Journal of Clinical Pharmacy and Therapeutics

Application development for the modern Web involves sophisticated engineering workflows – including user interface (UI) aspects. Such user interfaces comprise Web elements that are typically created with HTML/CSS markup and JavaScript-like languages, yielding Web documents. Their testing entails performing checks to examine visual and structural parts of the resulting UI software against requirements such as usability, accessibility, performance, or, increasingly, compliance with standards. However, current techniques are largely ad-hoc and tailor-made to specific classes of requirements or Web technologies and extensively require human-in-the-loop qualitative evaluations. Web UI evaluation so far has lacked formal foundations, which would provide assurances of compliance with requirements in an automatic manner. To this end, we devise a methodology and accompanying technical framework for web UIs. In our approach, requirements are formally specified in a spatio-temporal logic able to capture both the layout of visual components as well as how they change over time, as a user interacts with them. The technique we advocate is independent of the underlying technologies a Web application may be developed with, as well as the browser and operating system used. To concretely support the specification and evaluation of UI requirements, our framework is grounded on open-source tools for instrumenting, analyzing, and reporting spatio-temporal behaviors in webpages. We demonstrate our approach in practice over Web accessibility standards posing challenges for automated verification.

We study emerging traits of interpersonal and social trust in online social networks of needs (OSNNs), where trust interactions start online and evolve into in-person meetings. We present a lightweight web scraping solution to harness data from online social networks; thanks to it we were able to monitor a nation-wide portal for childcare and see the evolution of online reviews from both families and carers. We analysed the data by first considering topological information to test centrality metrics as proxies for trustworthiness. Next, we focused on features/profile analysis and tested the Castelfranchi–Falcone trust model from psychology (CF-T), fitting it to online reviews of childcare services. Even though such reviews are relatively scarce and seemingly skewed, we feature-engineered the CF-T model to predict the evolution of reviews, treated as proxies for trust. By aggregating CF-T scores at the regional level, we discovered a strong correlation with per capita GDP, which suggests that high levels of trust in social networks of needs reflect social capital.

Main tasks of usability experts for Web sites comprise the analysis of user interaction behavior on graphical user interfaces, the discovery of issues, and the derivation of improvements to the interface. The analysis of user interaction behavior and corresponding discovery of issues are made difficult by modern Web interfaces that incorporate dynamic interface elements and that orchestrate complex reactions to user responses. We propose a semi-automated approach for discovering visual stimuli, which capture summarized views of the interface as encountered by users during interaction. Discovered visual stimuli allow for meaningful aggregations of user interactions based on what users encountered on the interface such that the analysis by usability experts can relate the interface views with user interactions correctly and identify arising issues. We provide WebVSD as an implementation of the approach and perform a set of evaluations with real-world Web sites that show the accuracy of proposed methods in isolation and in the tool chain, as well as case studies and a survey of usability experts indicating the usefulness of the suggested approach.

Danmu (or “bullet screen”), a popular feature on video sharing platforms, plays a crucial role in facilitating knowledge sharing and learning. In recent years, danmu has drawn attention to automatic generation methods. However, existing methods mostly utilize limited content sources, such as the video itself (e.g., subtitles) and neighboring danmus, while other valuable sources remain underexplored. To this end, this paper proposes a Category-Guided Note-to-Danmu conversion model (CG-NTD) by leveraging user-generated notes. The model is designed to identify unique contents within the notes and convert them into danmus, while also showing the source note categories. CG-NTD classifies the notes by fusing them with subtitle and neighboring danmu features. Then, it uses a cross-attention mechanism to integrate the note’s category feature with note, subtitle, and danmu contexts, to identify three keywords from the notes as the generated danmus. Using Bilibili as the research site, we implement a plugin prototype named BNoteToDanmu. Automatic and human evaluations reveal that CG-NTD outperforms BiLSTM, mT5, and BERT baselines in Precision, Recall, and F1-score metrics, and generates more understandable and relevant danmus than ChatGPT. Moreover, the plugin demonstrates promising applications, such as assisting users in viewing videos, posting danmus, and recognizing high-quality notes. These findings offer insights into leveraging user creations to generate danmu to enhance its learning value on video sharing platforms.

