Browser Plug-in Research Articles

Computational Methods for Improving the Observability of Platform-Based Advertising

The advertising ecosystem has been progressively reshaped by computational advertising models facilitated and controlled by digital media platforms. Meta and Alphabet, two of the dominant players in this ecosystem, have contributed significantly to industry disintermediation via the design and market dominance of their advertising systems, which in turn are symbiotic with their own business models, and feed on large amounts of consumer data generated by everyday platform use. The operations and impacts of these systems remain opaque and difficult to observe. Responding to this challenge, we present a methodological framework integrating cultural, legal, media, and computational perspectives to enable the observation of computational advertising. This methodology comprises three novel computational methods: direct data gathering from ad transparency libraries; automated data donation via participatory citizen science browser plug-ins; and a privacy-aware mobile screen capture app. We describe how our suite of computational approaches enables scalable and participant-situated research, overcoming limitations posed by restricted platform access to computational advertising data. Ethical considerations and practical implications are discussed alongside case study findings, further demonstrating the potential for these methods to inform regulatory debates and scholarly advancements in the field.

Analyzing Security Risks in Browser Extension Search Tools: A Literature Review

The literature review titled "Analyzing Security Risks in Browser Extension Search Tools" explores the growing interconnection between the digital and physical domains and the vulnerabilities and threats that accompany it. The study highlights how the Darkweb can be used for communication and anonymity, but because it is encrypted, it presents difficulties for law enforcement. Although browser extensions are essential for improving user experiences when browsing, they also offer security vulnerabilities like cross-site scripting and privilege escalation that can be used maliciously. The study emphasizes the necessity of better detection techniques for malicious extensions using Static and dynamic analysis combined, as well as the significance of advanced threat analysis and AI integration to foresee and mitigate cyber attacks.In order to improve the practical applicability of security measures, the study also urges a deeper comprehension of user behaviors and socioeconomic dynamics inside the Darkweb ecosystem. In order to successfully handle evolving cyber risks, future research topics include developing automated security testing procedures, upgrading authorship attribution algorithms for user privacy, and developing tools such as the WAP static analysis tool. Keywords: Browser add-ons, security hazards, Darkweb, online threats, sophisticated threat analysis, artificial intelligence integration, methods of detection, user conduct, and acknowledgment of authors WAP tool, cybersecurity, and automated security testing.

Support for a browser plug-in blocking online alcohol imagery among Australian participants: A qualitative interview study.

Alcohol depictions are extremely common online, and there is a reported relationship between alcohol exposure and alcohol use. A browser plug-in specifically designed to block online alcohol depictions may be helpful to prevent the uptake of alcohol or increased alcohol use. Twenty-five semi-structured interviews were conducted, examining participants' beliefs about the effects of online alcohol exposure and their support for developing the browser plug-in. Using reflexive thematic analysis, we found participants highlighted a clear impact of viewing alcohol online and offline alcohol use. Participants believed a browser plug-in that blocked alcohol was acceptable and would be especially useful for minors (to prevent alcohol initiation) and those who are aiming to reduce their alcohol use. Participants emphasised that viewing online alcohol exposure had an impact on drinking behaviours, such as increased craving and temptation. The browser plug-in was considered an easy intervention tool for both parents and people who are experiencing alcohol-related problems or trying to reduce their drinking. SO WHAT?: Participants' continuous support of an alcohol-blocking browser plug-in suggests that future health promotion strategies should consider the development of a prototype plug-in.

Safeguarding Cyber Ecosystems: Machine Learning and Data Mining Approaches for Proactive Phishing Detection in Websites

Abstract: The prevalence of cyber threats, notably phishing attacks, presents a significant challenge in the digital realm with the increasing reliance on the internet. Phishing, where victims' credentials are illicitly acquired through deceptive websites resembling legitimate ones, is a particularly concerning form of cybercrime. This paper proposes an innovative system utilizing Machine Learning and Data Mining techniques to detect both established and newly generated phishing URLs without historical behavioral data. The system, embodied in a Chrome browser plugin, operates in real-time to provide immediate protection as users browse web pages. A distinctive feature of this system is its capability to identify phishing websites lacking prior behavioral patterns, ensuring adaptability to evolving cyber threats. Through comprehensive training with an extensive dataset, the model powering the plugin aims for high accuracy in detecting phishing attempts. By deploying advanced Machine Learning methodologies, the system discerns subtle patterns and characteristics intrinsic to phishing URLs, effectively distinguishing them from legitimate websites. The ultimate objective is to enhance cyber security by providing users with a robust and proactive tool against the growing sophistication of phishing attacks, thereby contributing to the ongoing efforts to create a secure online environment amidst the dynamic landscape of cyber threats.

