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1. It’s more than money: The true costs of spam in online survey research

   https://doi.org/10.1177/17470161251357454  

   Agrawal, Shubham; Watson, Gwendolyn_Paige; Schuster, Amy_M; Cotten, Shelia_R; Tidwell, Michael_L; Baker, Nathan; Wang, Sicheng (July 2025, Research Ethics)

   Online surveys are a popular method for collecting data in the social sciences. Despite its cost-effectiveness, concerns regarding the legitimacy of data from online surveys are increasing. One such concern is fraudulent responses or “spam” by malicious agents intentionally deceiving the survey process to gain monetary incentives or sway research results. The research costs of “spam”—their influence on research conclusions and their threat to scientific integrity—are not well understood. Here we show the differences in financial and research costs of spam using data from an online survey of transportation workers that was cleaned using a stringent battery of spam detection techniques that utilized commercially available features and a custom spam detection algorithm. We found that we would have wasted about 73% of our budget on incentivizing spammers if we had stopped data collection upon reaching the intended sample size. We also found significant differences in research conclusions related to the relationships between key organizational constructs, including affective commitment, job satisfaction, and turnover intention, between subsamples with and without spam. Our results demonstrate that researchers who are unaware of spam or do not adequately clean their data may spend substantially more monetary and human resources, as well as derive misleading conclusions. This study highlights the importance of survey researchers being cognizant of spam responses and employing robust spam detection techniques to ensure the scientific integrity of non-probability online survey research. 

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2. What is the Future of Survey-Based Data Collection for Local Government Research? Trends, Strategies, and Recommendations

   https://doi.org/10.1177/10780874231175837  

   Krause, Rachel M.; Fatemi, S. Mohsen; Nguyen Long, Le Anh; Arnold, Gwen; Hofmeyer, Sarah L. (May 2023, Urban Affairs Review)

   Surveys are an important vehicle for advancing research on urban policy and governance. The introduction of online tools eased survey-based data collection, making it cheaper and easier to obtain data from key informants like local elected officials or public administrators. However, the utility of web-based survey administration may be diminishing. To investigate this dynamic and search for strategies to support survey research in urban studies, we perform a systematic review of survey research in urban policy and administration scholarship and conduct an original survey follow-up experiment. Our findings identify a clear downward trend in survey response rates that was accentuated during the COVID-19 pandemic. Results from our survey experiment show distinctly different costs per solicitation and per completed survey, depending on administration mode. These findings stimulate discussion on how scholars may continue to use surveys to generate high-quality, empirically rigorous research on urban affairs in light of recent trends. 

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3. Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

   https://doi.org/10.1002/ecs2.3795  

   Kitzes, Justin; Blake, Rachael; Bombaci, Sara; Chapman, Melissa; Duran, Sandra M.; Huang, Tao; Joseph, Maxwell B.; Lapp, Samuel; Marconi, Sergio; Oestreich, William K.; et al (November 2021, Ecosphere)

   Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys. 

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4. Generative Adversarial Networks: A Survey Toward Private and Secure Applications

   https://doi.org/10.1145/3459992  

   Cai, Zhipeng; Xiong, Zuobin; Xu, Honghui; Wang, Peng; Li, Wei; Pan, Yi (July 2021, ACM Computing Surveys)

   Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions. 

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5. Distributing a survey using Every Door Direct Mail in an ideal use case

   https://doi.org/10.1177/20597991251329754  

   White, Jacob_C; Bessette, Douglas_L (April 2025, Methodological Innovations)

   While paper mail-based surveys avoid much of the risk of bots and fraudulent data, they suffer from lower response rates and ever-inflating material and logistical costs. In response, there is a nascent, but growing literature investigating a lower cost, explicitly anonymous, mail-based survey distribution method called Every Door Direct Mail (EDDM). This study contributes to this growing body of literature by using EDDM to disseminate a sequential mixed-mode census-style survey that meets best use-case recommendations per past research. We make several design alterations to elicit higher response rates including using an outer envelope and cash incentive. The survey, distributed near large-scale solar developments in three urban Michigan communities (~1554 households), was geographically based, targeted a specific and limited population, and covered the potentially sensitive topic of local solar development, which may have also led to a higher response rate. The survey achieved an overall response rate of 10.2% with 158 complete surveys returned, demonstrating this work’s usefulness, use case, and flexibility. 

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