In the evolving field of Natural Language Processing (NLP), understanding the temporal context of text is increasingly critical for applications requiring advanced temporal reasoning. Traditional pre-trained language models like BERT, which rely on synchronic document collections such as BookCorpus and Wikipedia, often fall short in effectively capturing and leveraging temporal information. To address this limitation, we introduce BiTimeBERT 2.0, a novel time-aware language model pre-trained on a temporal news article collection. BiTimeBERT 2.0 incorporates temporal information through three innovative pre-training objectives: Extended Time-Aware Masked Language Modeling (ETAMLM), Document Dating (DD), and Time-Sensitive Entity Replacement (TSER). Each objective is specifically designed to target a distinct dimension of temporal information: ETAMLM enhances the model’s understanding of temporal contexts and relations, DD integrates document timestamps as explicit chronological markers, and TSER focuses on the temporal dynamics of ”Person” entities. Moreover, our refined corpus preprocessing strategy reduces training time by nearly 53%, making BiTimeBERT 2.0 significantly more efficient while maintaining high performance. Experimental results show that BiTimeBERT 2.0 achieves substantial improvements across a broad range of time-related tasks and excels on datasets spanning extensive temporal ranges. These findings underscore BiTimeBERT 2.0’s potential as a powerful tool for advancing temporal reasoning in NLP.

Suicide remains a critical global health issue, with rising numbers claiming more lives each year despite ongoing prevention efforts. Current research has extensively explored factors influencing suicidal tendencies, emphasizing trauma, mental health disorders, and social relationships. However, traditional studies often relied on traditional data sources and often examined risk factors in isolation, which may not fully capture the dynamics observed in social media platforms. To address these limitations, our study utilizes data from r/SuicideWatch and r/Teenagers to analyze the emotional sentiment and explore themes associated with suicidal ideation, with r/Teenagers serving as a comparative reference. By leveraging natural language processing (NLP) techniques and statistical methodologies, including sentiment analysis and BERTopic modeling, we aim to gain deeper insights into the factors contributing to suicidal thoughts. Using TextBlob, our findings reveal a significant difference in sentiment between the two subreddits, with r/SuicideWatch posts predominantly expressing challenges and distressing emotions. Through BERTopic analysis, we identified key themes such as emotional challenges related to romantic relationships, academic pressure, and substance use concerns in r/SuicideWatch, highlighting their strong association with suicidal ideation. While r/Teenagers had some similar themes regarding struggles with loneliness and academics, the topics were focused more on general adolescent concerns. These findings demonstrate that advanced NLP methods can effectively analyze large-scale social media data, providing valuable insights into the multifaceted nature of suicidal ideation and emphasizing the need for targeted intervention strategies. Suggested improvements include enhancing relationship counseling and peer support networks, implementing school-based mental health programs, and leveraging social media for real-time support and awareness campaigns. By understanding the emotional and thematic nuances of online discussions, these strategies can more effectively address the multifaceted factors contributing to mental health challenges and reduce the risk of suicidal behavior.

Web data are often modelled as complex networks in which entities interact and form communities. Nevertheless, web data evolves over time, and network communities change alongside it. This makes Community Detection (CD) in dynamic graphs a relevant problem, calling for evolutionary CD algorithms. The choice and evaluation of such algorithm performance is challenging because of the lack of a comprehensive set of benchmarks and specific metrics. To address these challenges, we propose CoDÆN – COmmunity Detection Algorithms in Evolving Networks – a benchmarking framework for evolutionary CD algorithms in dynamic networks, that we offer as open source to the community. CoDÆN allows us to generate synthetic community-structured graphs with known ground truth and design evolving scenarios combining nine basic graph transformations that modify edges, nodes, and communities. We propose three complementary metrics (i.e. Correctness, Delay, and Stability) to compare evolutionary CD algorithms. Armed with CoDÆN, we consider three evolutionary modularity-based CD approaches, dissecting their performance to gauge the trade-off between the stability of the communities and their correctness. Next, we compare the algorithms in real Web-oriented datasets, confirming such a trade-off. Our findings reveal that algorithms that introduce memory in the graph maximise stability but add delay when abrupt changes occur. Conversely, algorithms that introduce memory by initialising the CD algorithms with the previous solution fail to identify the split and birth of new communities. These observations underscore the value of CoDÆN in facilitating the study and comparison of alternative evolutionary community detection algorithms.

