Survey Research

2018 ◽  
pp. 257-276
Author(s):  
Ernest W. Brewer ◽  
Geraldine Torrisi-Steele ◽  
Victor C. X. Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Survey Research ◽  
Mobile Devices ◽  
Historical Perspective ◽  
Human Condition ◽  
The Core ◽  
Digital World ◽  
Core Issues

Survey research, in various forms, is the mainstay for social researchers and anyone interested in finding out about people's opinions, attitudes, beliefs, and experiences. Survey research evolved from simple data collection to a more sophisticated scientific method and has proved useful in describing various aspects of the human condition as a basis for further action. However, now survey research is being challenged by the digital world as defined by big data, social media, and mobile devices. In the chapter, the authors provide a historical perspective on survey research, along with a brief presentation of foundational elements of survey research. Then, with the intent of evoking reflective discussion, the authors identify some of the core issues and viewpoints surrounding survey research in the present digital world.

scholarly journals Cyber-ethnography of cannabis marketing on social media

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Marina C. Jenkins ◽  
Lauren Kelly ◽  
Kole Binger ◽  
Megan A. Moreno
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Major Theme ◽  
Medical Cannabis ◽  
Specific Targeting ◽  
Digital World ◽  
Ethnographic Approach ◽  
Field Notes ◽  
One Year ◽  
Primary Form

Abstract Background Since 2012, several states have legalized non-medical cannabis, and cannabis businesses have used social media as a primary form of marketing. There are concerns that social media cannabis exposure may reach underage viewers. Our objective was to identify how cannabis businesses cultivate an online presence and exert influence that may reach youth. Methods We chose a cyber-ethnographic approach to explore cannabis retailers on social media. We searched cannabis retailers with Facebook and Instagram presence from Alaska, Oregon, Colorado, and Washington, and identified 28 social media business profiles. One year of content was evaluated from each profile. In-depth, observational field notes were collected from researchers immersed in data collection on business profiles. Field notes were analyzed to uncover common themes associated with social media cannabis marketing. Results A total of 14 businesses were evaluated across both Facebook and Instagram, resulting in 14 sets of combined field notes. A major theme was Normalization of Cannabis, involving both Broad Appeal and Specific Targeting. Conclusions It is concerning that Normalization of Cannabis by cannabis businesses may increase cannabis acceptability among youth. In a digital world where the majority of youth are spending time online, it is important for policymakers to examine additional restrictions for cannabis businesses marketing through social media.

scholarly journals Perceptions of Auditor Negligence: The Effects of Big Data Visualisations on Jurors’ Decisions

2021 ◽  
Author(s):  
◽  
Travis Christensen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Jury Verdicts ◽  
Word Clouds ◽  
Audit Litigation ◽  
Different Types ◽  
Different Sources ◽  
Bar Graphs

<p>This study analyses the effects of Big Data visualisations on jurors’ decisions in audit litigation cases. Specifically, the study investigates the effects of different types of Big Data visualisations (word clouds or bar graphs) and different sources of Big Data (emails or social media posts) on jurors’ perceptions of auditors’ work and the size of the negligence awards that jurors recommend. The study theorises that the emotions elicited and the reliability of the data used to create visualisations such as word clouds will have dramatic effects on jury verdicts in audit negligence trials. There is considerable literature to support this assertion. However, after data collection, it was discovered that jurors are not influenced by the emotions elicited by visualisations. Rather, participants were very sceptical of more novel types of visualisations, such as word clouds, but could be persuaded by the inherent emotions elicited and the reliability of the data if they found the visualisation useful.</p>

Big Data Techniques for Supporting Official Statistics

Web Services ◽  
2019 ◽  
pp. 728-744 ◽  
Author(s):  
Antonino Virgillito ◽  
Federico Polidoro
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Science ◽  
Official Statistics ◽  
The Core ◽  
Web Scraping ◽  
Collection Process ◽  
Data Collection Process ◽  
Data Source ◽  
Use Of Internet

Following the advent of Big Data, statistical offices have been largely exploring the use of Internet as data source for modernizing their data collection process. Particularly, prices are collected online in several statistical institutes through a technique known as web scraping. The objective of the chapter is to discuss the challenges of web scraping for setting up a continuous data collection process, exploring and classifying the more widespread techniques and presenting how they are used in practical cases. The main technical notions behind web scraping are presented and explained in order to give also to readers with no background in IT the sufficient elements to fully comprehend scraping techniques, promoting the building of mixed skills that is at the core of the spirit of modern data science. Challenges for official statistics deriving from the use of web scraping are briefly sketched. Finally, research ideas for overcoming the limitations of current techniques are presented and discussed.

