Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks

2012 ◽  
Vol 5 (1) ◽  
pp. 16-34 ◽  
Author(s):  
Marion E. Hambrick
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Home Pages ◽  
Sporting Event ◽  
Sport Industry ◽  
Twitter Users ◽  
Promotional Messages ◽  
Share Information

Sport industry groups including athletes, teams, and leagues use Twitter to share information about and promote their products. The purpose of this study was to explore how sporting event organizers and influential Twitter users spread information through the online social network. The study examined two bicycle race organizers using Twitter to promote their events. Using social network analysis, the study categorized Twitter messages posted by the race organizers, identified their Twitter followers and shared relationships within Twitter, and mapped the spread of information through these relationships. The results revealed that the race organizers used their Twitter home pages and informational and promotional messages to attract followers. Popular Twitter users followed the race organizers early, typically within the first 4 days of each homepage’s creation, and they helped spread information to their respective followers. Sporting event organizers can leverage Twitter and influential users to share information about and promote their events.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

How users tweet about a cyber attack: An explorative study using machine learning and social network analysis

2020 ◽  
Vol 11 (2) ◽  
pp. 195-214 ◽  
Author(s):  
Daniel Vogler ◽  
Florian Meissner
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Cyber Attack ◽  
Ticket Sales ◽  
Security Issues ◽  
Twitter Users

Cybercrime is a growing threat for firms and customers that emerged with the digitization of business. However, research shows that even though people claim that they are concerned about their privacy online, they do not act correspondingly. This study investigates how prevalent security issues are during a cyber attack among Twitter users. The case under examination is the security breach at the US ticket sales company, Ticketfly, that compromised the information of 26 million users. Tweets related to cybersecurity are detected through the application of automated text classification based on supervised machine learning with support vector machines. Subsequently, the users that wrote security-related tweets are grouped into communities through a social network analysis. The results of this multi-method study show that users concerned about security issues are mostly part of expert communities with already superior knowledge about cybersecurity.

Counter-hegemonic contra-flow and the Al Jazeera America fiasco: A social network analysis of Al Jazeera America’s Twitter users

2017 ◽  
Vol 13 (3) ◽  
pp. 267-282 ◽  
Author(s):  
Tal Samuel-Azran ◽  
Tsahi Hayat
Keyword(s):  
United States ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Selective Exposure ◽  
Al Jazeera ◽  
The West ◽  
Twitter Users ◽  
News Outlet

Al Jazeera America, arguably the most ambitious attempt in history by a non-Western network to broadcast to US audiences, was shut in April 2016. A social network analysis of Al Jazeera America’s following on Twitter reveals that 42 per cent of Al Jazeera America’s followers did not follow any other US news outlet and that most of the remaining 58 per cent followed liberal stations. The findings illustrate mainstream US news consumers’ reluctance to follow Al Jazeera America, which only appealed to specific audiences. The analysis portrays the challenges facing counter-hegemonic contra-flow stations such as Al Jazeera America in their bid to gain legitimacy in the West, and specifically in the United States, and highlights the relevance of selective exposure and hostile media theories in the case of counter-flowing stations.

scholarly journals Social Network Analysis: #BlackLivesMatter Distribution at Actor Level and System Level

2021 ◽  
Vol 6 (2) ◽  
pp. 275-283
Author(s):  
Edy Prihantoro ◽  
Rizky Wulan Ramadhani
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Quantitative Methods ◽  
Closeness Centrality ◽  
System Level ◽  
Eigenvector Centrality ◽  
Black Lives Matter ◽  
A Value ◽  
Twitter Users

#BlackLivesMatter accompanies several cases of discrimination against the black community. The hashtag was spread by actors who have great influences on Twitter users. The actors create communication network which connected to each other to form opinions about the Black Lives Matter movement. Researchers conducted a study to determine the distribution of #BlackLivesMatter at the actor level for the period 20-27 April 2021 in Twitter. The study used quantitative methods and a positivistic paradigm with a Social Network Analysis (SNA) approach. The results show that the actor with the highest degree of centrality is @jeanmessiha with 238 interactions, the actor with the highest betweenness centrality is @helloagain0611 with a value of 0.000049, the actor with the highest eigenvector centrality is @jeanmessiha with a value of 1 and there are 1,416 actors who have closeness centrality. # BlackLivesMatter has a low diameter value so that it spreads quickly but not too widely, not much reciprocity occurs, not concentrated in one dominant cluster but spread widely in several clusters. The actors play a role in spreading diverse opinions regarding Black Lives Matter, thus creating free discussion in several clusters on Twitter. Opinion widely spread on Twitter creates public opinion regarding the Black Lives Matter movement.

A multi-methods design to understand the #longCOVID and #longhaulers conversation on Twitter (Preprint)

2021 ◽  
Author(s):  
Sara Santarossa ◽  
Ashley Rapp ◽  
Saily Sardinas ◽  
Janine Hussein ◽  
Alex Ramirez ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Public Perception ◽  
Long Term Effects ◽  
Patient Centered ◽  
The Social ◽  
Personal Experiences ◽  
Twitter Users

BACKGROUND The scientific community is just beginning to uncover potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. OBJECTIVE The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. METHODS A multipronged approach was used to analyze data (N = 2,500 records from Twitter) about long-COVID and from people experiencing long COVID-19. A text analysis was completed by both human coders and Netlytic, a cloud-based text and social networks analyzer. A social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users. RESULTS Among the 2,010 tweets about long COVID-19, and 490 tweets by COVID-19 long-haulers 30,923 and 7,817 unique words were found, respectively. For booth conversation types ‘#longcovid’ and ‘covid’ were the most frequently mentioned words, however, through visually inspecting the data, words relevant to having long COVID-19 (i.e., symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long-haulers. When discussing long COVID-19, the most prominent frames were ‘support’ (1090; 56.45%) and ‘research’ (435; 21.65%). In COVID-19 long haulers conversations, ‘symptoms’ (297; 61.5%) and ‘building a community’ (152; 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long-haulers, networks are highly decentralized, fragmented, and loosely connected. CONCLUSIONS The present study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions.

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