Nowadays, X (formerly known as Twitter) is an important source of information and latest updates during ongoing events, such as disaster events. However, the huge number of tweets posted during a disaster makes identification of relevant information highly challenging. Therefore, a summary of the tweets can help the decision-makers to ensure efficient allocation of resources among the affected population. There exist several automated summarization approaches that can generate a summary given the tweets related to a disaster. Development of these automated summarization approaches require availability of ground-truth summary of the dataset for verification. However, the number of publicly available datasets along with the ground-truth summary for disaster events are still inadequate. To improve this situation, we need to create more ground-truth summaries. Existing approaches for ground-truth summary generation rely on the annotators’ wisdom and intuition. This process requires immense human effort and significant time. Moreover, the selection of the important tweets from the humongous set of input tweets often results in sub-optimal choice of tweets in the final summary. Therefore, to handle these challenges, we propose a hybrid approach (PORTRAIT) for ground-truth summary generation, where we partly automate the procedure to improve the quality of ground-truth summary and reduce human effort and time. We validate the effectiveness of PORTRAIT on nine disaster events through quantitative and qualitative analysis. We prepare and release the ground-truth summaries for nine disaster events, which consist of both natural and man-made disaster events belonging to five different continents.

The sudden onset of the recently concluded COVID-19 pandemic has driven substantial progress in various scientific fields. One notable example is the comprehension of public vaccination attitudes and the timely monitoring of their fluctuations through social media platforms. This approach can serve as a cost-effective means to supplement surveys in gathering public vaccine hesitancy levels. In this paper, we propose a deep-learning framework leveraging textual posts on social media to extract and track users’ vaccination stances in near real-time. Compared to previous works, we integrate into the framework the recent posts of a user’s social network friends to collaboratively detect the user’s genuine attitude towards vaccination. Based on our annotated dataset from X (previously known as Twitter), the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to the state-of-the-art text-only models. Using this framework, we successfully confirm the feasibility of using social media to track the evolution of vaccination attitudes in real life. In addition, we illustrate the generality of our framework in extracting other public opinions such as political ideology. We further show one practical use of our framework by validating the possibility of forecasting a user’s vaccine hesitancy changes with information perceived from social media.

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this paper proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.

User location discovery from social media is crucial for location-based services like emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships, but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this paper, we propose a Twitter user geolocation method based on location feature enhancement, to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.

This study investigates the concept of frames in the realm of online polarization, with a focus on social media platforms. The research extends the understanding of how frames—emerging, complex, and often subtle concepts—become prominent in online conversations that are polarized. The study proposes a comprehensive methodology for identifying and characterizing these frames, integrating machine learning techniques, network analysis algorithms, and natural language processing tools. This method aims for generalizability across multiple platforms and types of user engagement. Two novel metrics, homogeneity and relevancy are introduced for the rigorous evaluation of identified frame candidates. Grounded in several foundational presumptions, including the role of topics and multi-word expressions in framing, the study sheds light on how frames emerge and gain significance within digital communities. The research questions explored include the methods for identifying frames, the variability and significance of these frames, and the effectiveness of different computational techniques in this context. To validate the approach, we present a case study of the 2021 Chilean presidential election, using data from both Twitter and WhatsApp platforms. This real-world application allows for the examination of how frames fluctuate in response to events and the specific mechanisms of platforms. Overall, the study makes several key contributions to the field, offering new insights and methodologies for analyzing the complexities of online polarization. It serves as groundwork for future research on the dynamics of online communities, especially those associated with distinctly polarized events.

