on big datasets."

Belangrijkste Inzichten Gedestilleerd Uit

Analyzing Trendy Twitter Hashtags in the 2022 French Election

by Aamir Mandvi... om arxiv.org 03-01-2024

https://arxiv.org/pdf/2310.07576.pdf

Diepere vragen

How can the findings of this study be applied to other social media platforms beyond Twitter?

The methodology outlined in this study, which involves creating a semantic network based on user interactions with hashtags, can be adapted for use on other social media platforms like Facebook or Instagram. By analyzing user engagement with specific topics or keywords across different platforms, researchers can gain insights into public sentiment and behavior trends that transcend individual platforms. This cross-platform analysis could provide a more comprehensive understanding of user behavior and preferences in the digital space.

What potential limitations or biases might arise when using semantic networks for social media predictions?

One potential limitation is the inherent bias in the data collected from social media platforms. Users may not always represent a diverse range of demographics or opinions, leading to skewed results. Additionally, semantic networks rely heavily on text-based interactions (such as hashtags), which may not capture nuanced sentiments expressed through images or videos. There is also a risk of oversimplification in assuming that user interactions with certain hashtags accurately reflect their true beliefs or emotions.

How could incorporating additional contextual information enhance the accuracy of predicting user responses?

To improve prediction accuracy, incorporating additional contextual information such as user demographics, location data, and past behavior patterns could provide a more holistic view of users' online activities. By integrating these factors into the analysis alongside semantic networks, researchers can create more personalized models that account for individual differences and preferences. Furthermore, considering temporal factors like trending topics or external events could help capture real-time fluctuations in user responses and sentiment accurately.