Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062826
A. Alshehadeh, G. Elrefae, Haneen A. Al-Khawaja, B. Bagustari, S. Eletter, Abdelhafid Belarabi
This study explored the data mining tools role in cyber-risk management among Jordanian commercial banks listed on the Amman Stock Exchange. A standardized questionnaire was distributed to the information technology and bank risk management staff (n=139) in 13 commercial banks. There were significant effects of data mining tools in cyber-risk management on the discovery, dissemination, and participation of knowledge systems, and on opportunities to improve knowledge systems through the development of the environment for study and data retrieval systems. This study emphasizes the need to look for areas of improvement that would affect the efficiency of using data mining tools in the discovery, dissemination, and sharing of knowledge systems to manage cyber risks, as well as the need to combine data mining technology with to use their cognitive abilities and technical concepts with the necessary methods and tools to improve the bank's knowledge culture and to provide all necessary data stores and warehouses for the formation of new knowledge, which would help in the development of cyber risk management in Jordanian banks, which are listed on the Amman Stock Exchange.
{"title":"The Role of Data Mining Tools in Commercial Banks' Cyber-Risk Management","authors":"A. Alshehadeh, G. Elrefae, Haneen A. Al-Khawaja, B. Bagustari, S. Eletter, Abdelhafid Belarabi","doi":"10.1109/SNAMS58071.2022.10062826","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062826","url":null,"abstract":"This study explored the data mining tools role in cyber-risk management among Jordanian commercial banks listed on the Amman Stock Exchange. A standardized questionnaire was distributed to the information technology and bank risk management staff (n=139) in 13 commercial banks. There were significant effects of data mining tools in cyber-risk management on the discovery, dissemination, and participation of knowledge systems, and on opportunities to improve knowledge systems through the development of the environment for study and data retrieval systems. This study emphasizes the need to look for areas of improvement that would affect the efficiency of using data mining tools in the discovery, dissemination, and sharing of knowledge systems to manage cyber risks, as well as the need to combine data mining technology with to use their cognitive abilities and technical concepts with the necessary methods and tools to improve the bank's knowledge culture and to provide all necessary data stores and warehouses for the formation of new knowledge, which would help in the development of cyber risk management in Jordanian banks, which are listed on the Amman Stock Exchange.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116791113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062580
Lisa Grobelscheg, Karolina Śliwa, Ema Kusen, Mark Strembeck
We present an analysis of the narratives that emerged on Twitter during four different terror attacks. To this end, we analyze a data-set consisting of more than five million Twitter messages. We use the structural topic model (STM) approach to automatically detect six narratives of crisis. Our findings indicate that i) Twitter users are highly engaged in the dissemination of operational and memorial narratives, ii) emotions and narratives directed towards authority accounts dominate the discourse regarding the entire event, iii) the presence of positive authority nodes (i.e. authority nodes who are predominantly perceived as being positive) directly impacts the type of a discourse and fuels hopeful memorial narratives to a larger extent than accusations and blaming.
{"title":"On the Dynamics of Narratives of Crisis during Terror Attacks","authors":"Lisa Grobelscheg, Karolina Śliwa, Ema Kusen, Mark Strembeck","doi":"10.1109/SNAMS58071.2022.10062580","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062580","url":null,"abstract":"We present an analysis of the narratives that emerged on Twitter during four different terror attacks. To this end, we analyze a data-set consisting of more than five million Twitter messages. We use the structural topic model (STM) approach to automatically detect six narratives of crisis. Our findings indicate that i) Twitter users are highly engaged in the dissemination of operational and memorial narratives, ii) emotions and narratives directed towards authority accounts dominate the discourse regarding the entire event, iii) the presence of positive authority nodes (i.e. authority nodes who are predominantly perceived as being positive) directly impacts the type of a discourse and fuels hopeful memorial narratives to a larger extent than accusations and blaming.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128827715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062800
A. Aljumah, M. Nuseir, Ghaleb A. El Refae
Recently, big data analysis (BDA) capabilities and knowledge management are the essential elements for organizational performance, and this aspect needs researchers' and regulators' attention. Hence, the current article investigates the impact of BDA knowledge management, BDA technological, and management capabilities on the firm performance of the oil industry in the UAE. The study also analyzes the mediating role of firms' agility among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in the UAE. The researchers have gathered the primary data from the BDA of the oil industry using survey questionnaires. The article has employed the smart-PLS to check the data reliability and association among variables. The results indicated that BDA knowledge management, BDA technological, and management capabilities have a positive association with the firm performance of the oil industry in the UAE. The findings also exposed that the firms' agility significantly mediates among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in UAE. This article guides the regulators in making regulations regarding the improvement of firm performance using BDA knowledge management and BDA technological and management capabilities.
