Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Al-Shargabi, Amal A.; Selmi, Afef

doi:10.1109/access.2021.3091537

Cited by 22 publications

(12 citation statements)

References 33 publications

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“…Closeness centrality (CC) calculates the average distance between a node and all other nodes on the network. This measure describes the proximity of this node to other nodes [24] as in Eq. ( 2).…”

Section: B Network Analysis Research Using Social Network Analysismentioning

confidence: 99%

A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

Sembiring¹,

Suharyadi²,

Iriani³

et al. 2023

IJACSA

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Log data from computers used for network forensic analysis is ineffective at identifying specific security threats. Log data limitations include the difficulty in reconstructing communication patterns between nodes and the inability to identify more advanced security threats. By combining traditional log data analysis methods with a more effective combination of approaches, a more comprehensive view of communication patterns can be achieved. This combined approach can then help identify potential security threats more effectively. It's difficult to determine the specific benefits of combining Packet Capture (PCAP) and Social Network Analysis (SNA) when performing forensics. This article proposes a new approach to forensic analysis that combines PCAP and social network analysis to overcome some of the limitations of traditional methods. The purpose of this discovery is to improve the accuracy of network forensic analysis by combining PCAP and social network analysis to provide a more comprehensive view of network communication patterns. Network forensics, which combines pcap analysis and social network analysis, provides more comprehensive results. PCAP analysis is used to analyze network traffic, conversation statistics, protocol distribution, packet content and round-trip times. Social network analysis maps communication patterns between nodes and identifies the most influential key players within the network. PCAP analysis efficiently captures and analyzes network packets, and SNA provides insight into relationships and communication patterns between devices on the network.

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Section: B Network Analysis Research Using Social Network Analysismentioning

confidence: 99%

A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

Sembiring¹,

Suharyadi²,

Iriani³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Al-Shargabi et al [4] proposed a research study to explore the spread and the effects of the COVID-19 pandemic by analyzing the tweets of Saudi Arabia Twitter users. They collected 8905 Arabic tweets to visualize the social network for COVID-19 pandemic tweets of Saudi users.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the great importance of the social networks, so it can be considered one of the main directions for many researches. Al-Shargabi et al [4] proposed a research study to explore the effects of the COVID-19 global epidemic by analyzing the tweets of Saudi Arabia Twitter users. They collected 8905 Arabic tweets to visualize the social network graph for the COVID-19 tweets of the people who live at Saudi Arabia.…”

Section: Introductionmentioning

confidence: 99%

The Identification of the Top Positive Influential Users of the Social Networks to Help in the Control of Covid-19 Spread

Samir

Gharib²,

Rady³

2022

IJICIS

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Covid-19 pandemic is considered the most worldwide problem, and causes horrible crises for all human being. Social networks can play a vital role in the prevention of the spread of the Covid-19 pandemic. The top influential users of social networks like Twitter can have positive or negative effect in the broadcast of useful and same time harmful information about how to deal with the virus, and encourage people to follow up the rules announced by World Health Organization (WHO). So the detection of the top positive and negative influential users can help in the control of the spread of the virus. The proposed approach is based on applying influence maximization solutions to identify the top influential users from Twitter social network graph, and to determine if the influence is positive or not.The proposed approach has four main phases, the first phase is collecting Covid-19 pandemic related tweets dataset and extract the related users and their followers. The second phase is creating a social network graph from the collected dataset. The third phase is using LKG influence maximization approach to identify the most effective users from the social network graph. The last phase is based on using hashtags frequency analysis to be able to identify the type of influence of each top influential user.

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“…The number of social media users has been growing rapidly in the past decade. People discuss and share information and opinions on social media platforms [4]. It has been reported that two-thirds of American adults use social media to post their status, opinions, and other information on a regular basis.…”

Section: Introductionmentioning

confidence: 99%

Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model

Luo

Wang

2022

IEEE Access

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Twitter has been an important platform for people to discuss and share health-related information. It provides a massive amount of data for real-time monitoring of infectious diseases (such as COVID-19) and freeing disease-prevention organizations from the tedious labor involved in public health surveillance. Personal health mention (PHM) detection is one of the critical methods to keep up-to-date on an epidemic's condition; it attempts to identify a person's health condition based on online text information. This paper explores PHM identification for COVID-19 through Twitter. We built a COVID-19 PHM data set containing tweets annotated with four types of COVID-19-related health conditions. A masked attention model was devised to classify the tweets as self-mention, other-mention, awareness, and non-health. We obtained promising results on the PHM identification task. The classification results facilitate timely health monitoring and surveillance for digital epidemiology. We also evaluate how the attention mechanism and training method affect the model's predictive performance.

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Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Cited by 22 publications

References 33 publications

A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

The Identification of the Top Positive Influential Users of the Social Networks to Help in the Control of Covid-19 Spread

Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model

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