BNoteHelper: A Note-based Outline Generation Tool for Structured Learning on Video-sharing Platforms

Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video and aiming to consume the video itself. In this article, we design and implement BNoteHelper, a note-based video outline prototype that generates outline titles by extracting user-generated notes on Bilibili, using the BART model fine-tuned on a built dataset. As a browser plugin, BNoteHelper provides users with video overview and navigation as well as note-taking template, via two main features: outline table and navigation marker. The model and prototype are evaluated through automatic and human evaluations. The automatic evaluation reveals that, both before and after fine-tuning, the BART model outperforms T5-Pegasus in BLEU and Perplexity metrics. Also, the results from user feedback reveal that the generation outline sourced from notes is preferred by users over that sourced from video captions due to its more concise, clear, and accurate characteristics but also too general with less details and diversities sometimes. Two features of the video outline are also found to have respective advantages, especially in holistic and fine-grained aspects. Based on these results, we propose insights into designing a video summary from the user-generated creation perspective, customizing it based on video types, and strengthening the advantages of its different visual styles on video-sharing platforms.

TermIt: Managing normative thesauri

Thesauri are popular, as they represent a manageable compromise – they are well-understood by domain experts, yet formal enough to boost use cases like semantic search. Still, as the thesauri size and complexity grow in a domain, proper tracking of the concept references to their definitions in normative documents, interlinking concepts defined in different documents, and keeping all the concepts semantically consistent and ready for subsequent conceptual modeling, is difficult and requires adequate tool support. We present TermIt, a web-based thesauri manager aimed at supporting the creation of thesauri based on decrees, directives, standards, and other normative documents. In addition to common editing capabilities, TermIt offers term extraction from documents, including a web document annotation browser plug-in, tracking term definitions in documents, term quality and ontological correctness checking, community discussions over term meanings, and seamless interlinking of concepts across different thesauri. We also show that TermIt features better fit the E-government scenarios in the Czech Republic than other tools. Additionally, we present the feasibility of TermIt for these scenarios by preliminary user experience evaluation.

An Enhanced Mechanism for Protecting Web Applications from Cross Site Request Forgery (CSRF)

Cross-Site Request Forgery (CSRF) is considered one of the top vulnerabilities in today’s web, where an untrusted website can force the user browser to send an unauthorized valid request to the trusted site. Legitimate users will lose their integrity over the website when the CSRF takes place. So far, many solutions have been proposed for the CSRF attacks such as the referrer HTTP header, custom HTTP header, origin header, client site proxy, a browser plug-in, and random token validation, but in this research, the use of random token validation to solve the problem of CSRF attacks was implemented. The proposed solution in the study used a concept known as Nonce which is a type of random token attached to requests sent over a server. This solution proved to be effective enough to protect and prevent web applications from CSRF attacks. Although no system can be secured against attacks, this study recommends that the random token validation (Nonce) approach of protecting web applications should be adopted by all web applications developers and users to improve on the already established solutions to reduce the chances of attacks on web applications to a very slim percentage, if not eliminated.

A research platform for measuring information exposure, trust, and behaviour – burden of infodemics

Abstract Background During a health emergency, accurate and useful information can be drowned out by legitimate questions, concerns, information voids, conflicting information, and misinformation. Very few studies connect information exposure and trust to health behaviours and health outcomes, which limits available evidence to inform when and where to act to mitigate infodemics, especially in low resource settings. We have developed a toolkit that can support studies linking information exposure to health behaviours at the individual level and therefore can help estimate burden of infodemics at population level. Methods We developed and validated a research toolkit to measure topic-specific information exposure and health behaviors. We evaluated it in realistic scenarios to understand the usability of the toolkit. Results We address researcher requirements via a web-based study platform that includes an app that participants use to record topic-specific information exposure, a browser plugin for tracking access to relevant webpages, questionnaires that can be delivered at any time during a study, and app-based incentives for participation such as visual analytics to compare trust levels with other participants. Platform features include tailoring studies to local contexts, ease of use for participants, and frictionless sharing of de-identified data for aggregating individual participant data in international meta-analyses. The toolkit was improved for usability to researchers and study participants. Conclusions When applied in research studies, as per proposed protocol and with Institutional Review Board ethics clearance, this toolkit can capture detailed data about information exposure and health behaviour data, standardise study design while supporting localisation, and make it easy to synthesise individual participant data across studies. This will facilitate estimations of population-level estimated of impact of information diets on health behaviors and health outcomes. Key messages • The toolkit will make it easy to synthesise results in a global meta-analysis, also by researchers in resource poor settings. • Next steps include establishing a coordinating site to support training in regional centres to turn meta-analyses into ongoing monitoring of the global burden of infodemics.

