Text Chat Research Articles

Artificial intelligence and geriatric medicine: New possibilities and consequences.

Artificial intelligence (AI) research attempts to build systems that approximate human intelligence through computer software.1 They can accept novel input and output meaningful information. Applications of AI to medicine include simulation of molecular dynamics, drug discovery and treatment selection for patients.1 Recently a text chat program called ChatGPT has been made available to the public.2 It has knowledge of the world and events till 2021 and falls under the domain of generative AI. These are programs that can generate novel paragraphs and visuals to text prompts. Users can ask questions via a chat format and obtain answers to their queries. In this article, the usage of ChatGPT in the field of Geriatric Medicine is explored. Antipsychotic medications, such as haloperidol and olanzapine, are often used to manage symptoms of delirium, such as agitation or psychosis. However, the use of antipsychotics in delirium is not without risks and should be carefully considered. One review of the literature on the use of antipsychotics in delirium found that while these medications may be effective in reducing symptoms of delirium, they may also have potential adverse effects such as sedation, extrapyramidal symptoms (e.g., tremor, rigidity, and akathisia), and an increased risk of falls and fractures (Wilder-Smith et al., 2010). The authors of the review concluded that the use of antipsychotics in delirium should be limited to cases where the benefits outweigh the potential risks, and that nonpharmacological interventions should be used as the first line of treatment whenever possible. Another review of the literature on the use of antipsychotics in delirium found that haloperidol was more effective at reducing symptoms of delirium than placebo, but that there was limited evidence on the effectiveness of other antipsychotics (Fong et al., 2013). The authors of the review also noted that the use of antipsychotics was associated with an increased risk of adverse effects, and that the decision to use these medications should be made on a case-by-case basis, taking into account the patient's specific needs and circumstances (Inouye et al., 2014). It is important to note that the use of antipsychotics in delirium should be individualized and carefully monitored, and that other nonpharmacological interventions should be considered as the first line of treatment whenever possible.

Conversational dynamics of joint attention and shared emotion predict outcomes in interpersonal influence situations: an interaction ritual perspective

Abstract This article addresses conversational dynamics in interpersonal influence situations. Drawing on interaction ritual theories and the research of interaction processes and patterns, we argue that sequential transition patterns of task and social–emotional acts can capture essences of conversational interaction ritual.A successful ritual then generates emergent solidarity and induces desired outcomes of interpersonal influence. In two experimental situations of online text chats between strangers (advice-giving, Nconversation = 336 with a student sample and compliance-gaining, Nconversation = 737 with a MTurk sample), we found certain transition patterns consistently predicted influence outcomes and interpersonal perceptions. Aligning with our theoretical expectations, these transitions indicate the moment-to-moment emergence of joint attention and shared emotion, which are essential ingredients of a successful interaction ritual.

“The conversation has more levels”: exploring Zoom’s text chat as a discussion mediator in middle school teachers’ online professional development

This study examined the role of text-based discussions in mediating middle school teachers’ discussions during a semester-long, online, synchronous professional development sequence. The PD sequence involved teachers engaged in weekly Zoom meetings, during which they learned about digital literacy and designed curricula. Qualitative data analysis of the transcripts of Zoom chats and spoken comments revealed three central findings: text-based chatting a) provided an additional, public mode for participants to mediate thinking and collaboration, b) allowed for multilayered, democratic, and more equitable access to the conversation, and c) served as an informal space where participants built community through playful discourse. The findings highlight the potential of multimodal, multilayered discussion spaces for facilitating collaborative thinking and more equitable discussion access for learners.

Telemedicine in Swedish primary health care - a web-based survey exploring patient satisfaction

BackgroundDirect-to-consumer telemedicine (TM), with patients having access to a physician via video or text chat, has gradually been introduced into Swedish primary care during the last two decades. Earlier studies have concluded that patients were generally satisfied with TM and the satisfaction rate was consistently above 80% and comparable with in-person visits. Despite the number of studies looking at user experience, studies assessing what factors influence patient satisfaction are lacking. To further develop digital care, it is important to explore the patients’ opinions of this relatively new phenomenon. The primary aim of this study was to explore patient opinions regarding satisfaction with TM-provided care, based on different age groups, sex, symptoms, and which type of health care professional they were assessed by.MethodsThe study was a quantitative web survey performed in 2020–2021 in which 688 patients using a TM provider in Southern Sweden responded. The results were analysed using Chi-2 test with the main outputs; satisfaction level and percentage that would use TM for similar symptoms in the future.ResultsThe results from the survey population show that patients that were assessed by a doctor were more likely to intend to use TM in the future and were more satisfied with the visit than patients assessed by a nurse. Our results also show that patients older than 70 years of age were less likely to use TM again compared to the total population.ConclusionThis study shows that patient satisfaction with TM varied depending on the patient’s age. The older patients were less satisfied than their younger equivalents. For patient satisfaction to be high, it was also crucial which health care provider the patient had been assessed by. The patients were more satisfied when assessed by a doctor than by a nurse. In addition, the study shows that patient satisfaction with TM was generally comparable to traditional care.

