Recommendations for Secondary Analysis of Qualitative Data

Publications and presentations resulting from secondary analysis of qualitative research are less common than similar efforts using quantitative secondary analysis, although online availability of high-quality qualitative data continues to increase. Advantages of secondary qualitative analysis include access to sometimes hard to reach participants; challenges include identifying data that are sufficient to respond to purposes beyond those the data were initially gathered to address. In this paper I offer an overview of secondary qualitative analysis processes and provide general recommendations for researchers to consider in planning and conducting qualitative secondary analysis. I also include a select list of data sources. Well-planned secondary qualitative analysis projects potentially reflect efficient use or reuse of resources and provide meaningful insights regarding a variety of subjects

Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media

Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits

Fake Content Detection in the Information Exponential Spreading Era

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance

Cluster based jamming and countermeasures for wireless sensor network MAC protocols

A wireless sensor network (WSN) is a collection of wireless nodes, usually with limited computing resources and available energy. The medium access control layer (MAC layer) directly guides the radio hardware and manages access to the radio spectrum in controlled way. A top priority for a WSN MAC protocol is to conserve energy, however tailoring the algorithm for this purpose can create or expose a number of security vulnerabilities. In particular, a regular duty cycle makes a node vulnerable to periodic jamming attacks. This vulnerability limits the use of use of a WSN in applications requiring high levels of security. We present a new WSN MAC protocol, RSMAC (Random Sleep MAC) that is designed to provide resistance to periodic jamming attacks while maintaining elements that are essential to WSN functionality. CPU, memory and especially radio usage are kept to a minimum to conserve energy while maintaining an acceptable level of network performance so that applications can be run transparently on top of the secure MAC layer. We use a coordinated yet pseudo-random duty cycle that is loosely synchronized across the entire network via a distributed algorithm. This thwarts an attacker\u27s ability to predict when nodes will be awake and likewise thwarts energy efficient intelligent jamming attacks by reducing their effectiveness and energy-efficiency to that of non-intelligent attacks. Implementing the random duty cycle requires additional energy usage, but also offers an opportunity to reduce asymmetric energy use and eliminate energy use lost to explicit neighbor discovery. We perform testing of RSMAC against non-secure protocols in a novel simulator that we designed to make prototyping new WSN algorithms efficient, informative and consistent. First we perform tests of the existing SMAC protocol to demonstrate the relevance of the novel simulation for estimating energy usage, data transmission rates, MAC timing and other relevant macro characteristics of wireless sensor networks. Second, we use the simulation to perform detailed testing of RSMAC that demonstrates its performance characteristics with different configurations and its effectiveness in confounding intelligent jammers

Defense by Deception against Stealthy Attacks in Power Grids

Cyber-physical Systems (CPSs) and the Internet of Things (IoT) are converging towards a hybrid platform that is becoming ubiquitous in all modern infrastructures. The integration of the complex and heterogeneous systems creates enormous space for the adversaries to get into the network and inject cleverly crafted false data into measurements, misleading the control center to make erroneous decisions. Besides, the attacker can make a critical part of the system unavailable by compromising the sensor data availability. To obfuscate and mislead the attackers, we propose DDAF, a deceptive data acquisition framework for CPSs\u27 hierarchical communication network. Each switch in the hierarchical communication network generates a random pattern of addresses/IDs by shuffling the original sensor IDs reported through it. During the data acquisition from remotely located sensors to the central controller, the switches craft the network packets by replacing a few sensors\u27 associated addresses/IDs with the generated deceptive IDs and by adding decoy data for the rest. While misleading the attackers, the control center must retrieve the actual data to operate the system correctly. We propose three remapping mechanisms (e.g., seed-based, prediction-based, and hybrid) and compare their robustness against different stealthy attacks. Due to the deception, artfully altered measurements turn into random data injections, making it easy to remove them as outliers. As the outliers and the estimated residuals contain the potential attack vectors, DDAF can detect and localize the attack points and the targeted sensors by analyzing this information. DDAF is generic and scalable to be implemented in any hierarchical CPSs network. Experimental results on the standard IEEE 14, 57, and 300 bus power systems show that DDAF can detect, mitigate, and localize up-to 100% of the stealthy cyberattacks. To the best of our knowledge, this is the first framework that implements complete randomization in the data acquisition of the hierarchical CPSs

Looking at the Lanham Act: Images in Trademark and Advertising Law

Words are the prototypical regulatory subjects for trademark and advertising law, despite our increasingly audiovisual economy. This word-focused baseline means that the Lanham Act often misconceives its object, resulting in confusion and incoherence. This Article explores some of the ways courts have attempted to fit images into a word-centric model, while not fully recognizing the particular ways in which images make meaning in trademark and other forms of advertising. While problems interpreting images are likely to persist, this Article suggests some ways in which courts could pay closer attention to the special features of images as compared to words