Third-Party Tracking in Online Public Library Environments in the United States and Canada: A Statistical Analysis

A Review of:Gardner, G. J. (2021). Aiding and abetting: Third-party tracking and (in)secure connections in public libraries. The Serials Librarian, 81(1), 69–87. https://doi.org/10.1080/0361526X.2021.1943105 Objective – To determine through statistical data collection the frequency of tracking by third parties in online public library environments along with the visibility and ease of discovery of online library policies and disclosures related to third-party tracking in particular and data privacy in general. Design – Online evaluation of public library websites. Setting – English-language public libraries in the United States and Canada. Subjects – 178 public library websites (133 in the United States and 45 in Canada). The libraries included in the study were intentionally selected for their membership in either the Canadian Urban Libraries Council (CULC) or the Urban Libraries Council (ULC) in the Unites States, since these libraries have some of the largest systems membership serving predominantly urban and suburban communities in both countries. The included Canadian libraries serve nearly 41% of the population in that country while the included libraries in the United States are positioned to serve 28% percent of the total population. The author notes that “These percentage figures serve as hypothetical, upper-bound estimates of the population affected by third-party tracking since not every member of these communities actually uses their local public library” (Gardner, 2021, p.72). Methods – In addition to evaluating the public library catalog and website in general with regards to third-party tracking and data privacy, 10 common content sources (databases) available at all of the included libraries were also included in the examination. Two browser add-ons designed to detect third-party tracking, Ghostery and Disconnect, were used in the study due to their popularity and incorporation into previous similar studies. In addition to third-party tracking the author executed word searches on library homepages using Ctrl-F for words commonly used to denote privacy or terms of use statements. No qualitative analysis was performed to determine if information shared regarding third-party tracking was accurate, and subpages were not examined. The data collection period lasted a total of three months beginning in March 2017 and running through May 2017. Main Results – The data gathered between March and May of 2017 clearly indicates a general disregard among most sampled public libraries regarding the protection of patron data gathered by third-party tracking. Of Canadian libraries included in the sample 89% (40) enabled third-party tracking, while libraries in the United States allowed it at a rate of 87% (116). Both Ghostery and Disconnect revealed an almost identical number of incidences of third-party tracking in library catalogs and in the 10 popular public library databases examined in the study. Certain OPACS were associated with higher tracking counts as were certain library databases. Libraries were found to be lax when it came to providing a link on the homepage potentially informing users of the presence of third-party tracking. Of the 156 total libraries with third-party tracking in their online catalogs, 69 (44%) included a homepage link while the rest did not. The author notes that the presence of a link was all that was examined, and not specific language used to disclose the level of third-party tracking or data privacy. In total, 8 of the 10 common content sources allowed third-party tracking. All 10 provided a link to either privacy or terms of service statements on their landing pages. Conclusion – Although patron privacy is an issue addressed in the American Library Association (ALA) Code of Ethics (American Library Association, 2021), the author concludes that “Together with previous research on usage of privacy-enhancing tools in public libraries, these results suggest that public libraries are accessories to third-party tracking on a large scale” (Gardner, 2021, p.69).

Visuals for Emancipatory Technology: A Case Study in Co-Designing a Visual Language to Counter Online Gender-Based Violence on Indian Twitter

This essay is an account of the visual design for Uli, a user-facing browser plugin to detect and moderate online gender-based violence on Twitter. The authors of this essay, who were involved as visual designers in the team that developed Uli, discuss the co-design process behind creating the visual narrative of such a tool to represent the collective labour in its creation by journalists, activists, community influencers, writers, technologists, and researchers engaged in the struggle against the interwoven caste, religion, gender and sexuality-based violence both online and offline. The essay finally sheds light on how such a visual identity and narrative can promote an alternate visual culture that challenges the dominant visual language of social media that's complacent in the propagation of online gender-based violence.