Role of Artificial Intelligence (AI) in Changing Consumer Buying Behaviour

This case study examines how positively Artificial Intelligence (AI) has affected marketers' capacity to analyse and comprehend customer behaviour. Marketers are studying how people act online in order to improve the effectiveness of their marketing plans and strategies. Given the enormous amount of data presently available and the frequency of data breaches, AI might be the solution. By studying millions of instances, an image recognition technology may be able to detect and classify objects in photographs. A chatbot can learn to have realistic conversations with people by being shown instances of text chats. To keep ahead of the competition, businesses must now leverage the greatest AI talent. It can shed light on each stage of the customer journey and help marketers better understand the driving forces behind consumer behaviour. When done correctly, it can result in positive outcomes including a higher client lifetime value and stronger client connections. Marketing teams may use AI to decipher massive amounts of data in order to utilise the information and identify the target market. They can utilise it to create user-centred sales funnels and base their marketing strategies on them. More traffic is ultimately generated, which is positive for marketing teams looking to convert visitors. Technology like artificial intelligence (AI) is changing the way we view and comprehend marketing.

Perceived Group Identity Alters Task-Unrelated Thought and Attentional Divergence During Conversations.

Task-unrelated thought (TUT) occurs frequently in our daily lives and across a range of tasks, but we know little about how this phenomenon arises during and influences the way we communicate. Conversations also provide a novel opportunity to assess the alignment (or divergence) in TUT during dyadic interactions. We conducted a study to determine: (a) the frequency of TUT during conversation as well as how partners align/diverge in their rates of TUT, (b) the subjective and behavioral correlates of TUT and TUT divergence during conversation, and (c) if perceived social group identity impacts TUT and TUT divergence during conversation. We used a minimal groups induction procedure to assign participants (N = 126) to either an ingroup, outgroup, or control condition. We then asked them to converse with one another via a computer-mediated text chat application for 10 min while self-reporting TUTs. On average, participants reported TUT about once every 2 min; however, this rate was lower for participants in the ingroup condition, compared to the control condition. Conversational pairs in the ingroup condition were also aligned more in their rates of TUT compared to the outgroup condition. Finally, we discuss subjective and behavioral correlates of TUT and TUT divergence in conversations, such as valence, turn-taking ratios, and topic shifts.

Provision of Psychological Support to a 31-Year-Old Man with SARS-CoV2-Induced Pneumonia during and after Hospitalization: A Clinical Case Report

A 31-year-old man hospitalized during the first wave of the pandemic in 2020 suffering from severe psychological distress, requested psychological assistance as his condition progressively worsened, eventually requiring intubation. After being referred to the clinical psychology service by a ward physician, the patient was assisted remotely for two months for a total of 22 sessions during hospitalization and after discharge. A psychometric evaluation was carried out when the patient was close to discharge and longitudinally, for a total of four times, for depression (Patient Health Questionnaire-9 (PHQ-9)), anxiety (Generalized Anxiety Disorder Scale-7 (GAD-7)), post-traumatic stress disorder (Impact of Event Scale-Revised (IES-R)) and insomnia (Insomnia Severity Index (ISI)). Support was provided remotely, mainly through audio and video calls, and text chats were also utilized when possible and as required. The initial psychometric evaluation indicated moderate depression, severe anxiety, the presence of post-traumatic stress disorder and sleep problems. Psychological distress decreased until reaching a situation of no distress in the final evaluation. Psychological interventions from which the patient benefitted were stress reduction and breathing techniques, empathic support, elaboration of the possibility of grief and cognitive restructuring regarding fears relative to his condition. Psychological support provided remotely and the monitoring of psychological status after discharge are highly advisable in pandemic emergency situations. The CARE checklist of information to include when writing a case report was utilized in the writeup of this case report.

