Analyzing Information Dissemination Model Based on User Attitude and Public Opinion Environment

核心概念

The author proposes the UAPE model to consider dynamic user attitudes and public opinion environment in information dissemination, achieving higher accuracy than existing research.

摘要

The content discusses the UAPE model for information dissemination in social networks. It addresses the dynamic nature of user attitudes and the influence of public opinion on dissemination trends. The model considers multiple users' joint impact as part of the public opinion environment. Experimental results show UAPE outperforms existing models with an accuracy range of 91.62% to 94.01%. The study emphasizes accurately predicting dissemination trends in online social networks to respond effectively to emergent events.

统计

Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01%
Datasets I, II, III contain original tweets and comments related to a particular topic, respectively.
Dataset IV contains original tweets on two specific topics and comments related to them.
Dataset VI contains all original Weibo posts and comments on three topics.

引用

"The growth of online social networking has minimized geographical constraints on information dissemination."
"UAPE regards the user’s attitude towards the topic as dynamically changing."
"Extensive experiments demonstrate that UAPE can achieve more satisfactory prediction accuracy on the dissemination trend."

从中提取的关键见解

Information Dissemination Model Based on User Attitude and Public Opinion Environment

by Xinyu Li,Jin... 在 arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06141.pdf

更深入的查询

How can considering dynamic changes in user attitudes improve prediction accuracy in information dissemination

Considering dynamic changes in user attitudes can significantly improve prediction accuracy in information dissemination by capturing the evolving nature of user opinions. Users' attitudes towards topics are not static; they can shift based on various factors like new information, interactions with other users, or changing societal trends. By incorporating these dynamic changes into models like UAPE, we can better simulate how information spreads through social networks. This allows for a more nuanced understanding of how different user attitudes interact and influence each other over time, leading to more accurate predictions of information dissemination trends.

What are the potential implications of overlooking public opinion environment in modeling information dissemination

Overlooking the public opinion environment in modeling information dissemination can have several potential implications. Firstly, it may lead to inaccurate predictions as the broader sentiment surrounding a topic plays a crucial role in shaping individual user attitudes and behaviors. Ignoring public opinion dynamics could result in missing out on key influencers or trends that drive the spread of information within a network. Additionally, without considering the public opinion environment, models may fail to capture group dynamics and polarization effects that heavily impact how information is received and shared among users. Ultimately, neglecting this aspect could limit the effectiveness and reliability of predictive models for information dissemination.

How might understanding attitude persistence contribute to predicting information dissemination trends

Understanding attitude persistence is essential for predicting information dissemination trends because it provides insights into how likely users are to maintain their stance on specific topics over time. By quantifying node persistence towards certain attitudes using algorithms like Ai
v = Ai
v - q X u=1 |tiu-tiv|*Pi(u,v)-(tiu ⊕ tiv)*Pi(u,v), we can track how resistant individuals are to changing their opinions despite being exposed to diverse viewpoints or new pieces of information during an ongoing dissemination process.
This knowledge helps modelers anticipate which nodes might act as key influencers due to their steadfast beliefs or conversely identify nodes susceptible to attitude shifts based on external influences within the network.
By factoring in attitude persistence alongside dynamic changes in user opinions and public opinion environments, predictive models gain a more comprehensive understanding of how ideas propagate through social networks and evolve over time accurately