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Area of Science:

  • Computer Science
  • Network Science
  • Mathematics
  • Societal Health

Background:

  • Internet misinformation poses a threat to social systems.
  • Existing research often overlooks the dynamic nature of social connections.
  • Temporal networks, where connections change over time, require new theoretical approaches.

Purpose of the Study:

  • To propose a novel theoretical approach for containing misinformation in temporal networks.
  • To introduce a heuristic-containing (HC) strategy optimized for minimizing outbreak size.
  • To evaluate the effectiveness of the HC strategy against existing methods.

Main Methods:

  • Developed a misinformation spreading model specifically for temporal networks.
  • Proposed a heuristic-containing (HC) strategy.
  • Conducted extensive numerical simulations and theoretical analyses on artificial and real-world networks.

Main Results:

  • The proposed HC strategy significantly outperforms random-containing and targeted-containing strategies.
  • The HC strategy effectively increases the outbreak threshold, making containment easier.
  • The HC strategy demonstrably decreases the final outbreak size.

Conclusions:

  • The new theoretical approach and HC strategy offer a robust solution for combating misinformation in dynamic social networks.
  • The HC strategy provides a significant advancement in controlling the spread of online falsehoods.
  • This research highlights the importance of considering network dynamics in misinformation containment efforts.