Message Patterns Research Articles

Characterizing electronic messaging use among hospitalists and its association with patient volumes.

Secure electronic messaging is increasingly being utilized for communications in healthcare settings. While it likely increases efficiency, it has also been associated with interruptions, high message volumes, and risk of errors due to multitasking. We aimed to characterize patterns of secure messaging among hospitalists to understand the volume of messages, message patterns, and impact on hospitalist workload. This was a retrospective cross-sectional study of Epic Secure Chat secure electronic messages received and sent by hospitalists from April 1 to April 30, 2023 at a large academic medical center. Number of conversations per day, number of chats sent and accessed per hour, and average minutes between when a chat was sent and accessed (lag time) were analyzed using a Pearson correlation coefficient test. Measures were plotted against patient volume and time of day. Hospitalists sent or received an average of 130 messages per day with an average of 13 messages sent or received per hour. The median lag time was 39 s. There was a statistically significant correlation between hospital medicine morning census and number of conversations per day, number of chats sent per hour, and number of chats accessed per hour, but census did not impact lag time. Secure messaging volumes may be higher than previously reported, which may affect hospitalist workload and workflow and have unintended effects on interruptions, multitasking, and medical errors. Additional work should be done to better understand local messaging patterns and opportunities to optimize volume of work and distractions.

A Local-First Approach for Green Smart Contracts

Shared code in blockchains, known as smart contracts , stands to replace important parts of our digital governance and financial infrastructure. The permissionless execution of smart contracts is tightly coupled to cryptocurrencies and Proof-of-Work blockchains. As a result, smart contracts inherit the environmental impact of Proof-of-Work blockchains, such as its energy consumption, carbon footprint, and electronic waste. The four concepts of relaxed consistency, strong identities, probabilistic consensus, and the use of liabilities instead of assets may change the status quo. This work explores the integration of these concepts to decouple smart contracts from Proof-of-Work blockchains. By means of a local-first approach, which may expose users to inconsistent ephemeral contract states, the architecture of smart contracts can be transformed to become green. Because such contract states may be dropped, we base the interactions between users on liabilities. We propose a novel paradigm for smart contract architectures, named Green Smart Contracts, that is based on a local-first approach. Furthermore, we present and implement a prototype solution for this paradigm. We validate the need for a mechanism to resolve consistency violations by replaying the contract calls of a real smart contract. Our simulation shows that violations occur more often (13% of contract invocations) when using liabilities than when using a traditional blockchain (3% of contract invocations). However, we additionally validate that they can be avoided using a consensus mechanism, and our experiments show that a publish-subscribe messaging pattern uses the fewest messages to do so, though it may not be applicable for use cases that disallow the inherent imbalance in the messaging between peers. Our carbon emission estimation shows that a Green Smart Contract approach lowers carbon emissions by 52.31% when compared with the messaging behavior of a typical peer-to-peer blockchain with 1000 nodes.

Association of symptom severity, anxiety, and health literacy with portal usage among patients with cancer.

1567 Background: Patient portals within electronic health records (EHR) have become increasingly essential in promoting patient-centered oncology care. They offer opportunities for completing symptom surveys that can aid in symptom management, facilitate patient-clinician communication, and enable health monitoring. As part of the Northwestern University Improving the Management of Symptoms During and Following Cancer Treatment (NU IMPACT) study, we examined the relationship between symptom severity, anxiety, health literacy, and shared decision-making with usage of three common features in the patient portal (portal). Methods: This study included adults diagnosed with cancer in the NU IMPACT study. Participants completed baseline surveys on socio-demographics, health literacy (Single Item Literacy Screener), anxiety (Patient-Reported Outcomes Measurement Information System; PROMIS), with high anxiety scored as ≥60 vs <60, shared decision-making (CollaboRATE), and symptom severity (Patient-Reported Outcome version of the Common Terminology Criteria for Adverse Events; PRO-CTCAE). Portal activities one year following enrollment were extracted from EHRs. Portal usage patterns of patient-initiated messages, portal lab and clinical note views were counted as number of days the activity was used over the year and categorized as tertiles. Multivariable ordinal logistic regression models were used to examine associations between survey measures and ordered tertiles of patient portal activities. Results: Participants (N=3,464) were mostly female (66%), White (85%), with a mean (SD) age of 61(13) years old. The frequency ranges of patient-initiated messages over a year were 0-163 days, labs viewed were 0-350 days, and clinical notes viewed were 0-103 days. Participants with at least one severe or very severe symptom on the PRO-CTCAE were more likely to be in higher tertiles of patient-initiated portal messages (OR: 1.37, 95% CI: 1.17-1.61), lab views (OR: 1.48, 95% CI: 1.26-1.74), and clinical note views (OR: 1.19, 95% CI: 1.01-1.40). Participants with higher levels of anxiety were more likely to be in higher tertiles of patient-initiated portal messages (OR: 1.54, 95% CI: 1.28-1.85), lab views (OR: 1.22, 95% CI: 1.01-1.46), and clinical note views (1.42, 95% CI: 1.18-1.71). Participants with higher health literacy were more likely to be in higher tertiles of patient-initiated portal messages (OR: 1.38, 95% CI: 1.09-1.74) and lab views (OR: 1.34, 95% CI: 1.06-1.69), but not note views (OR: 1.03, 95% CI: 0.82-1.30). Shared decision making was not associated with portal usage. Conclusions: Patients with greater symptom severity and anxiety may turn to the portal for symptom management. Certain features within the portal may demand a higher level of health literacy for engagement. Understanding the factors that promote and impede portal usage can help optimize cancer care delivery.

