Rumors clarification with minimum credibility in social networks

Area of Science:

• Information Science
• Computational Social Science
• Epidemiology

Background:

• The COVID-19 pandemic generated overwhelming information, including widespread rumors that influenced public belief.
• Assessing user credibility is crucial for understanding influence and combating misinformation effectively.

Purpose of the Study:

• To address the challenge of minimizing credibility cost for rumor clarification within a specific timeframe.
• To develop an efficient method for identifying key individuals to disseminate accurate information during health crises.

Main Methods:

• Introduced the 'minimum credibility rumor clarifying' problem, focusing on seed set selection.
• Proposed the Longest-Effective-Hops (LEH) algorithm, designed for scenarios where users can be influenced multiple times.
• Conducted theoretical analysis to establish the algorithm's universality and effectiveness.

Main Results:

• The LEH algorithm demonstrated superior efficiency in both time and performance compared to existing state-of-the-art methods.
• Theoretical analysis confirmed the algorithm's effectiveness in minimizing credibility while maximizing information influence.
• Experimental results validated the algorithm's practical applicability in rumor clarification scenarios.

Conclusions:

• The LEH algorithm offers an effective and efficient solution for combating misinformation by strategically utilizing credible influencers.
• This approach is vital for public health communication, especially during widespread health emergencies like the COVID-19 pandemic.
• The study highlights the importance of considering credibility and influence dynamics in information dissemination strategies.