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Mashael M. Alsulami

Bio: Mashael M. Alsulami is an academic researcher. The author has contributed to research in topics: Mobile instant messaging & Recommender system. The author has an hindex of 1, co-authored 3 publications receiving 4 citations.

Papers
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Proceedings ArticleDOI
01 May 2019
TL;DR: A survey of 257 WhatsApp Saudi users to determine their behavior and understanding of storage related features and to identify factors that impact the storage usage of IM applications shows a significant impact of duplicate and unwanted content on the storage space of WhatsApp.
Abstract: With the rapid increase in the use of mobile devices, many users use mobile instant messaging (IM) services as a way to easily communicate and connect with friends, family members, and others. Instant messaging (IM) applications provide several services such as chatting, voice calls, video calls and multimedia sharing. They are deployed with many features that aim to improve the quality of experience (QoE) for users of such services. However, the storage usage of IM applications may negatively impact the storage space of mobile devices in an unexpected way and degrade its performance. Some IM applications, such as WhatsApp, have some features and options to manage the storage usage but they are not fully visible or effective. Thus, this paper aims to explore and understand user's perception of storage related features in IM applications. We conduct a user study by considering WhatsApp application as a case study due to its popularity among other IM applications. We present a survey of 257 WhatsApp Saudi users to determine their behavior and understanding of storage related features and to identify factors that impact the storage usage of IM applications. Our results show a significant impact of duplicate and unwanted content on the storage space of WhatsApp. The results show that 56.6% of participants misunderstood the functionality of the auto-download feature and 55% of participants were not aware of the existing of storage management features. We believe that our findings will have a potential role in making better design decisions to improve the usability of storage-related features provided in several IM applications.

5 citations

Journal ArticleDOI
TL;DR: The proposed SentiFilter model is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics and is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.
Abstract: Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual’s needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users’ explicit feedback. Findings also indicate that sentiment of users’ comments does not reflect users’ perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users’ explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.

3 citations

Journal ArticleDOI
TL;DR: This paper investigates the relationship between user attitude and user behavior among users from the Makkah region in Saudi Arabia to determine the applicability of considering users’ behaviors, as indicators of their attitudes towards unwanted content, and proposes a semi-explicit attitude measure to infer user attitude from user-selected examples.
Abstract: The high volume of user-generated content caused by the popular use of online social network services exposes users to different kinds of content that can be harmful or unwanted. Solutions to protect user privacy from such unwanted content cannot be generalized due to different perceptions of what is considered as unwanted for each individual. Thus, there is a substantial need to design a personalized privacy protection mechanism that takes into consideration differences in users’ privacy requirements. To achieve personalization, a user attitude about certain content must be acknowledged by the automated protection system. In this paper, we investigate the relationship between user attitude and user behavior among users from the Makkah region in Saudi Arabia to determine the applicability of considering users’ behaviors, as indicators of their attitudes towards unwanted content. We propose a semi-explicit attitude measure to infer user attitude from user-selected examples. Results revealed that semi-explicit attitude is a more reliable attitude measure to represent users’ actual attitudes than self-reported preferences for our sample. In addition, results show a statistically significant relationship between a user’s commenting behavior and the user’s semi-explicit attitude within our sample. Thus, commenting behavior is an effective indicator of the user’s semi-explicit attitude towards unwanted content for a user from the Makkah region in Saudi Arabia. We believe that our findings can have positive implications for designing an effective automated personalized privacy protection mechanism by reproducing the study considering other populations.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used.
Abstract: In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers’ inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user’s system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis, we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

9 citations

Journal ArticleDOI
TL;DR: In this paper , the authors provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used.
Abstract: In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers’ inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user’s system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

9 citations

Journal ArticleDOI
TL;DR: The results suggest that conjoint analysis can improve users’ segmentation and consequently provide better solutions for avoiding the gap betweenusers’ concerns, attitudes, and behavior.
Abstract: Personal privacy on online social networks (OSN) is becoming increasingly important. The collection and misuse of personal information can affect people’s behavior and can have a broader impact on civil society. The aim of this paper is to explore the privacy paradox phenomenon on OSNs that is reflected in the gap between OSN users’ privacy concerns and behavior and to introduce a new segmentation framework based on preference data from conjoint analysis. For the purpose of the study, an online survey on four dimensions of OSNs has been conducted. Conjoint analysis has been employed on collected data to reveal users’ preferences, followed by two-step cluster analysis for the preference-based segmentation. The characteristics of the resulting clusters were compared with self-reported behavior and privacy concerns, as well as the results of the Westin Privacy Segmentation approach. The results suggest that conjoint analysis can improve users’ segmentation and consequently provide better solutions for avoiding the gap between users’ concerns, attitudes, and behavior.

4 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an approach based on the simultaneous use of user eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset.
Abstract: The proposed paper introduces an innovative methodology useful to assign intelligent scores to web pages. The approach is based on the simultaneous use of User eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset. Specifically, the UX tool analyses different parameters addressing the score, such as navigation time, number of clicks, and mouse movements for page, finding possible outliers, the ANN are able to predict outliers, and the LSTM processes the web pages tags together with UX and user scores. The final web page score is assigned by the LSTM model corrected by the UX output and improved by the navigation user score. This final score is useful for the designer by suggesting the tags typologies structuring a new web page layout of a specific topic. By using the proposed methodology, the web designer is addressed to allocate contents in the web page layout. The work has been developed within a framework of an industry project oriented on the formulation of an innovative AI interface for web designers.

3 citations

Journal ArticleDOI
TL;DR: The proposed SentiFilter model is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics and is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.
Abstract: Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual’s needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users’ explicit feedback. Findings also indicate that sentiment of users’ comments does not reflect users’ perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users’ explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.

3 citations