Message patterns of online gender-based humor discriminatory practices biases stereotyping and disempowering tools through discourse analysis

This study explored the message patterns of gender-based humor in social media in different layers of discriminatory practices against certain genders, language biases against women and LGBT including elements of stereotyping and disempowering tools against the personal images of subordinate genders. This research used discourse analysis based on the mapped-out online posts and comments of the fourteen (14) profiles of individuals and extracted their important testimonies based on the collected online gender-based humor to elicit the message patterns. Gender-based humor online enhanced the language use in creating messages that express biases towards women and the LGBT. Humor has both implicit and explicit messages that stereotype women and LGBT as weak and slow. These senses of humor also disempower the women and LGBT's personal images as groups who are easily dominated or are cowards. As asserted, gender-based humor posed a threat to community as it highlights hierarchy-enhancing social roles. Gender-based humor in social media appeared as a mainstreamed form of social differentiation.

It's how I was raised: How ethnic-racial socialization patterns influence antiracism actions in minoritized emerging adults.

Research highlights the benefits of critical action on individual and community well-being; however, more needs to be understood about the ways ethnic-racial socialization (ERS) influences emerging adults' participation in antiracism actions. The present study examined patterns of parental ERS messages received by a sample of 668 racially and ethnically minoritized emerging adult college students (Mage = 18.76, SD = 1.23; female = 81.8%), and their associations with the emerging adults' demographic characteristics and three forms of antiracism actions. A latent profile analysis revealed a five-profile solution and showed variability in patterns of parental cultural socialization and preparation for bias messages. Participants in profiles reflecting far higher than average frequencies of both messages (high frequency) and those who received mean preparation for bias and above-average cultural socialization (culturally focused) tended to engage most frequently across all forms of antiracism. Our findings suggest the importance of parental ERS messages for fostering engagement in antiracism actions among diverse college students. Results are informative for those who work with minoritized emerging adults navigating racist contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.

Message patterns through discourse analysis on the concept of apology and forgiveness during Ramadan among college students practicing Islam

Cultural values, beliefs, and traditions can significantly influence communication patterns related to seeking forgiveness. Within the context of Islam, apologizing during Ramadan is widely regarded as an integral part of religious practices. The significance of apologizing during Ramadan is observed among various Muslim communities, such as in Indonesia and the Philippines. The study aimed to explore how cultural values and religious teachings shape the perception and practice of apology among individuals during the holy month of Ramadan in the Philippines. The research employed a purposive sampling technique, selecting 16 college students who provided valuable insights into the message patterns surrounding apology and forgiveness within their cultural and religious context. The findings highlighted the enduring relevance of cultural and religious beliefs in shaping apology and forgiveness practices, even in the face of modern systems. Despite societal changes, the concept of apology based on cultural and religious values remained significant and remarkable among young Muslim individuals. Cultural and religious beliefs emphasized the importance of maintaining positive relationships with family, friends, relatives, and neighbors through seeking forgiveness and expressing apologies. The study contributed to the existing literature on forgiveness and cultural studies, providing insights into the interplay between cultural values, religious teachings, and apology practices during Ramadan.

