5,026 research outputs found
An Empirical Study on Android for Saving Non-shared Data on Public Storage
With millions of apps that can be downloaded from official or third-party
market, Android has become one of the most popular mobile platforms today.
These apps help people in all kinds of ways and thus have access to lots of
user's data that in general fall into three categories: sensitive data, data to
be shared with other apps, and non-sensitive data not to be shared with others.
For the first and second type of data, Android has provided very good storage
models: an app's private sensitive data are saved to its private folder that
can only be access by the app itself, and the data to be shared are saved to
public storage (either the external SD card or the emulated SD card area on
internal FLASH memory). But for the last type, i.e., an app's non-sensitive and
non-shared data, there is a big problem in Android's current storage model
which essentially encourages an app to save its non-sensitive data to shared
public storage that can be accessed by other apps. At first glance, it seems no
problem to do so, as those data are non-sensitive after all, but it implicitly
assumes that app developers could correctly identify all sensitive data and
prevent all possible information leakage from private-but-non-sensitive data.
In this paper, we will demonstrate that this is an invalid assumption with a
thorough survey on information leaks of those apps that had followed Android's
recommended storage model for non-sensitive data. Our studies showed that
highly sensitive information from billions of users can be easily hacked by
exploiting the mentioned problematic storage model. Although our empirical
studies are based on a limited set of apps, the identified problems are never
isolated or accidental bugs of those apps being investigated. On the contrary,
the problem is rooted from the vulnerable storage model recommended by Android.
To mitigate the threat, we also propose a defense framework
Encouraging Privacy-Aware Smartphone App Installation: Finding out what the Technically-Adept Do
Smartphone apps can harvest very personal details
from the phone with ease. This is a particular privacy concern.
Unthinking installation of untrustworthy apps constitutes risky
behaviour. This could be due to poor awareness or a lack of knowhow:
knowledge of how to go about protecting privacy. It seems
that Smartphone owners proceed with installation, ignoring any
misgivings they might have, and thereby irretrievably sacrifice
their privacy
Analyzing user reviews of messaging Apps for competitive analysis
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends
Inclusiveness Matters: A Large-Scale Analysis of User Feedback
In an era of rapidly expanding software usage, catering to the diverse needs
of users from various backgrounds has become a critical challenge.
Inclusiveness, representing a core human value, is frequently overlooked during
software development, leading to user dissatisfaction. Users often engage in
discourse on online platforms where they indicate their concerns. In this
study, we leverage user feedback from three popular online sources, Reddit,
Google Play Store, and Twitter, for 50 of the most popular apps in the world to
reveal the inclusiveness-related concerns from end users. Using a
Socio-Technical Grounded Theory approach, we analyzed 23,107 posts across the
three sources and identified 1,211 inclusiveness related posts. We organize our
empirical results in a taxonomy for inclusiveness comprising 6 major
categories: Fairness, Technology, Privacy, Demography, Usability, and Other
Human Values. To explore automated support to identifying inclusiveness-related
posts, we experimented with five state-of-the-art pre-trained large language
models (LLMs) and found that these models' effectiveness is high and yet varied
depending on the data source. GPT-2 performed best on Reddit, BERT on the
Google Play Store, and BART on Twitter. Our study provides an in-depth view of
inclusiveness-related user feedback from most popular apps and online sources.
We provide implications and recommendations that can be used to bridge the gap
between user expectations and software so that software developers can resonate
with the varied and evolving needs of the wide spectrum of users
Giving Users Control Over How Peers Handle Their Data: A Design Science Study
In today’s interconnected world, Internet users are increasingly concerned about losing control over the data they share with peers, which indicates a need for higher levels of control and notification mechanisms. We address this need by building on design science methodology and developing a socio-technical artifact, i.e., a peer-privacy-friendly online messaging service. We draw on Malhotra et al.’s (2004) Internet Users’ Information Privacy Concerns framework and refine and evaluate our artifact via focus groups, interviews, and a survey among users of online messaging services. Our artifact provides senders with the ability to control how their personal information is processed by peers and allows receivers to be made aware of the sender’s privacy expectations. We contribute to the growing literature on peer privacy concerns by developing and evaluating design requirements, principles, and an instantiation that can mitigate peer privacy concerns that go beyond concerns about organizational data practices
Social media disinformation in the pre-electoral period in Portugal
Since the North American presidential election of 2016, the role of social media on the propagation of misleading news and its instrumentalization by partisan groups has raised concerns. In this article we analyse the contents of 47 Facebook pages and 39 Facebook groups prior to the Portuguese parliamentary election of 6th of October of 2019 to track disinformation. Groups and pages to monitor were selected through a process that combined the number of fans or members, the proportion of political content, and the number of posts per week. We concluded that disinformative content was prevalent in the pages and groups monitored, that several political actors had a relevant influence on the debate and that most disinformation stemmed from the spinning of both mainstream and non-mainstream news to serve a political purpose
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