Measuring the burden of infodemics with a research toolkit for connecting information exposure, trust, and health behaviours

BackgroundDuring a public health emergency, accurate and useful information can be drowned out by questions, concerns, information voids, conflicting information, and misinformation. Very few studies connect information exposure and trust to health behaviours, which limits available evidence to inform when and where to act to mitigate the burden of infodemics, especially in low resource settings. This research describes the features of a toolkit that can support studies linking information exposure to health behaviours at the individual level.MethodsTo meet the needs of the research community, we determined the functional and non-functional requirements of a research toolkit that can be used in studies measuring topic-specific information exposure and health behaviours. Most data-driven infodemiology research is designed to characterise content rather than measure associations between information exposure and health behaviours. Studies also tend to be limited to specific social media platforms, are unable to capture the breadth of individual information exposure that occur online and offline, and cannot measure differences in trust by information source or content. Studies are also designed very differently, limiting synthesis of results.ResultsWe demonstrate a way to address these requirements via a web-based study platform that includes an app that participants use to record topic-specific information exposure, a browser plugin for tracking access to relevant webpages, questionnaires that can be delivered at any time during a study, and app-based incentives for participation such as visual analytics to compare trust levels with other participants. Other features of the platform include the ability to tailor studies to local contexts, ease of use for participants, and frictionless sharing of de-identified data for aggregating individual participant data in international meta-analyses.ConclusionsOur proposed solution will be able to capture detailed data about information exposure and health behaviour data, standardise study design while simultaneously supporting localisation, and make it easy to synthesise individual participant data across studies. Future research will need to evaluate the toolkit in realistic scenarios to understand the usability of the toolkit for both participants and investigators.

Real-Time Phishing Website Detection using Machine Learning and Updating Phishing Probability with User Feedback

Phishing attacks remain a significant threat to internet users worldwide. Cybercriminals often send out phishing links through various channels such as emails, social media platforms, or text messages, to trick users into disclosing their sensitive infor-mation such as passwords, usernames, or credit card details. This stolen information is then used to perpetrate various types of fraud or sold on the dark web for profit. To combat this problem, various machine learning-based solutions have been developed for detect-ing phishing websites. However, these solutions vary in their effec-tiveness, with some focusing on URL-based algorithms while oth-ers focus on website content. This paper proposes a machine learn-ing-based approach to real-time phishing website detection, with a focus on the website's URL, domain page, and content. The pro-posed framework will be implemented as a browser plug-in, which can identify phishing risks as users visit websites. The framework integrates several techniques, including blacklist interception, whitelist filtering, and machine learning prediction, to improve ac-curacy, reduce false alarm rates, and minimize computation times. The proposed approach also incorporates user feedback to update the phishing probability over time, thereby increasing the accuracy of detecting phishing websites. This feedback loop involves users reporting suspected phishing websites to the system, which then updates the phishing probability calculation with new information to improve its accuracy. The significance of this research lies in its ability to provide real-time phishing detection capabilities, which can help protect internet users from falling victim to phishing at-tacks. Furthermore, the use of machine learning-based algorithms and user feedback ensures that the system is continuously updated to remain effective against new and emerging threats.

Addressing labour exploitation in the data science pipeline: views of precarious US-based crowdworkers on adversarial and co-operative interventions

PurposeUnderlying much recent development in data science and artificial intelligence (AI) is a dependence on the labour of precarious crowdworkers via platforms such as Amazon Mechanical Turk. These platforms have been widely critiqued for their exploitative labour relations, and over recent years, there have been various efforts by academic researchers to develop interventions aimed at improving labour conditions. The aim of this paper is to explore US-based crowdworkers’ views on two proposed interventions: a browser plugin that detects automated quality control “Gold Question” (GQ) checks and a proposal for a crowdworker co-operative.Design/methodology/approachThe authors interviewed 20 US-based crowdworkers and undertook a thematic analysis of collected data.FindingsThe findings indicate that US-based crowdworkers tend to have negative and mixed feelings about the GQ detector, but were more enthusiastic about the crowdworker co-operative.Originality/valueDrawing on theories of precarious labour, this study suggests an explanation for the findings based on US-based workers’ objective and subjective experiences of precarity. The authors argue that for US-based crowdworkers “constructive” interventions such as a crowdworker co-operative have more potential to improve labour conditions.

