Using the Think Aloud method to assess the feasibility and acceptability of Network Canvas among Black MSM and transgender persons in Atlanta, GA: Qualitative Analysis (Preprint)

2021 ◽  
Author(s):  
Natalie D. Crawford ◽  
Dorie Josma ◽  
Kristin R.V. Harrington ◽  
Joseph Morris ◽  
Alvan Quamina ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Sexual Behaviors ◽  
Black Msm ◽  
Network Data ◽  
Think Aloud ◽  
Self Administration ◽  
Social Network Data ◽  
Transgender Persons

BACKGROUND Characteristics of one’s social network have been important factors in understanding infectious disease transmission patterns. Social network data collection is generally time and resource intensive, yet is crucial to our understanding of the complex epidemiologic landscape of human behaviors among stigmatized social groups. OBJECTIVE We sought to evaluate the feasibility and acceptability of a self-administered social network data collection tool, Network Canvas, among Black MSM (BMSM) and transgender persons using the think aloud method, which is a robust and flexible research technique used to perform usability testing. METHODS We piloted a self-administered network interview within the Network Canvas Software Suite. Participants ≥ 18 years were recruited through a community-based organization in Atlanta, GA and were included based upon willingness to share information on sexual behaviors and drug use for themselves and their social networks. A semi-structured interview guide was used to document cognitive decision-making processes while using the tool. Recorded interviews were transcribed verbatim, and thematic analyses were performed. RESULTS Among seven BMSM and transgender participants, three main themes were identified from cognitive processes: Network Canvas’s utility, navigation, and intuitive design. Overall, Network Canvas was described as ‘easy to use,’ with suggestions mainly directed toward improving navigation tools and implementing an initial tutorial on the program prior to use. Participants were willing to use Network Canvas to document their social networks and characteristics. In general, observed verbal responses from participants matched their behavior although there were some discrepancies between verbal affirmations of use and understanding versus external observation. CONCLUSIONS We found Network Canvas to be a useful new tool to capture social network data. Self-administration allowed for more autonomy for participants when providing sensitive information about themselves and their social networks. Further, automated name generation and visualization of one’s social network in the application has the potential to reduce cognitive burden during data collection. More efficient methods of social network data collection have the potential to provide epidemiologic information to guide prevention efforts for populations with stigmatized health conditions or behaviors.

Social Network Construction in the Information Age

2011 ◽  
pp. 131-155 ◽  
Author(s):  
Michael Farrugia ◽  
Neil Hurley ◽  
Diane Payne ◽  
Aaron Quigley
Keyword(s):  
Social Network ◽  
Data Collection ◽  
Information Age ◽  
Network Data ◽  
Electronic Data ◽  
Network Construction ◽  
Electronic Data Collection ◽  
Social Network Data

In this chapter, the authors will discuss the differences between manual data collection and electronic data collection to understand the advantages and the challenges brought by electronic social network data. They will discuss in detail the processes that are used to transform electronic data to social network data and the procedures that can be used to validate the resultant social network.

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

scholarly journals Edge overlap in weighted and directed social networks

2021 ◽  
pp. 1-15
Author(s):  
Heather Mattie ◽  
Jukka-Pekka Onnela
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Network Data ◽  
Directed Networks ◽  
Rural Villages ◽  
Social Network Data ◽  
Structure Strength ◽  
Mean And Variance ◽  
The Mean

Abstract With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here, we extend definitions of edge overlap to weighted and directed networks and present closed-form expressions for the mean and variance of each version for the Erdős–Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks, and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

Mining of Leaders in Mobile Telecom Social Networks

2016 ◽  
pp. 68-77
Author(s):  
Mantian (Mandy) Hu
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Information Diffusion ◽  
Network Data ◽  
Massive Datasets ◽  
Social Network Data ◽  
The Social ◽  
Marketing Efficiency ◽  
Mobile Telecom

In the age of Big Data, the social network data collected by telecom operators are growing exponentially. How to exploit these data and mine value from them is an important issue. In this article, an accurate marketing strategy based on social network is proposed. The strategy intends to help telecom operators to improve their marketing efficiency. This method is based on mutual peers' influence in social network, by identifying the influential users (leaders). These users can promote the information diffusion prominently. A precise marketing is realized by taking advantage of the user's influence. Data were collected from China Mobile and analyzed. For the massive datasets, the Apache Spark was chosen for its good scalability, effectiveness and efficiency. The result shows a great increase of the telecom traffic, compared with the result without leader identification.

Keyword Based Searching in Social Networks

2017 ◽  
Vol 2 (2) ◽  
pp. 60
Author(s):  
Zainab Nayyar ◽  
Nousheen Hashmi ◽  
Nazish Rafique ◽  
Khurram Mahmood
Keyword(s):  
Social Networks ◽  
Social Network ◽  
The Political ◽  
Network Data ◽  
Social Network Data ◽  
Research Article ◽  
The Social

The main purpose of analyzing the social network data is to observe the behaviors and trends that are followed by people. How people interact with each other, what they usually share, what are their interests on social networks, so that analysts can focus on new trends for the provision of those aspects which are of great interest for people so in this research article an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result the political trends among people are analyzed.

scholarly journals Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data †

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 306
Author(s):  
Nikolaus Nova Parulian ◽  
Tiffany Lu ◽  
Shubhanshu Mishra ◽  
Mihai Avram ◽  
Jana Diesner
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Social Systems ◽  
Target Function ◽  
Centrality Measures ◽  
Network Data ◽  
Social Network Data ◽  
Empirical Networks ◽  
The Impact

Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the domain of social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of a target node according to centrality measures. This can represent the attempt of an adversary to boost or demote the degree to which others perceive individual nodes as influential or powerful. We study the impact of adversarial attacks on targets and victims, and identify metric-based security strategies to mitigate such attacks. We conduct a series of controlled experiments on synthetic network data to identify attacks that allow the adversary to achieve their objective with a single move. We then replicate the experiments with empirical network data. We run our experiments on common network topologies and use common centrality measures. We identify a small set of moves that result in the adversary achieving their objective. This set is smaller for decreasing centrality measures than for increasing them. For both synthetic and empirical networks, we observe that larger networks are less prone to adversarial attacks than smaller ones. Adversarial moves have a higher impact on cellular and small-world networks, while random and scale-free networks are harder to perturb. Also, empirical networks are harder to attack than synthetic networks. Using correlation analysis on our experimental results, we identify how combining measures with low correlation can aid in reducing the effectiveness of adversarial moves. Our results also advance the knowledge about the robustness of centrality measures to network perturbations. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and developing solutions to improving network security.

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