A comparison of network sampling designs for a hidden population of drug users: Random walk vs. respondent-driven sampling

Bell, David C.; Erbaugh, Elizabeth B.; Serrano, Tabitha; Dayton-Shotts, Cheryl A.; Montoya, Isaac D.

doi:10.1016/j.ssresearch.2016.08.016

Cited by 6 publications

(5 citation statements)

References 39 publications

(55 reference statements)

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“…After reasonable filtering, the remaining valid data will be used for the subsequent analysis process. [13][14][15] Data analysis needs to apply a reasonable analysis method, which mainly relying on the characteristics of the target object to determine. Then, an analysis model is established, which is also a key link affecting the efficiency and results of the analysis.…”

Section: The Application Of Data Mining Technology In Social Networkmentioning

confidence: 99%

RETRACTED: The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

Zhu

2020

International Journal of Electrical Engineering & Education

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The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

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Section: The Application Of Data Mining Technology In Social Networkmentioning

confidence: 99%

RETRACTED: The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

Zhu

2020

International Journal of Electrical Engineering & Education

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“…Non-probabilistic network sampling (Bernard 2011) was employed to recruit informants through the snowball method. Network sampling allows for building upon existing trust relations, easing access to informants willing to be studied regarding an intimate and sensitive topic such as health-oriented practices (Bell et al 2017). The first author recruited the pool of initial informants through their secondary networks, avoiding inherent biases when dealing with members of the primary network.…”

Section: Methodsmentioning

confidence: 99%

Do you care or do I have a choice? Expert authority and consumer autonomy in medicine consumption

Schneider‐Kamp

Askegaard

2020

Consumption Markets & Culture

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Consumer research has posited an ambivalent role of choice in consumer empowerment, from liberating and including to disciplining and totalising. This article provides empirical evidence for this ambivalence and nuances how consumers navigate the liminal space between expert authority and consumer autonomy.We investigate the role of choice in consumer empowerment through an ethnographic study of medicine consumption practices in acute non-lifethreatening situations in Denmark, finding these practices to be dominated by consumer choice, not only for over-the-counter but, interestingly, also for prescription-only medicine.The implications of these findings extend beyond the context of medicine consumption. First, we provide insights on the ambivalent role of choice in consumer empowerment in a complex context of mixed commercial and public agencies. Second, we demonstrate how online information access empowers consumers to resist expert authority and act autonomously. Third, we exemplify the processes by which globalization and ideologies inform and shape consumer resistance.

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“…For instance, some works focus on the scenario of hidden populations isolated from the rest of society [23], which are notoriously hard to sample from (e.g. drug users [13]). In other cases, the underlying graph is not static, but it can significantly change during the sampling process [39].…”

Section: Related Workmentioning

confidence: 99%

Benchmarking API Costs of Network Sampling Strategies

Coscia

Rossi

2018

2018 IEEE International Conference on Big Data (Big Data)

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Online social media contain valuable quantitative and qualitative data, necessary to advance complex social systems studies. However, these data vaults are often behind a wall: the owners of the media sites dictate what, when, and how much data can be collected via a mandatory interface (called Application Program Interface: API). To work with such restrictions, network scientists have designed sampling methods, which do not require a full crawl of the data to obtain a representative picture of the underlying social network. However, such sampling methods are usually evaluated only on one dimension: what strategy allows for the extraction of a sample whose statistical properties are closest to the original network? In this paper we go beyond this view, by creating a benchmark that tests the performance of a method in a multifaceted way. When evaluating a network sampling algorithm, we take into account the API policies and the budget a researcher has to explore the network. By doing so, we show that some methods which are considered to perform poorly actually can perform well with tighter budgets, or with different API policies. Our results show that the decision of which sampling algorithm to use is not monodimensional. It is not enough to ask which method returns the most accurate sample, one has also to consider through which API constraints it has to go, and how much it can spend on the crawl.

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A comparison of network sampling designs for a hidden population of drug users: Random walk vs. respondent-driven sampling

Cited by 6 publications

References 39 publications

RETRACTED: The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

RETRACTED: The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

Do you care or do I have a choice? Expert authority and consumer autonomy in medicine consumption

Benchmarking API Costs of Network Sampling Strategies

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