Biblio

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

The recent breakthroughs in Artificial Intelligence (AI) have allowed individuals to rely on automated systems for a variety of reasons. Some of these systems are the currently popular voice-enabled systems like Echo by Amazon and Home by Google that are also called as Intelligent Personal Assistants (IPAs). Though there are rising concerns about privacy and ethical implications, users of these IPAs seem to continue using these systems. We aim to investigate to what extent users are concerned about privacy and how they are handling these concerns while using the IPAs. By utilizing the reviews posted online along with the responses to a survey, this paper provides a set of insights about the detected markers related to user interests and privacy challenges. The insights suggest that users of these systems irrespective of their concerns about privacy, are generally positive in terms of utilizing IPAs in their everyday lives. However, there is a significant percentage of users who are concerned about privacy and take further actions to address related concerns. Some percentage of users expressed that they do not have any privacy concerns but when they learned about the "always listening" feature of these devices, their concern about privacy increased.

Mobile app developers today have a hard decision to make: to independently develop native apps for different operating systems or to develop an app that is cross-platform compatible. The availability of different tools and approaches to support cross-platform app development only makes the decision harder. In this study, we used user reviews of apps to empirically understand the relationship (if any) between the approach used in the development of an app and its perceived quality. We used Natural Language Processing (NLP) models to classify 787,228 user reviews of the Android version and iOS version of 50 apps as complaints in one of four quality concerns: performance, usability, security, and reliability. We found that hybrid apps (on both Android and iOS platforms) tend to be more prone to user complaints than interpreted/generated apps. In a study of Facebook, an app that underwent a change in development approach from hybrid to native, we found that change in the development approach was accompanied by a reduction in user complaints about performance and reliability.