In the evolving education landscape, this survey investigates the integration and transformation of educational paradigms using social network analysis (SNA). This article examines the fundamentals of SNA, including nodes, edges, centrality metrics, and network dynamics, for a comprehensive understanding of the education domain. It guides researchers through various applications of SNA in education, such as student–teacher networks and institutional collaborations, highlighting the advantages and challenges of these complex interactions. The article assesses the methodologies used in educational SNA, including data collection strategies and the associated ethical considerations. The survey also discusses various case studies and applications where SNA facilitates well-informed decision-making, enhanced academic collaboration, and the evaluation of student performance. This article focuses on the transformative potential of SNA and acknowledges the limitations, ethical dilemmas, and technological challenges in the field. It concludes with a forward-looking perspective on the future of SNA in education, showcasing supportive technological advancement. This survey highlights the evolution of SNA since its incorporation into educational research and practices.

Ransomware is an increasingly prevalent form of malware targeting end-users, governments, and businesses. As it has evolved, adversaries added new capabilities to their arsenal. We propose a next-generation browser-based ransomware, RøB , which performs its malicious actions via web technologies, File System Access API (FSA) and WebAssembly (Wasm). RøB uses this API through the victims’ browsers; hence, it does not require the victims to download and install malicious binaries. We performed extensive evaluations with three different OSs, 23 file formats, 29 distinct directories, five cloud providers, and four antivirus solutions. Our evaluations show that RøB can encrypt various types of files in the local and cloud-integrated directories, external storage devices, and network-shared folders of victims. Our experiments also reveal that popular cloud solutions, Box Individual and Apple iCloud can be severely affected by RøB . Moreover, we conducted tests with commercial antivirus software such as AVG, Avast, Kaspersky, and Malware Bytes that perform sensitive directory and suspicious behavior monitoring against ransomware. We verified that RøB can evade these antivirus software and encrypt victim files. Moreover, existing ransomware detection solutions in the literature also cannot be a remedy against RøB due to its distinct features. Therefore, in this paper, we also propose RøBguard , a new detection system for RøB -like attacks. RøBguard monitors the web applications that use the FSA API via function hooking and uses a machine learning classifier to detect RøB -like attacks. We implemented a proof of concept version of RøBguard and our evaluation results show that RøBguard can detect RøB -like browser-based ransomware attacks effectively. We also provide future research directions that should be addressed in this domain.

Despite stringent data protection regulations, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other country-specific laws, numerous websites continue to use cookies to track user activities, raising significant privacy concerns. This study aims to investigate the compliance of e-commerce websites with these regulations from a cookie perspective and explore potential variations in cookie policies across different countries. We conducted a comprehensive analysis of 360 popular e-commerce websites (44,323 cookies) across multiple countries, examining cookie attributes and their potential links to privacy and security breaches. Our findings revealed that 73% of third-party cookies function as tracker cookies, with around 40% breaching lifecycle regulations. Additionally, 85% are vulnerable to potential cross-site scripting (XSS) attacks, while only 349 out of 44,323 adhere to robust measures aimed at combating cross-site request forgery (CSRF) attacks. We also discovered instances of masquerading cookies, where third-party cookies disguise themselves as first-party cookies, enabling unauthorized user tracking without consent. To the best of our knowledge, this study is the first to comprehensively analyze the compliance of e-commerce websites with the GDPR, CCPA, and country-specific regulations concerning cookie policies across different jurisdictions. Our findings highlight the urgent need for uniform and consistent cookie policies across websites and jurisdictions, as well as robust enforcement mechanisms and increased transparency to ensure compliance with data protection regulations. This research contributes to the ongoing discourse on privacy protection and underscores the importance of addressing the challenges posed by insecure cookie practices in the e-commerce sector.