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1373. A new centrality measure for misinformation diffusion
Invited abstract in session TD-28: Advancements of OR-analytics in statistics, machine learning and data science 7, stream Advancements of OR-analytics in statistics, machine learning and data science.
Tuesday, 14:30-16:00Room: 065 (building: 208)
Authors (first author is the speaker)
| 1. | Safiye Şeyma Kaya Gezmiş
|
| 2. | Zeynep Aksin
|
| Koc University | |
| 3. | Barış Yıldız
|
| Industrial Engineering, Koç Üniversity |
Abstract
Social networks offer the capability to spread inspiring ideas and adapt innovations at unprecedented speed and convenience. Although such an information spreading capability is invaluable, social networks can also rapidly disseminate misinformation to a large number of people, with dire consequences for impacted individuals and societies. In this study, we focus on the spread of a specific type of misinformation: acute rumors (AR), which we define as misinformation with the potential to quickly spread in the network and mobilize individuals to take harmful actions. We present a new diffusion model for AR, which extends the classical linear threshold model by considering the base idea of the individuals regarding the topic of the rumor and the emotional impact its content creates on them. Based on this diffusion model, we propose a new centrality measure that can accurately detect individuals with a high potential to enhance the spread of AR. In a comprehensive numerical study, we evaluate the performance of our proposed centrality measure by comparing it to existing traditional centrality measures in the literature. Our results attest to the superior performance of our centrality measure not only in terms of detecting the individuals with the highest potential to increase the reach of AR but also in finding the correct ranking among the individuals in the network regarding their potential to contribute significantly to the dissemination of acute rumors.
Keywords
- Social Networks
- Service Operations
- OR and the Internet
Status: accepted
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