Electronic Health Record Messaging Patterns of Health Care Professionals in Inpatient Medicine

This cross-sectional study examines secure messaging patterns among health care professionals in the internal medicine inpatient setting.

Parent‐child contact problems: Family violence and parental alienating behaviors either/or, neither/nor, both/and, one in the same?

AbstractThis article argues that in order to intervene effectively and ethically with children who are manifesting Parent–child contact problems (PCCPs) after parental separation, we begin by being mindful of what is normal about divorce transitions and use developmentally appropriate and culturally sensitive analysis to rule out children's common transitory reactions. It is then important to concurrently assess for both family violence (FV) and severe parental alienating behavior (PAB) on the part of both parents, which can co‐occur in some cases. The article asserts that it is also important to consider common problematic parenting responses that may potentiate the PCCP but not necessarily rise to the level of abuse. FV is defined as a child's direct experience of physical, sexual, or psychological maltreatment and indirect exposure to sibling abuse and/or to intimate partner violence (IPV). PAB is defined as an ongoing pattern of unwarranted negative messages on the part of one parent that conveys that the child's other parent is disinterested, irrelevant, dangerous, and not to be trusted. Any one or all of these factors may contribute to a child's strident negativity and sustained rejection of one parent, these being defining features of a PCCP. This article proposes ethical principles and priorities for decision‐making in these cases, considering the growing social science controversy about assessment and intervention for PCCPs. It concludes with an analysis of recent, contrasting policy approaches to PCCPs (e.g., Kayden's Law and the Joint Statement of the AFCC and NCJFCJ) and their potential impact on family justice system professionals and the families they serve.

Effective Spam Detection with Machine Learning

Abstract This paper aims to provide results of empirical experiments on the accuracy of different machine learning algorithms for detecting spam messages, using a public dataset of spam messages. The originality of our study lies in the integration of topic modeling, specifically employing Latent Dirichlet Allocation (LDA) alongside machine learning algorithms for spam detection. By extracting hidden topics and uncovering patterns in spam and non-spam messages, we provide unique insights into the distinguishing characteristics of spam messages. Moreover, the integration of machine learning is a powerful tool in bolstering risk control measures ensuring the sustainability of digital platforms and communication channels. The research tests the accuracy of spam detection classifiers on an open-source dataset of spam messages. The key findings of this study reveal that the Logistic Regression classifier achieved the highest F score of 0.986, followed by the Support Vector Machine classifier with a score of 0.98 and the Naive Bayes classifier with a score of 0.955. The study concludes that Logistic Regression outperforms Naive Bayes and Support Vector Machine in text classification, particularly in spam detection, emphasizing the role of machine learning techniques in optimizing risk management strategies for sustained digital ecosystems. This capability stems from Logistic Regression’s adeptness in modeling complex relationships, enabling it to achieve high accuracy on training and test datasets.

Lying aversion and vague communication: An experimental study

An agent may strategically employ a vague message to mislead an audience’s belief about the state of the world, but this may cause the agent to feel guilt or negatively impact how the audience perceives the agent. Using a novel experimental design that allows participants to be vague while at the same time isolating the internal cost of lying from the social identity cost of appearing dishonest, we explore the extent to which these two types of lying costs affect communication. We find that participants exploit vagueness to be consistent with the truth, while at the same time leveraging the imprecision to their own benefit. More participants use vague messages in treatments where concern with social identity is relevant. In addition, we find that social identity concerns substantially affect the length and patterns of vague messages used across the treatments.

Reflexive Control in the Israeli Digital Diplomacy of Normalization; Edy Cohen’s Twitter Page, a case study

To alter reality and achieve legitimacy while preserving itself as an occupying power, Israel uses Digital Diplomacy (DD) to foster Normalization. This study examines unusual patterns of message tailoring using Propaganda as a Reflexive Control to affect Arabs' perception and manipulate their decision-making algorithms towards Normalization. A quantitative content analysis of Cohen’s Twitter page is conducted to examine how political messages are created, supported, and contested using Reflexive Control (RC) processes and the use of its 4E Funnels to gauge the scope and intensity of Cohen’s page disinformation campaign. Between August 15 and September 15, 2020, a month before the signing of the Abraham Agreement, 2598 Reflexive Control techniques tailored into 883 tweets and retweets, were scraped and analyzed. The results show that Cohen's page uses all RC techniques and the 4E Funnels intensively and simultaneously: “Affecting the Adversary's Decision-Making (70.2%); a tactic flexibility of information weaponization; “”Affecting Perception by Providing False Information” (14.5%); a tactic flexibility; “Affecting the Timing of Decision-Making (10.4%); a tactic flexibility; and power pressure (5.0%), a tactic asymmetry of information weaponization. Using context analysis of the information environment and content analysis, the study found that the scope and intensity of Cohen’s page disinformation campaign is high. The page scored high levels of dissemination and amplification depending on fake followers (31.6%). These findings have implications for the RC’s plausible deniability, a tactical information asymmetry strategy.