Healthcare Management System Using Blockchain

Abstract: The current healthcare system has traditionally relied on paper-based medical records or electronic medical records (EMRs) that are stored in centralized databases. This method, however, has shown to be ineffective and unreliable, causing major delays in patient care. Medical documents are frequently sent over email as a temporary fix; however, this approach is unsafe and unreliable. Data breaches or unauthorised access to sensitive patient information pose a serious danger and may have far-reaching repercussions for both patients and healthcare providers. Blockchain technology can provide a more secure, decentralized, and efficient way to store and share medical records. Blockchain offers a tamper-proof and auditable method to trace transactions while using cryptographic methods to secure the integrity and confidentiality of data, access to patient data and modifications. Patients, physicians, and hospitals may easily, anytime, anywhere, and with total safety and security, access medical records via a decentralised blockchain network. Patients have discretion over who gets access to their information and can allow healthcare professionals and organisations access to their medical records. We are employing a number of technologies, like MetaMask, Ganache, Web3JS, and Solidity, to develop a blockchain-based solution. Users may connect with blockchain networks via the browser plugin MetaMask, and a local blockchain development tool called Ganache offers a testing environment for blockchain-based apps. Programmers may interface with the Ethereum blockchain using Web3JS, a JavaScript framework, and Solidity, a language used to create smart contracts the blockchain of Ethereum. A centralised blockchain network that can safely store and distribute patient data is the predicted result of a blockchain-based system for medical record sharing. The system's interoperability and scalability may be ensured via integration with already-existing healthcare systems and databases. Medical record sharing may be automated with smart contracts, allowing for safe and auditable access based on predefined criteria and permissions.

Effective Techniques for Protecting the Privacy of Web Users

With the rapid growth of web networks, the security and privacy of online users are becoming more compromised. Especially, the use of third-party services to track users’ activities and improve website performance. Therefore, it is unavoidable that using personal information to create unique profiles may violate individuals’ privacy. Recently, several tools have been developed such as anonymity, anti-tracking, and browser plugins to ensure the protection of users from third-party tracking methods by blocking JavaScript programs and other website components. However, the current state lacks an efficient approach that provides a comprehensive solution. In this paper, we conducted a systematic analysis of the most common privacy protection tools based on their accuracy and performance by evaluating their effectiveness in correctly classifying tracking and functional JavaScript programs, then evaluating the estimated time the browser takes to render the pages for each tool. To achieve this, we automatically browsed the most 50 websites determined in 2022 and categorized them according to different fields to get the in-page (as part of HTML script tags), and all external JavaScript programs. Then we collected data and datasets of 1578 JavaScript elements and obtained six diverse Firefox profiles when the tools were enabled. The results found that Ghostery has the highest percentage of allowing most functioning scripts with the lowest average error rate (AER). While at the same time NoScript achieved the highest percentage of blocking most tracking scripts since it is the highest blocker of third-party services. After that, we examined the speed of the browser finding that, Ghostery improved the load time by 36.2% faster than the baseline, while Privacy Badger only reduced the load time by 7.1%. We believe that our findings can help users decide on a privacy tool that meets their needs. Moreover, researchers and developers can use our findings to improve the privacy of internet users by designing more effective privacy protection techniques.

Modelling of an Adaptive Network Model for Phishing Website Detection Using Learning Approaches

Phishing links are spread via text messages, social media platforms, and email by phishing attackers. Social engineering skills are used to visit phishing websites to trick the users and enter critical information related to personal data. The confidential data is stolen to defraud legitimate financial institutions or general websites for illegally attaining the benefits. Many machine learning-based solutions are in the enhancements and the technology of machine learning applications to detect the suggested phishing. The rules are used for a solution which depends on the extracted features, and few features require to lies on the services of third-party that, creating time-consuming and instability in the service of prediction. A deep learning-based framework is suggested to detect website of phishing. A framework is established to determine if there is a risk of phishing in real-time during the web page is visited by the user to give a message of warming by the browser plug-in. The prediction service in real-time merges the various techniques for enhancing the accuracy to lower the fake alarm rates and the time of computation which has the filtering whitelist, interception of the blacklist, and prediction of deep learning (DL). Various models of deep learning are compared using the different datasets in the module of machine learning prediction. The greatest accuracy is obtained as 99.18% by the adaptive Recurrent Neural Networks (a−RNN) model from the results of experiments to demonstrate the suggested feasibility solution.