{"title":"Impact of Big Data Analytics, Knowledge Management, and Capabilities on the Firms' Ability and Performance","authors":"A. Aljumah, M. Nuseir, Ghaleb A. El Refae","doi":"10.1109/SNAMS58071.2022.10062800","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062800","url":null,"abstract":"Recently, big data analysis (BDA) capabilities and knowledge management are the essential elements for organizational performance, and this aspect needs researchers' and regulators' attention. Hence, the current article investigates the impact of BDA knowledge management, BDA technological, and management capabilities on the firm performance of the oil industry in the UAE. The study also analyzes the mediating role of firms' agility among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in the UAE. The researchers have gathered the primary data from the BDA of the oil industry using survey questionnaires. The article has employed the smart-PLS to check the data reliability and association among variables. The results indicated that BDA knowledge management, BDA technological, and management capabilities have a positive association with the firm performance of the oil industry in the UAE. The findings also exposed that the firms' agility significantly mediates among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in UAE. This article guides the regulators in making regulations regarding the improvement of firm performance using BDA knowledge management and BDA technological and management capabilities.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062739
Ladislav Peška, Patrik Veselý, T. Skopal, Krisztián Búza
In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.
{"title":"Person Authentication using Visual Representations of Keyboard Typing Dynamics","authors":"Ladislav Peška, Patrik Veselý, T. Skopal, Krisztián Búza","doi":"10.1109/SNAMS58071.2022.10062739","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062739","url":null,"abstract":"In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132972779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062754
M. Nuseir, A. Aljumah, Ghaleb A. El Refae, I. Aburezeq
This study aims to examine different factors that can improve student performance. Therefore, this study examined the impact of lecturers' influence, hedonic motivation, quality of service, and intention to use M -learning on student performance. This study also examined the mediating role of using m-learning. The proposed research model is examined using Structural Equation Modeling (SEM) through Smart PLS as a tool. The quantitative data were collected from students at the universities of the UAE. The usable questionnaires received back from the respondents were 399. Snowball sampling was used for data collection in the present study. The results of the study support the claim that lecturers' influence, hedonic motivation, quality of service and intention to use M -learning directly affect student performance. The mediation effect of M -learning was accepted in the present study. The findings are beneficial for policymakers and academicians for future studies and strategic development.
{"title":"Important Factors to Improve Student Performance in the Universities of UAE: Examining the Mediating Effect of using M-Learning","authors":"M. Nuseir, A. Aljumah, Ghaleb A. El Refae, I. Aburezeq","doi":"10.1109/SNAMS58071.2022.10062754","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062754","url":null,"abstract":"This study aims to examine different factors that can improve student performance. Therefore, this study examined the impact of lecturers' influence, hedonic motivation, quality of service, and intention to use M -learning on student performance. This study also examined the mediating role of using m-learning. The proposed research model is examined using Structural Equation Modeling (SEM) through Smart PLS as a tool. The quantitative data were collected from students at the universities of the UAE. The usable questionnaires received back from the respondents were 399. Snowball sampling was used for data collection in the present study. The results of the study support the claim that lecturers' influence, hedonic motivation, quality of service and intention to use M -learning directly affect student performance. The mediation effect of M -learning was accepted in the present study. The findings are beneficial for policymakers and academicians for future studies and strategic development.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062678
Riadh Jeljeli, Faycal Farhi, Saadia Anwar Pasha
Emotional Intelligence, when integrated into Public Relations, adds potential value to retail sector organizations. However, with the rise of ICT, these factors are further enriched by Artificial Intelligence as the organizations are more goal-oriented, adopting certain customer-centric approaches. This study also focused on the role of the Emotionally Intelligent Public Relation system in the United Arab Emirates benefiting from Artificial Intelligence integration. The primary focus remained on Artificial Intelligence, upgrading these Public Relations practices and improving customer loyalty. The researchers used a self-proposed conceptual model assessed using Structural Equation Modelling (SEM). Results revealed that Public Relations practices are significantly affecting Social Skills (p>. 020) and Empathy (.000) as primary components of Emotional Intelligence (EI). Besides, the effect of Social Skills on Empathy also remained significant (p> 045). On the other hand, Social Skills (p>. 000) have a significant effect on Customer Loyalty, which is significantly mediated by Artificial Intelligence (p>. 070). Thus, it is concluded that when Public Relations integrated with Emotional Intelligence are linked with Artificial Intelligence, positive, constructive outcomes are ensured. Particularly, in the retail sector, organizations integrating Artificial Intelligence provide the service as best matching the customers, adding more value to the services and product quality of the certain retailer, indicating the same patterns of consumer experiences leading to loyalty among them.