Analyzing Leadership Messaging Styles and Team Performance in the NFL: Insights from Post-First Loss Press Conferences

This study investigates the impact of leadership messaging styles used by NFL head coaches on team performance, specifically focusing on messaging following a team's initial loss of the season. The research builds upon previous findings that indicate certain leadership styles, such as Deliberativeness messaging, influence performance throughout the season. However, this study examines the messaging patterns specifically after the first loss. Qualitative data, consisting of press conference transcripts from NFL head coaches after their teams' initial loss between 2020 and 2022, were collected and analyzed using DICTION, a computer-aided text analysis program. This software generated numerical scores indicating the prevalence of different messaging styles: Activity, Optimism, Certainty, Realism, and Commonality. These scores facilitated quantitative tests and correlation analyses. The results reveal that most messaging styles exhibit weak associations with team performance. Nonetheless, the analysis identifies distinct tendencies among coaches, with a propensity for high levels of Optimism, low levels of Certainty and Realism, and moderately high levels of Commonality. These findings suggest that coaches may adopt these messaging patterns due to their effectiveness. Understanding these tendencies can assist amateur coaches in enhancing team performance following the first loss of the season. By leveraging the messaging tendencies identified in this study, coaches can potentially improve team outcomes.

A Time-Free Assumption for Solving Signature-Free Asynchronous Multivalued Byzantine Consensus

This paper tackles the asynchronous signature-free Byzantine consensus. One way to circumvent the FLP impossibility result consists in adding a Time-free assumption. This assumption is based on the pattern of messages that are exchanged. In the context of authenticated asynchronous Byzantine systems where at most t processes may exhibit a Byzantine behavior, Moumen and Mostéfaoui provide the main result. They assume at least one correct process p_i, called diamond langle t+1rangle text {-winning}, and a set Q of t correct processes such that, eventually, for each query issued by p_i, any process p_j of Q receives a response from p_i among the (n-t) first responses to that query. The main contribution of this paper is to show that a deterministic solution for the Signature-free Byzantine consensus problem is possible if the system model satisfies an additional assumption that relies on the pattern of exchanged messages. To solve the Consensus problem, we assume a correct process p_i, called diamond langle 2t+1rangle text {-winning}, and a set Q of (2t+1) correct processes (including p_i itself) such that, eventually, for each query issued by p_i, any process p_j of Q receives a response from p_i among the (n - t) first responses to that query. The processes of the set Q may change over time. Based on this assumption, a Signature-free Multivalued Byzantine consensus protocol is proposed. Whereas many time-free protocols have been designed for the consensus problem in the crash model and in the Byzantine Authenticated model, this is, to our knowledge, the first time-free deterministic solution to the Signature-free Byzantine consensus Problem.

An Examination of Organizational Messages during the COVID-19 Pandemic Using Risk Communication and Social Support Frameworks

Background: The COVID-19 virus continues to plague the world, though not at its alarming rate at the peak of the pandemic in 2020-2021. Throughout that time, a great deal of organizational messages were disseminated to audiences in response to the virus. Aims: This study examined organizational messages in the United States about COVID-19 between March and September 2020. The purpose of the project was to identify patterns in these organizational messages to identify where messaging can be improved to better support the public when contending with health emergencies. Method: A total of 106 organizational messages were assessed (N = 106) through risk communication and social support lenses. Results: It was found that the organizational messages provided informational, instrumental, and emotional support. Organizational messages tended to frame risks as low hazard and low outrage. Strategies involving both rational and emotional appeals were used. The most frequently promoted preventive behavior was social distancing. It was found that different organizations promoted preventive behaviors differently, specifically government using their messages to promote wearing face coverings more than other industries. Conclusions: Not only does this project fill a research gap, it also serves a practical function, as the findings can be presented to organizations as helpful information for the development of a comprehensive communication strategy during a public health situation.