Malware Detection in Web Browser Plugins Using API Calls with Permissions

With the exponential growth of internet users, web browsers play an essential role in gathering knowledge, social networking etc. Browser plugin/add-on is a unique feature of modern browsers that allows for adding new gimmicks to the browser functionality. Although this tool is handy, it poses a significant risk as it can collect and store users browsing history, passwords and more. Hence, attackers can try injecting malicious browser add-ons that can utilize security loopholes wherein the attacker may access user-critical data on the host device. The Smart Extension Malware Detector (SEMD), a reliable browser malware detection system that relies on extension development API calls and privileges using outfit machine learning approaches, was suggested and created by us. The research outcomes demonstrate that the SEMD model outperformed peer models while lowering the difficulty of the detection procedure.

Phishing Detection with Machine Learning

Abstract: The goal of our project is to implement a machine learning solution to the problem of detect- ing phishing and malicious web links. The end result of our project will be a software product which uses a machine learning algorithm to detect malicious URLs. Phishing is the technique of extracting user credentials and sensitive data from users by masquerading as a genuine website. In phishing, the user is provided with a mirror website which is identical to the legitimate one but with malicious code to extract and send user credentials to phishers. Phishing attacks can lead to huge financial losses for customers of banking and financial services. The traditional approach to phishing detection has been to either to use a blacklist of known phishing links or heuristically evaluate the attributes in a suspected phishing page to detect the presence of malicious codes. The heuristic function relies on trial and error to define the threshold, which is used to classify malicious links from benign ones. The drawback to this approach is poor accuracy and low adapt- ability to new phishing links. We plan to use machine learning to overcome these drawbacks by implementing some classification algorithms and comparing the performance of these algorithms on our dataset. We will test algorithms such as Logistic Regression, SVM, Decision Trees and Neural Networks on a dataset of phishing links from UCI Machine Learning repository and pick the best model to develop a browser plugin, which can be published as a browser extension.

API Testing Using Postman Tool

Abstract: Postman is one of the greatest API automation and documentation tools available today. Postman began as a simple Chrome browser plugin and has now grown to be a full API testing solution used by 5 million developers and over 100,000 enterprises all over the world. With a $2 billion value, it's a unicorn in its own right, and it's become the go-to platform for creating enterprise APIs. As the API economy continues to growing, more challenges are created for developers. The old methods of manually creating and testing APIs no longtime scale as today’s software and services can interface with hundreds of APIs within a single application. Development, testing, and delivery teams must work together to make sure that applications work barrier with APIs to provide a business advantage, rather than cause a business obstacle. Collaboration and operational efficiency are the keys to sharing modern API- powered applications. And this is the space that Postman plays in. In one of our GlobalLogic projects related to a Banking as well as Financial Services Customer Communications Management platform that is CCMP, we need to use Postman and its CI/CD add-on Newman to create, customize and automate Web API tests. This white paper and technical report narrate our passage direct this automation and showcases some learnings and best practices realized along the way. This is our view from the trenches on the capacity and possibilities of the Postman platform for API testing

Measuring the burden of infodemics on health outcomes through harmonized global metrics

Abstract Issue/problem Infodemics happen when an excess of information makes it difficult for people to discern what they see and hear to make good health decisions. Several challenges limit the usefulness of applying infodemiology research to the practice of managing infodemics including inconsistency in how information exposure is measured and a lack of focus on assessing associations with health behaviors. Description of the problem In 2021, WHO partnered with the University of Sydney to develop a study toolkit. We sought to create novel tools for measuring information exposure that can be easily deployed, linked to surveys measuring health behaviors, and implements a standardized study protocol so that data can be directly synthesized into a global analysis of information risk factors associated with health behaviors. Results A web-based study platform was developed, comprising tools for capturing information exposures within studies that link to health behavior surveys. The first tool is a smartphone application that asks users to actively record relevant information they see or hear in diary. The second application is a web browser plugin that passively tracks webpages with relevant keywords. Because localized studies follow a standardized protocol and de-identified participant data are recorded in a common format, local study investigators can opt-in to contributing study data to support global surveillance efforts. Lessons Through standardization of measurement tools and relevant study protocols, the toolkit can be used to quickly collect and synthesize data for global or regional analysis of infodemics, including in Europe. Validation of the toolkit in the field is needed to inform its open-source release. Key messages • A toolkit for measuring information risk factors associated with behavioral outcomes was developed. • Global collaboration using the toolkit can improve synthesisability of studies investigating infodemic burden of disease.