Chat History Research Articles

Pandemic in the digital age: analyzing WhatsApp communication behavior before, during, and after the COVID-19 lockdown

The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people’s daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people’s behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.

Authentication of LINE Chat History Files by Information Hiding

With the prevalence of smartphones, message exchanges via mobile chatting programs like LINE have become popular. The messages in the form of chat records in a LINE chat history, after being downloaded for legal uses, might be tampered with illicitly. A novel method for authenticating the chat history against such attacks is proposed. The signal used for authenticating each chat-record segment is created by concatenating the ID label of the segment and a digest yielded by hashing the segment content. The signal is then encoded by three types of spacing code, namely half space, full space, and tab, and embedded into the blank-space areas created by the tab codes in the chat records of the segment. Authentication of a history file is accomplished by extracting the authentication signals embedded in the file and comparing them with the original signals computed directly from the file. The embedded signals are invisible, arousing no suspicion from the hacker. The signals are fragile, because any modification of the records can be detected by the authentication process. Experiments for testing four types of tampering with text files of four languages have been conducted, yielding correct authentication results that show the feasibility of the proposed method.

Evolution of Interpersona Communication Through Symbols (Sticker) Cyber Native In The Line App

The essence of man in life is to conduct communication activities. An individual is said to communicate with others if both have an understanding of the meaning of the message conveyed in his interaction. Advances in information communication technology provide opportunities for individuals to more easily interact over the internet. Of the recorded internet users, 130 million are active on social media. New Media Communication, or communication based on information technology, makes social media a prima donna for individuals to interact socially in cyberspace. Text-based communication platforms such as WhatsApp and LINE make it easier for individuals or those in cyberspace referred to as digital natives to interact. These digital natives in their interactions do the continuous thing as a habit that eventually becomes a culture of communication.
 In this research, the focus of research is the evolution of communication between users in the new media era in LINE users as a text application-based social media with a constructivist approach, qualitative research methodology with virtual ethnographic research methods and descriptive analysis levels. Data collection with online interviews, as well as chat history observations and the use of LINE sticker features. The purpose of the study is: (1) to find out the communication of digital natives on the LINE application platform, (2) to find out the evolution of interpersona communication formed from the results of native digital interactions on the LINE application platform using a virtual ethnographic approach.

Third places: cultivating mobile communities of practice in the global south

ABSTRACT Research on communities of practice suggests that such groups can be used to support academic staff development, especially during times of crisis. We explore how a group of South African women academics and ‘eLearning champions’ engaged in a mobile community of practice under COVID-19 conditions. Our analysis of WhatsApp chat histories and focus group conversations reflects how the group evolved from a mobile CoP into a multi-modal third place, with implications for community-building, teaching transformation, and organisational change.

EFL students’ perceptions of blended learning in English language course: learning experience and engagement

The importance of blended learning has been emphasised during the current restriction caused by the coronavirus diseases 2019 (COVID-19). But there is a scarcity of studies on students’ learning experience in blended learning, particularly using WhatsApp as one of the learning tools. A combination of classroom meetings and online learning is implemented in a vocational university in Indonesia. The research reported in this article aims to examine students’ perceptions of their learning experience in a blended method of learning incorporating social media WhatsApp and Google Classroom application as learning instruments. The dataset includes a questionnaire consisting of a four-point Likert scale and open-ended questions, a recording of WhatsApp chat history, and students' work in Google Classroom. Data analysis uses a multi-methods approach. Descriptive analysis is used for the closed-ended questions using the Excel program and content analysis for the open-ended questions. Results show that students positively perceive their learning experience in blended learning. Students also articulate their concerns of the internet connection and instruction clarity on the online learning materials. This paper highlights some implications of the research findings including the lecturer's need for professional development that may enable them to help students succeed in blended learning.