Building extreme right discourses on Twitter for non-campaign periods: insights from populist leaders across Europe

The growing success of extreme right-wing populist parties changed the political state of play in Europe, which has been widely studied by the literature. Populist parties apply a particular communication style, based on simple and non-mediated messages on social media that promote distrust in public institutions. However, the ways in which these strategies are applied for non-campaigns weeks remain little addressed by academia, as well as its development in small countries. This study examines how far-right leaders from European countries with different sizes and historical extremist backgrounds used Twitter during a non-campaign period (29 September 2020 to 28 February 2021). Specifically, we analyze the issues (issue frame), strategies (game frame), and propaganda mechanisms employed through a mixture of quantitative and qualitative approaches. The sample includes the messages posted on Twitter (n=1346) by the leaders of the main far-right parties in France (Rassemblement National), Austria (FPÖ), Germany (AfD), Spain (Vox), and Portugal (Chega!). A manual content analysis was applied, allowing comparison of countries with different traditions regarding the Extreme Right. The results show a common pattern of anti-migration messages as the main topic for their communication style, but some differences are found in terms of agenda and propaganda. The most recent parties (AfD, Vox, and Chega!) rarely use Twitter tools, which means leaving behind the interactive capacity of social media; meanwhile, the agenda is less thematic in the Austrian and Portuguese cases. Our research contributes to discussions about the communication practices of far-right populist parties in Europe, identifying trends by country as the binary opposition between the people and the elite is not expressed through the same type of propaganda.

UML Profile for Messaging Patterns in Service-Oriented Architecture, Microservices, and Internet of Things

The exchange of information among information technology (IT) systems is inevitable. Service fulfillment often involves sending and receiving messages. The article presents a set of messaging patterns for service-oriented architecture, microservices, and messaging protocols for the Internet of Things. The paper describes selected patterns that are the result of current research work. In addition, patterns introduced in open-source frameworks such as ZeroMQ have also been included. Moreover, the set includes Enterprise Integration Patterns. All considered messaging patterns have been described using the stereotype extensibility mechanism of the Unified Modeling Language (UML), and their complete set has been included in the new UML Profile for Messaging Patterns. The paper also shows the manner of integration flow modeling. In the illustrative examples, both the integration flow modeling diagram and the profile have been used to describe the communication in the context of the Integrated services view of the 1+5 architectural views model. The profile has been designed in the visual paradigm tool and revealed in a public repository for the community.

Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm

In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.

Political patterns in literary messages in the eras of modern sects and the Almoravids, the Diwani model

The study seeks to shed light on the political patterns in the literary messages in the era of the sects and the Almoravids, and that the political messages are concerned with matters of concern to the state. Therefore, the caliphs worked on investing writers and writers, so the writers were the mouthpiece of the state. The study concluded that these messages carried several formats, including the order and prohibition system, which is through which orders are issued and directed to the soldiers and the people in compliance with the system. On the Sultan and the authority, and there the format of media correspondence is what was circulated between the kings of the sects and the Almoravids of correspondence in the exchange of information and news between them in all circumstances of accidents; These media discourses are one of the ways that develop trust between individuals, enhance communication and develop relations. These formats expressed the political aspects in some ways, with the most accurate expression.

Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study.

Identifying digital markers of sleep disturbance-a known suicide risk factor-may aid in the detection of imminent suicide risk. This study examined sleep-related communication and texting patterns in personal text messages (N=86,705) of suicide attempt survivors. Twenty-six participants provided dates of past suicide attempts and 2-week periods of positive mood, depressed mood, or suicidal ideation. Linguistic Inquiry Word Count was used to identify sleep-related texts via a custom dictionary. Mixed effect models were fitted to test the association between suicide/mood episode type (e.g., attempt versus ideation) and three outcomes: likelihood of a text including sleep-related content, nightly count of texts sent from midnight to 5:00 AM, and sum of unique hour bins from midnight to 5:00 AM with outgoing texts. Analyses with a sleep dictionary that was manually revised to be more accurate (but not the original unedited dictionary) showed sleep-related communication was more likely during depressed mood episodes than positive mood episodes. Otherwise, there were no significant differences in sleep-related communication or objective texting patterns across episode type. Although we did not detect differences in sleep-related communication tied to suicidal thoughts or behaviors, sleep-related communication may differ as a function of within-person mood level.