Social media are widely used by people to help satisfying personal and social needs. Examples include the enhancement of self-image, self-esteem, complementarity, relatedness and popularity. However, the relationship with social media can become problematic and lead to hurt various aspects of life, including wellbeing, psychological and emotional state and sociability. Existing literature provided evidence that obsessive and excessive use of social media can be associated with behavioural addiction symptoms such as conflict, mood modification, salience, tolerance, withdrawal and relapse. Research has also shown that social media can be equipped or augmented with tools to help users who are willing to change their problematic attachment behaviour. Designing such behaviour change tools can be challenging because people differ in their problematic attachment to social media. Unlike existing literature, which focuses on understanding the psychological correlates of social media activity and reasons that facilitate attachment. This thesis aims to explore the real-world experience of people who have a problematic attachment to social media and the role of social media design in such attachment. In order to achieve the goal of the thesis, multi-phase qualitative studies with people who experienced problematic attachment have been conducted. This helped to achieve a deep understanding of the role of social media in facilitating problematic attachment and reveal emotions and psychological states associated with it as well as the social media design features which contribute to triggering such states. The findings emerged through multi-phase qualitative studies helped developing user archetypes characterising how people differ in their problematic attachments to social media. These behavioural archetypes are intended to help the design process of software-assisted solutions to keep a healthy relationship with social media. The thesis evaluates how the archetypes can help a design team communication and engagement and aid a more creative and efficient design process.
Abstract Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g. collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigates smartphone usage before and after COVID-19. Our study uses a dataset from a smartphone app that objectively logs users' activities, including apps accessed and each app session's start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on the top 15 apps used in both periods . Mann-Whitney U test was used for the inferential analysis. The results revealed that time spent on the smartphone has increased since COVID-19. Emerging adults were found to spend more time on the smartphone compared to adults during both periods. The time spent on social media apps has increased since COVID-19. Females were found to spend more time on social media than males. Females were also found to be more likely to launch social media apps than males. There has been an increase in the number of people who use gaming apps since the pandemic. Value: The use of objectively collected data is a methodological strength of our study. We draw parallels on the usage of smartphone with contexts similar to COVID-19 period, especially concerning limitation on social gathering, including working from home for extended periods. Our dataset is made available to other researchers for benchmarking and future comparisons.
Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.
People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).
Procrastination on social networking sites (SNS) can impact academic performance and user's well-being. SNSs embed features that encourage users to be always connected and updated, e.g., the notification features. Such persuasive features can exploit peer pressure as well and lead users to believe they are expected to interact immediately, especially for those who may have less impulse control and seek for relatedness and popularity. We argue that SNS can be built to host countermeasures for such behavior and help people regulate their usage and preoccupation about it better. In this paper, we presented a mixed-method study including a qualitative (i.e., focus groups, diary, interviews, and co-design) and a quantitative phase (i.e., a survey) with 334 participants. Through the qualitative phase, we identified: (1) features of an SNS seen by participants as facilitators for procrastination, e.g., notification, immersive design, and surveillance of presence, and (2) countermeasures, such as reminders, chat timer, and goal setting, can be facilitated via SNS design to combat procrastination, and (3) a pairing between the features and the countermeasures. We then (4) confirmed these results and the pairing through the survey phase. Our study showed that countermeasures could be implemented to be universal across all SNS on one or even more device.
The information that is saved in the cloud about users is protected by a number of different safeguards in order to facilitate the development of smart cities. Phishing and other forms of social engineering are two examples of misleading tactics that may be used by hostile actors to get sensitive information about users. Phishing is still the first step of a multistage assault, despite the significant technological advancements that have been made to it in recent years. Phishing kits have evolved to become attack tools that are much simpler, more user-friendly, and more readily available as time has gone. Indicators of a successful phishing assault include using foreign characters in the URL, typosquatting of prominent domain names, reserved characters in redirections, and multichain phishing. When papers with these types of phishing URLs are uploaded to cloud storage, hackers get a helping push in the right direction. The use of cloud servers in the commission of these assaults is becoming more common. The currently available software to disallow list phishing URLs does not provide sufficient protection against multilevel phishing and instead places the onus of safety on the user to protect themselves. In addition, the immutability of blockchain data and the avalanche effect both demonstrate their effectiveness as crucial safety measures. In view of the recent advances in technology, we suggest an implementation of filtering that is based on blockchain technology to safeguard the cloud-based data of users. The Phish Block that has been presented is able to recognize homographic phishing URLs with an accuracy of 91 percent, thus ensuring the security of cloud storage.
Abstract Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g. collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigates smartphone usage before and after COVID-19. Our study uses a dataset from a smartphone app that objectively logs users' activities, including apps accessed and each app session's start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on the top 15 apps used in both periods. Mann-Whitney U test was used for the inferential analysis. The results revealed that time spent on the smartphone has increased since COVID-19. Emerging adults were found to spend more time on the smartphone compared to adults during both periods. The time spent on social media apps has increased since COVID-19. Females were found to spend more time on social media than males. Females were also found to be more likely to launch social media apps than males. There has been an increase in the number of people who use gaming apps since the pandemic. Value: The use of objectively collected data is a methodological strength of our study. We draw parallels on the usage of smartphone with contexts similar to COVID-19 period, especially concerning limitation on social gathering, including working from home for extended periods.
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