{"title":"EI Integrated PR for Online Communication in Customer Loyalty: The Mediating Role of Artificial Intelligence","authors":"Riadh Jeljeli, Faycal Farhi, Saadia Anwar Pasha","doi":"10.1109/SNAMS58071.2022.10062678","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062678","url":null,"abstract":"Emotional Intelligence, when integrated into Public Relations, adds potential value to retail sector organizations. However, with the rise of ICT, these factors are further enriched by Artificial Intelligence as the organizations are more goal-oriented, adopting certain customer-centric approaches. This study also focused on the role of the Emotionally Intelligent Public Relation system in the United Arab Emirates benefiting from Artificial Intelligence integration. The primary focus remained on Artificial Intelligence, upgrading these Public Relations practices and improving customer loyalty. The researchers used a self-proposed conceptual model assessed using Structural Equation Modelling (SEM). Results revealed that Public Relations practices are significantly affecting Social Skills (p>. 020) and Empathy (.000) as primary components of Emotional Intelligence (EI). Besides, the effect of Social Skills on Empathy also remained significant (p> 045). On the other hand, Social Skills (p>. 000) have a significant effect on Customer Loyalty, which is significantly mediated by Artificial Intelligence (p>. 070). Thus, it is concluded that when Public Relations integrated with Emotional Intelligence are linked with Artificial Intelligence, positive, constructive outcomes are ensured. Particularly, in the retail sector, organizations integrating Artificial Intelligence provide the service as best matching the customers, adding more value to the services and product quality of the certain retailer, indicating the same patterns of consumer experiences leading to loyalty among them.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130159025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062702
O. Younis, Yahya M. Tashtoush, Mohammad H. Alomari, Omar A. Darwish
This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system.
{"title":"A Platform for Automated Solar Data Analysis Using Machine Learning","authors":"O. Younis, Yahya M. Tashtoush, Mohammad H. Alomari, Omar A. Darwish","doi":"10.1109/SNAMS58071.2022.10062702","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062702","url":null,"abstract":"This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131524928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062839
Muzamil Ahmed, H. Khan, Saqib Iqbal, Q. Althebyan
This paper proposes an automated question answering system based on improved Term Frequency- Inverse Document Frequency (TF-IDF) and cosine similarity. The main purpose of this research study is to provide an effective question answering system that retrieves precise and relevant answers to the users' queries with high confidence. The existing studies in the relevant literature show that several techniques have been proposed for automatic question answering systems. The rule-based techniques depend on inference rules and take less time to respond to the user query. However, generations of pattern and inference rules are difficult as natural languages lack to follow a fixed pattern. The content-based similarity method pre-computes similarity with all the repository questions for a given query. In this research study, firstly all repository questions are pre-processed and a matrix using the improved TF -IDF model is generated. Then, we find the similarity of each user query with the matrix after query pre-processing. For the proposed approach, we remove stop-words and apply lemmatization and POS tagging techniques for pre-processing. The proposed framework is implemented using the standard datasets used in the existing studies. The empirical analysis-based results show that the systems adopting the proposed technique takes less than five seconds to respond to user queries with maximum similarity. The proposed framework attains up to 84% accuracy.
{"title":"Automated Question Answering based on Improved TF-IDF and Cosine Similarity","authors":"Muzamil Ahmed, H. Khan, Saqib Iqbal, Q. Althebyan","doi":"10.1109/SNAMS58071.2022.10062839","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062839","url":null,"abstract":"This paper proposes an automated question answering system based on improved Term Frequency- Inverse Document Frequency (TF-IDF) and cosine similarity. The main purpose of this research study is to provide an effective question answering system that retrieves precise and relevant answers to the users' queries with high confidence. The existing studies in the relevant literature show that several techniques have been proposed for automatic question answering systems. The rule-based techniques depend on inference rules and take less time to respond to the user query. However, generations of pattern and inference rules are difficult as natural languages lack to follow a fixed pattern. The content-based similarity method pre-computes similarity with all the repository questions for a given query. In this research study, firstly all repository questions are pre-processed and a matrix using the improved TF -IDF model is generated. Then, we find the similarity of each user query with the matrix after query pre-processing. For the proposed approach, we remove stop-words and apply lemmatization and POS tagging techniques for pre-processing. The proposed framework is implemented using the standard datasets used in the existing studies. The empirical analysis-based results show that the systems adopting the proposed technique takes less than five seconds to respond to user queries with maximum similarity. The proposed framework attains up to 84% accuracy.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126542030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062712
Martin Zboril, J. Syrovátková, A. Pavlíček
Social logins offer users a simplified authentication method where they may leverage already-established social media accounts, such as with Twitter or Facebook. This concept brings users many benefits but, simultaneously, users should be aware of the multiple associated security risks. Social media users should also pay attention to the appropriate security configuration of their accounts to decrease the probability of a cyberattack. Social logins and the security configurations of social media have been broadly researched within the environment of the Czech Republic. This paper further analyzes the relation between the users' perception of security as regards social logins, the security risks associated with them, the security configuration of social media accounts, and the risk appetite of users. The authors examined the data through descriptive analysis and multi-factor analysis.