Modifikasi Pembentukan Algoritma Rc4 Cipher Dengan Metode Acak Mid-Square Technique Untuk Pengamanan Voice Chat

Technological developments and human lifestyles allow access to information systems in new ways, for example using online conversations (chat) via an internet connection, which is often in the form of text communication (text chat) or voice (voice chat). Instant messaging only became popular when the internet began to be widely used in the mid-1990s. Along with the human need for easy and fast communication, the application that can be used is Voice Chat which allows users to carry out voice communication. But the voice chat application is less secure because it is easily intercepted by other parties. Eavesdroppers can easily find out the contents of conversations in instant messaging and the lack of privacy for chat users. The RC4 algorithm has a weakness in its use to encrypt a message. The downside is that Vigenere can only perform encryption and decryption processes for short messages. One way that can be used is to make security for the voice chat application using the Mid-Square Technique algorithm

Is bigger better? A study of the effect of group size on collective intelligence in online groups

What is the optimal size for online groups that use electronic communication and collaboration tools? Previous research typically suggested optimal group sizes of about 5 to 7 members, but this research predominantly examined in-person groups. Here we investigate online groups whose members communicate with each other using two electronic collaboration tools: text chat and shared editing. Unlike previous research that studied groups performing a single task, here we measure group performance using a test of collective intelligence (CI) that includes a combination of tasks specifically chosen to predict performance on a wide range of other tasks [72]. Our findings suggest that there is a curvilinear relationship between group size and performance and that the optimal group size in online groups is between 25 and 35. This, in turn, suggests that online groups may now allow more people to be productively involved in group decision-making than was possible with in-person groups in the past.

Smartphone use and social media addiction in undergraduate students

Background: Children’s use of social media has increased significantly over the past decade. As a result, they are susceptible to smartphone addiction. In particular, parents' and children's well-being and behaviors are negatively affected by smartphone addiction. Such addiction likely affects both physical performance and lifestyle. Adolescents utilize their smartphones while performing other tasks. The secondary task might divert attention away from the primary task. Reaction time is the combination of brain processing and muscular movement. Texting or communicating on a smartphone while performing another task may affect reaction time. Thus, the purpose of this study was to explore the influence of smartphone use on reaction time in undergraduate students who were addicted to smartphones. Methods: The Smartphone Addiction Scale-Short Version (SAS-SV) was used to assign 64 undergraduate students to the smartphone addiction group (n = 32) and the control group (n = 32). The reaction time (RT) of an organism is used to determine how rapidly it responds to stimuli. All participants were examined on the RT test under three conditions: no smartphone use (control), texting, and chatting on a smartphone. Participants were questioned by smartphone through text message or chat with the support of a researcher during the texting and conversation conditions. While responding to the questions, the participant was administered an RT test. Results: The results showed that smartphone addiction tends to have a reduced influence on reaction time when compared to the control group. Also, texting or conversing on a smartphone while doing other work had a substantial impact on reaction time in the undergraduates. Conclusions: Combining smartphone use with other activities tends to reduce undergraduate students' reaction time.

Requesting on WhatsApp: The interplay of interactional competence and deontics in English as an additional language

This exploratory study focuses on changes in the accomplishment of requests by an adult English as an additional language speaker/learner interacting on WhatsApp for nine months. The analysis follows a microanalytic approach to digital interaction informed by recent developments within longitudinal conversation analysis. It unpacks the array of semiotic and interactional resources that the focal learner employs to make class-related requests to the teacher. Longitudinal comparison of four request sequences over time suggests that the differences in how the requesting posts are designed and responded to index both increased interactional competence to accomplish requests in English on WhatsApp - through more contextually-sensitive and conventional semiotic resources - as well as evolving socio-interactional ties and related deontic concerns between the learner and the teacher. Despite the popularity of text chats, only a handful of studies have investigated the practices employed by additional language learners to engage in text chat interaction change over time and this work has not focused on naturally occurring interactions or on requesting. The present study thereby contributes new understandings to text chat interaction with additional language speakers and to longitudinal research on interactional competence development in online settings.

Digital forensic analysis of discord on google chrome

Over the past decade, social media applications have significantly increased their market share and garnered a wide user base. However, these applications have also attracted the attention of criminals desiring to exploit the apps to support illicit operations due to the low barrier to entry and ease of usage. A digital forensic investigation of these applications can reveal valuable information about criminal activity and the suspect. Discord is a Voice over Internet Protocol (VoIP) service that enables text, image, video, and audio chats. It has grown in popularity, and as a result, it is subject to increased use by cybercriminals. In this paper, we examine the remnants of the increasingly popular social media application ‘‘Discord” when used on the Google Chrome web browser. We recovered various artifacts such as payment information, sent messages, account settings, conversations, uploaded attachments, and much more, all of which could be utilized in a forensic investigation.

