Soft rumor control in social networks: Modeling and analysis. (April 2021)
- Record Type:
- Journal Article
- Title:
- Soft rumor control in social networks: Modeling and analysis. (April 2021)
- Main Title:
- Soft rumor control in social networks: Modeling and analysis
- Authors:
- Askarizadeh, Mojgan
Tork Ladani, Behrouz - Abstract:
- Abstract: Nowadays, social networks become ubiquitous platforms for sharing and diffusing information around the world. However, spreading rumors as unverified and opaque information in social networks causes harmful damages to societies. An approach for combating rumors in social networks is to use soft control mechanisms i.e. enhancing the people's knowledge and awareness against the rumor to persuade them avoiding rumor dissemination. In this paper, we propose a soft rumor control model in which people refer to their trusted friends or ask the reputable authorities about the rumor to avoid rumor spreading. The model includes a method for selecting consultants who are both expert in rumor context and responsive to queries about rumors by the user. The battlespace between rumor and anti-rumor spreaders is then modeled as an evolutionary game to analyze the controls' effectiveness. To evaluate the proposed model, we use Pheme dataset of tweets and conduct simulation analysis. It is shown that trusted consultants suggested by the model with high precision are the same users who send anti-rumor messages in real world. Furthermore, we analyze and compare soft rumor control methods on societies with different assumed cyber literacy and habits. Moreover, it is interestingly shown that using soft rumor control mechanisms in some situations outperforms traditional hard controls (e.g. censorship). Note that as we have used tangible factors in formulating the proposed model, it canAbstract: Nowadays, social networks become ubiquitous platforms for sharing and diffusing information around the world. However, spreading rumors as unverified and opaque information in social networks causes harmful damages to societies. An approach for combating rumors in social networks is to use soft control mechanisms i.e. enhancing the people's knowledge and awareness against the rumor to persuade them avoiding rumor dissemination. In this paper, we propose a soft rumor control model in which people refer to their trusted friends or ask the reputable authorities about the rumor to avoid rumor spreading. The model includes a method for selecting consultants who are both expert in rumor context and responsive to queries about rumors by the user. The battlespace between rumor and anti-rumor spreaders is then modeled as an evolutionary game to analyze the controls' effectiveness. To evaluate the proposed model, we use Pheme dataset of tweets and conduct simulation analysis. It is shown that trusted consultants suggested by the model with high precision are the same users who send anti-rumor messages in real world. Furthermore, we analyze and compare soft rumor control methods on societies with different assumed cyber literacy and habits. Moreover, it is interestingly shown that using soft rumor control mechanisms in some situations outperforms traditional hard controls (e.g. censorship). Note that as we have used tangible factors in formulating the proposed model, it can help social network developers to build feasible soft rumor control facilities in their own products. … (more)
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 100(2021)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 100(2021)
- Issue Display:
- Volume 100, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 100
- Issue:
- 2021
- Issue Sort Value:
- 2021-0100-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-04
- Subjects:
- Rumor propagation -- Soft rumor control -- Consultation -- Social network -- Trust -- Evolutionary game
Engineering -- Data processing -- Periodicals
Artificial intelligence -- Periodicals
Expert systems (Computer science) -- Periodicals
Ingénierie -- Informatique -- Périodiques
Intelligence artificielle -- Périodiques
Systèmes experts (Informatique) -- Périodiques
Artificial intelligence
Engineering -- Data processing
Expert systems (Computer science)
Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2021.104198 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16719.xml