{"title":"Social Login Usage for Third-Party Cloud Services — Relation of Security Risks and Configuration","authors":"Martin Zboril, J. Syrovátková, A. Pavlíček","doi":"10.1109/SNAMS58071.2022.10062712","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062712","url":null,"abstract":"Social logins offer users a simplified authentication method where they may leverage already-established social media accounts, such as with Twitter or Facebook. This concept brings users many benefits but, simultaneously, users should be aware of the multiple associated security risks. Social media users should also pay attention to the appropriate security configuration of their accounts to decrease the probability of a cyberattack. Social logins and the security configurations of social media have been broadly researched within the environment of the Czech Republic. This paper further analyzes the relation between the users' perception of security as regards social logins, the security risks associated with them, the security configuration of social media accounts, and the risk appetite of users. The authors examined the data through descriptive analysis and multi-factor analysis.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116941133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062675
Alireza Mohammadinodooshan, Niklas Carlsson
Recently, researchers have modeled how reliability and political bias of news may affect Facebook users' engagement, as measured using interaction metrics such as the number of shares, likes, etc. However, the temporal dynamics of Facebook users' engagement with news of varying degrees of bias and reliability is less studied. In light of the COVID-19 pandemic, it is also important to quantify how the pandemic changed user engagement with various news. This paper presents the first temporal study of Facebook users' interaction dynamics, accounting for both the bias and reliability of the publishers. We consider a dataset of 992 U.S. publishers, and the study spans the period from Jan. 2018 to July 2022. This allows us to accurately assess the effect of the covid outbreak on the temporal dynamics of Facebook users' interactions with different classes of news. Our study examines these two parameters' effect on Facebook user engagement using both per-publisher and aggregated statistics. Several findings are revealed by our analysis, including that publishers in different bias and reliability classes experienced significantly different levels of engagement dynamics during and following the covid outbreak. For example, we show that the least reliable news exhibited the most considerable growth of followers during the covid period and the most reliable news sources exhibited the greatest growth rate of followers during the post-covid period. We also show that the interaction rate (number of interactions normalized over the number of followers) with Facebook news posts during the post-covid period is smaller than it was even before the outbreak. Furthermore, we demonstrate how the COVID-19 outbreak caused statistically significant structural breaks in the temporal dynamics of engagement with several types of news, and quantify this effect. With social media becoming a popular news source during crises, the observed temporal dynamics provide important insights into how information was consumed over the recent years, benefiting both researchers and public sectors.
{"title":"Temporal Dynamics of User Engagement with U.S. News Sources on Facebook","authors":"Alireza Mohammadinodooshan, Niklas Carlsson","doi":"10.1109/SNAMS58071.2022.10062675","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062675","url":null,"abstract":"Recently, researchers have modeled how reliability and political bias of news may affect Facebook users' engagement, as measured using interaction metrics such as the number of shares, likes, etc. However, the temporal dynamics of Facebook users' engagement with news of varying degrees of bias and reliability is less studied. In light of the COVID-19 pandemic, it is also important to quantify how the pandemic changed user engagement with various news. This paper presents the first temporal study of Facebook users' interaction dynamics, accounting for both the bias and reliability of the publishers. We consider a dataset of 992 U.S. publishers, and the study spans the period from Jan. 2018 to July 2022. This allows us to accurately assess the effect of the covid outbreak on the temporal dynamics of Facebook users' interactions with different classes of news. Our study examines these two parameters' effect on Facebook user engagement using both per-publisher and aggregated statistics. Several findings are revealed by our analysis, including that publishers in different bias and reliability classes experienced significantly different levels of engagement dynamics during and following the covid outbreak. For example, we show that the least reliable news exhibited the most considerable growth of followers during the covid period and the most reliable news sources exhibited the greatest growth rate of followers during the post-covid period. We also show that the interaction rate (number of interactions normalized over the number of followers) with Facebook news posts during the post-covid period is smaller than it was even before the outbreak. Furthermore, we demonstrate how the COVID-19 outbreak caused statistically significant structural breaks in the temporal dynamics of engagement with several types of news, and quantify this effect. With social media becoming a popular news source during crises, the observed temporal dynamics provide important insights into how information was consumed over the recent years, benefiting both researchers and public sectors.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123825309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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