Personal Data Collection and Usage for Mobile Marketing. Customer Awareness and Perception

2021 ◽  
Vol 18 ◽  
pp. 42-50
Author(s):  
Darlynton Yartey ◽  
Oladokun Omojola ◽  
Lanre Amodu ◽  
Naomi Ndubueze ◽  
Babatunde Adeyeye ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Mobile Devices ◽  
Personal Data ◽  
Mobile Marketing ◽  
Online Information ◽  
Customer Base ◽  
Higher Institutions ◽  
Awareness Level

Marketers have often relied on data to better understand the preferences of the customer base. Whilethe traditional methods were engaged in the retrieval of data, the mobile devices connected to the internetintroduced an influx of data on a real time called big data. Based on this advancement, marketers with thetechnical capacity are able to identify customer needs accurately and identify sway in trends. Although thisstrategy seems beneficial to the marketers, the naïve nature of the customers to the collection and usage ofpersonal online information for mobile marketing remains a crucial poser. Hence, this study through surveysought to identify the awareness level and perception of 700 undergraduates in three higher institutions inLagos, Nigeria. Results show that all the respondents had connected mobile devices, received advertisingmessages on their devices and were active shoppers online. Furthermore, the females were more aware of thecollection and usage of personal data, hence, they embraced the collection based on relevance of advertisingmessages and strict use for mobile marketing. This study therefore recommended marketers’ collection andusage of customers’ personal data to be based on strict use for mobile marketing and assurance of relevance ofadvertising messages.

Big Data and Democracy

2020 ◽  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Political Campaigns ◽  
Original Research ◽  
Targeted Advertising ◽  
Echo Chambers ◽  
Political Turmoil ◽  
The Impact

This edited collection tackles subjects that have arisen as a result of new capabilities to collect, analyse and use vast quantities of data using complex algorithms. Questions tackled include what is wrong with targeted advertising in political campaigns, whether echo chambers really are a matter of genuine concern, what is the impact of data collection through social media and other platforms on questions of trust in society and is there a problem of opacity as decision-making becomes increasingly automated? The contributors consider potential solutions to these challenges and discuss whether an ethical compass is available or even feasible in an ever more digitized and monitored world. The editors bring together original research on the philosophy of big data and democracy from leading international authors, with recent examples and case references – including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks – and combine them in one authoritative volume at time of great political turmoil.

On Building Better Mousetraps and Understanding the Human Condition

2015 ◽  
Vol 659 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Jimmy Lin
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Scientific Reasoning ◽  
Explanatory Power ◽  
Data Use ◽  
Human Condition ◽  
Science And Engineering ◽  
Disruptive Technologies ◽  
The Past ◽  
Data Collection And Analysis

Over the past few years, we have seen the emergence of “big data”: disruptive technologies that have transformed commerce, science, and many aspects of society. Despite the tremendous enthusiasm for big data, there is no shortage of detractors. This article argues that many criticisms stem from a fundamental confusion over goals: whether the desired outcome of big data use is “better science” or “better engineering.” Critics point to the rejection of traditional data collection and analysis methods, confusion between correlation and causation, and an indifference to models with explanatory power. From the perspective of advancing social science, these are valid reservations. I contend, however, that if the end goal of big data use is to engineer computational artifacts that are more effective according to well-defined metrics, then whatever improves those metrics should be exploited without prejudice. Sound scientific reasoning, while helpful, is not necessary to improve engineering. Understanding the distinction between science and engineering resolves many of the apparent controversies surrounding big data and helps to clarify the criteria by which contributions should be assessed.

scholarly journals Digital Epidemiology: Forecasting Epidemics in the Era of Social Media and Mobile Devices Big-Data

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Mohamed Khalil ◽  
Mohamed Said ◽  
Hesham Osman ◽  
Belal Ahmed ◽  
Dalia Ahmed ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mobile Devices ◽  
Digital Epidemiology

scholarly journals Relationship between Social Media Collaboration and Ecosystem Management in Enugu State, Nigeria

2018 ◽  
Vol 10 (4(J)) ◽  
pp. 97-108
Author(s):  
Emmanuel K Agbaeze ◽  
Ajoku P.P. Onyinye ◽  
Obamen Joseph ◽  
Omonona Solomon
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Survey Research ◽  
Ecosystem Management ◽  
Significant Relationship ◽  
Sampling Technique ◽  
Snowball Sampling ◽  
Enugu State ◽  
The Relationship ◽  
Snowball Sampling Technique

This study was done on the relationship between social media collaborations and ecosystem management in Enugu state. The study was premised on the case of herdsmen-farmers/villagers clash over grazing fields and farmlands in Enugu State being the ecology-related issue. Survey research design was adopted for the study. A sample of 100 social media group participants was selected using snowball sampling technique. The questionnaire was used for data collection. Content validity was used as a method for testing the validity of the questionnaire while Cronbach's alpha method was used for testing the internal consistency of the items on the questionnaire. Pearson’s Product Moment Correlation was used to test the hypothesis formulated for the study. Findings revealed that social media collaborations via Facebook, WhatsApp and Twitter have a significant relationship with ecosystem management. It was recommended that government and ecologically concerned agencies should employ social media collaborations as the policy for ecosystem management. 

scholarly journals People's Councils for Ethical Machine Learning

2018 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

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