Determinants of satisfaction and reuse intention of chatbots : An empirical study by using the TAM, AIDUA and IS success models

Chatbots are new technology platforms based on artificial intelligence (AI) to support and serve customers by simulating human communication via voice commands, text chats, or both. The new AI technology is adopted to support tourists at tourism destinations. Although the usage of chatbots is becoming a trend in the travel industry, few studies clarified why travelers are satisfied with chatbots and intend to reuse this AI platform for their next trips. This study develops a modified framework by integrating the artificially intelligent device use acceptance model (AIDUA), the information system success model (IS success), and the technology acceptance model (TAM) to explain why tourists are willing to use chatbots for vacation. The study

Chatting with Your Peers across Modalities: Effects of Performing Increasingly Complex Written Computer-Mediated Tasks on Oral L2 Development

This study investigates whether peer interaction in a second language (L2) using written computer-mediated communication (CMC or text chat) may function as a bridge into oral performance. By designing and sequencing tasks according to the SSARC model of task complexity (we also examine its effects on L2 development. Finally, we explore the role of learners’ affective variables for L2 performance and development. Fifteen low–intermediate adolescent refugee learners of L2 English in the Netherlands participated in the study. Using a within-subject pre-test post-test design, we examined their language performance in both text-based CMC and face-to-face (F2F) tasks before and after a task-based classroom intervention. Results show that the intervention had a significant and strong effect on most of the linguistic measures of complexity, accuracy, and fluency (CAF). Similar gains in text chat and oral interaction provide evidence that a direct transfer of language experiences across modalities can occur. Together with the fact that most participants valued the use of written CMC in the classroom, our findings indicate that increasingly complex text chat tasks can be an effective way to promote the oral skills of language learners. We discuss our findings in light of how the design of written CMC tasks can afford L2 development across modalities.

An exploratory study of English-language learners’ text chat interaction in synchronous computer-mediated communication: functions and change over time

The use of text chat in synchronous computer-mediated communication (SCMC) could help remedy the widely reported lack of active involvement in online language learning. However, the nuances and complexities of learners’ text-chat actions warrant further examination. This exploratory study used observational data collected from 40 students in a graduate-level Academic English course at eight time points across a semester to analyze the functions of text-chat interaction in an SCMC setting and to capture how their text chat changed over time. This led to the identification of five functions of text-chat interaction (i.e. cognition, metacognition, socio-affect, organization, and technology), suggesting that cognitively meaningful learning occurred immediately through such interaction. Over time, there was a decline in the quantity of text-chat interactions. In addition, the patterns of chat between students and the teacher were somewhat different from those observed among students. In the former case, text quantity diminished, especially in the category of cognition; but in the latter, students engaged in more text chat with their peers to show affect, cognitive agreement, and disagreement over time. The study’s findings regarding the complex dynamics of an authentic process in SCMC language classrooms have considerable practical value in facilitating interaction in similar language-learning contexts.

Electronic Brainstorming With a Chatbot Partner: A Good Idea Due to Increased Productivity and Idea Diversity.

Brainstorming is a creative technique that fosters collaboration to enhance idea generation. The occurrence of evaluation apprehension, a fear of being evaluated negatively by others, however, can stymy brainstorming. How the advantages of collaboration can be leveraged while evaluation apprehension is prevented is an open scientific and practical problem. In this brief research report, it is proposed that chatbots could provide a solution. Chatbots can be designed to share ideas with their users, facilitating inspiration. Compared to human beings, chatbots are also perceived as possessing limited agency and evaluative capacity. This could reduce evaluation apprehension. Given that chatbots are often embedded in a textual chat interface, social cues (picture, name, and description) can reinforce the perceived chatbot identity, enhancing its alleged effects on evaluation apprehension and subsequently on brainstorming performance. These conjectures were tested in an online 2 × 2 between-subjects experiment (n = 120) where people were instructed to brainstorm with a partner that was framed as either a chatbot or human being (but followed the same automated script), with or without the presence of social cues. The results showed that brainstorming with a chatbot led participants to produce more ideas, with more diversity than brainstorming with an alleged human being. Social cues enhanced the effect on idea diversity, but only with the chatbot. No significant effects on evaluation apprehension were found. The contribution of this study is therefore that chatbots can be used for effective human–machine teaming during brainstorming, but this enhancement is not explained by its effects on evaluation apprehension.

