TY - JOUR
T1 - Bot invasion
T2 - protecting the integrity of online surveys against spamming
AU - Berger, Matthew N.
AU - Mathieu, Erin
AU - Davies, Cristyn
AU - Shaban, Ramon Z.
AU - Bag, Shopna
AU - Rachel Skinner, S.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Despite the various advantages of online surveys, such as their cost-effectiveness and broad reach, the infiltration of bots can result in data distortion, eroding trust and hindering effective decision-making. Identifying bot responses within survey data is paramount, and epidemiologic and public health researchers can utilise various tactics such as email authentication and scrutiny of response times, to detect fraudulent responses. This paper discusses the authors' experience of bot spamming in an online survey, which skewed our findings. We discuss the actions taken to detect and invalidate bot responses within survey data and discuss potential forms of bot prevention. To detect fraudulent responses, the authors investigated the time taken to complete the survey, recruitment rates, invalid email addresses, and invalid free-format responses. Supplementary strategies, such as data validation methods and monitoring tools, can complement reCAPTCHA systems to alleviate the adverse effects of bot activity on survey data accuracy. However, employing other methods that require challenges, or additional questions may reduce the recruitment rate and deter potential participants. Given the advancing sophistication of bots, ongoing innovation in authentication techniques is imperative to protect the dependability and accuracy of survey data in the future.
AB - Despite the various advantages of online surveys, such as their cost-effectiveness and broad reach, the infiltration of bots can result in data distortion, eroding trust and hindering effective decision-making. Identifying bot responses within survey data is paramount, and epidemiologic and public health researchers can utilise various tactics such as email authentication and scrutiny of response times, to detect fraudulent responses. This paper discusses the authors' experience of bot spamming in an online survey, which skewed our findings. We discuss the actions taken to detect and invalidate bot responses within survey data and discuss potential forms of bot prevention. To detect fraudulent responses, the authors investigated the time taken to complete the survey, recruitment rates, invalid email addresses, and invalid free-format responses. Supplementary strategies, such as data validation methods and monitoring tools, can complement reCAPTCHA systems to alleviate the adverse effects of bot activity on survey data accuracy. However, employing other methods that require challenges, or additional questions may reduce the recruitment rate and deter potential participants. Given the advancing sophistication of bots, ongoing innovation in authentication techniques is imperative to protect the dependability and accuracy of survey data in the future.
KW - bot detection algorithms
KW - bots
KW - data integrity
KW - data quality
KW - spam
KW - survey methodology
UR - http://www.scopus.com/inward/record.url?scp=105005376937&partnerID=8YFLogxK
U2 - 10.1515/em-2024-0026
DO - 10.1515/em-2024-0026
M3 - Article
AN - SCOPUS:105005376937
SN - 2194-9263
VL - 14
JO - Epidemiologic Methods
JF - Epidemiologic Methods
IS - 1
M1 - 20240026
ER -