Communication in online games on the example of own research

Aim. The aim of the research was to identify the ways in which users of online games communicate. The author of the article attempted to answer the question of whether these interactions differ depending on the age and gender of the players.
 Methods. The research was carried out in quantitative orientation on a group of 154 subjects: players who use the Facebook social platform. The research technique was an online survey.
 Results and conclusion. The study showed that Internet users most often use text chat. Communication among women is also popular with the help of the pre-set character animations and sound signals in the game. Men communicated in some ways similar to women: via text chat, character animations and in-game beeps, although they also indicated frequent communication via voice chat. It also turned out to be a myth that women do not like games. The study found that younger gamers spend more time gaming than the older gamers and prefer communication channels such as text chat, character animations and in-game signals.
 Cognitive value. I consider the topic discussed in the article current, important and necessary due to the fact that the virtual and real worlds overlap more and more. People make friends not only in direct contact but also via the Internet, e.g., in online gaming communities. The time of the pandemic and the associated home isolation confirmed the above conclusion

University Instructors’ Views on Courses Delivered Online

The health emergency is compelling us to adopt new habits, including education-related practices. Therefore, it may be worth considering targeted teaching strategies and their future development. Current research in e-learning and technology-supported teaching has recently developed in an attempt to identify possible future transformations at all levels of education.This paper focuses on tertiary-level education and describes a study on educators’ views on courses delivered via Moodle during the pandemic and on the most commonly used pedagogical models.Using a case study on methodology, the author analyses some possible forms of online education, from the most basic, to those implemented through a combination of digital activities and resources, and their application at the University of Urbino (Italy). Data were collected by means of a questionnaire administered to 231 university teachers during the first year of the pandemic.The findings suggest that there is a good basic tendency to use technology but also inevitable stress that is however combined with an underlying sense of satisfaction. The educators’ technological skills, their tools, and the advice offered by the institution appear satisfactory, while students seem to have found some basic difficulties in accessing online lessons. The most commonly used didactic practices are sharing files via Moodle, using web conferencing software and text chats during lessons. Finally, future developments are to be expected as lecturers express their willingness to continue using the same technological resources that they have found most effective during the COVID-19 emergency.

Re-designing a regulatory scale for dynamic assessment in the synchronous text chat environment in collaboration with teachers

The discussion on the dynamic assessment (DA) - a combination of assessment and instruction - and regulatory scales from implicit to explicit corrective feedback (CF) is relatively new in the CALL context. Applying the notions of Sociocultural Theory, Zone of Proximal Development (ZPD) and Mediation, the present study examines how a DA-based regulatory scale from implicit to explicit CF could be designed to promote language learning in the text chat environment. This was done in collaboration with teachers. Four English-as-a-second-language (ESL) teachers and eight ESL students participated in the study. Using the methodology of design-based research (DBR), the study was conducted mainly in three stages: exploratory stage, first intervention, and second intervention. This study drew on various data. Naturally occurring interaction data such as teachers’ oral conversation transcripts and text chat transcripts were analysed using conversation analysis, while the teachers’ interviews were analysed using thematic analysis. Thus, employing DBR, the present study introduces a three-step regulatory scale that could promote learning in the text chat environment. The findings suggest that the three-step regulatory scale could be used by teachers to identify the learner’s potential and assist them in partial or complete self-regulation. The study will introduce a DA-based regulatory scale to promote language learning in the text chat environment and contribute to the knowledge of DA, ZPD and mediation in the CALL context.

Towards automatic annotation of collaborative problem‐solving skills in technology‐enhanced environments

AbstractBackgroundCollaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology‐enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory‐driven measurements that are usually carried out as manual annotations over recorded logs of collaborative activities. However, manual annotation has limited scalability and is not conductive towards CPS assessments at scale.ObjectiveWe explore possibilities for automated annotation of actions in collaborative‐teams, especially chat messages. We evaluate two approaches that employ machine learning for automated classification of CPS events.MethodData were collected from engineering, physics and electronics students' participation in a simulation‐based task on electronics concepts, in which participants communicated via text‐chat messages. All task activities were logged and time stamped. Data have been manually classified for the CPS skills, using an ontology that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments.ResultsWe applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k‐nearest‐neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.ImplicationsOur results suggest that a kNN classifier can be very effective for accurate annotation of CPS events. It achieves reasonably strong results even when trained on only half of the available data. This shows a promise towards reduction of manual data annotation for CPS measurement.