How Online Teams with Diverse Backgrounds Worked to Excel: Findings from an International eTournament

A tide of changes with technological advances at its center has allowed more efficient and productive synchronous and asynchronous collaborations among dispersed individuals across the globe in recent years. Working effectively in virtual teams of individuals with diverse backgrounds is thus critical for students to succeed in the 21st century. However, relevant training for international collaboration is lacking in the higher education system. The research team examined data from a project aimed to heighten students’ multidisciplinary and multicultural competencies via a team-based, international eTournament organized in 2019 and enhanced and repeated in early 2020 featuring the 17 Sustainable Development Goals (SDGs) of the United Nations. Students were teamed up according to a mechanism, to ensure diversity in each virtual team and mimic the real practice in many workplaces. A two-stage “strategize-play” approach was deployed with activities carried out entirely online. Team members first got to know each other, built up their teams and formulated their strategies for the next stage. In the second stage, the virtual teams competed with one other on a gamified learning platform called PaGamO by answering questions related to the SDGs. About 240 students (2019) and 420 students (2020) participated. Various sets of quantitative and qualitative data were collected, including student chat histories, focus group interviews, data analytics from PaGamO recording how the students progressed in the game, as well as the pre- and post-game surveys. This article focuses on the chat histories of students from the top-5 and bottom-5 teams of the 2019 and 2020 eTournaments. The results provide evidence that the high performing teams took a different gaming approach from the low performing teams in such areas as team building and game strategy deployment.

Politeness in Making Requests and Responses in Computer-mediated Communication among Chinese College Students

This study aims to explore the politeness strategies used in making requests and responses in computer-mediated communication (CMC) among Chinese college students and the applicability of discursive approach to such analysis. Following the steps of previous studies using discursive approach, some extracts of chat history on WeChat concerning requests and responses are selected and showed to the interlocutors. Then the interlocutors were asked to judge the conversations from the perspective of politeness. By analyzing the data, this study found that various strategies were used in order to achieve politeness in CMC, which can be associated with Brown and Levinson’s theory of politeness. This study also found that discursive approach can be applied in analyzing politeness in CMC well though it differs from face-to-face communication in some ways.

A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

In this study, we demonstrate the role of visualization to facilitate forensic analysis goal in interpreting metadata of evidence of interest to answer who, what, why, when, where, and how an incident occurred. Two mobile Instant Messaging (IM) applications (i.e. WhatsApp and Line) were deployed as a case study. Subsequently, a tool – W*W Visualizer – was designed and developed with the aims to analyze and visualize the connection of evidence metadata, text frequency and word count, and display report of analysis activities. The tool is developed by adopting Object-Oriented Software Development Model with Visual Studio platform and C# language were used to develop the system. Our findings show that W*W Visualizer could transform the data of the chat database into a visual form, for example graph, chart and word cloud. The tool also allows the user to perform search feature such as searching based on keyword and timestamp from the IM chat history. It is expected that outcomes from this study would significantly influence digital forensics practitioners in analyzing and interpreting evidence data, and judicial authorities in understanding the presentation of evidence.

Analysis on Verbal Communication of Male Homosexuals from the Sociolinguistic Perspective

Homosexuals, especially gays, have become a sub-cultural group which cannot be ignored in the modern society, therefore, it is necessary to promote the society to envisage the phenomenon of homosexuality. From the perspective of sociolinguistics, this study analyzes the chat histories of homosexual men on the social media in order to explore how they realize self-presentation and further discuss what dilemmas they are facing. This study aims to help homosexuals construct correct social identity and let public members in mainstream culture have a more particular knowledge of homosexuality.

Increased Accuracy Of Sequence To Sequence Models With The CNN Algorithm For Multi Response Ranking On Travel Service Conversations Based On Chat History

Building a chatbot cannot be separated from the knowledge base. The knowledge base can be obtained from data that has been labeled by the developer, documents that have been converted into pre-processing data, or information taken from social media. In this case, the data used as knowledge is chat history. In the chat history there are certainly many variations of answers and allowing a question to give rise to many answers. To overcome these multi responses, response was made. The existence of ranking, of course the response desired by the user will be more appropriate. Challenge in ranking is how to get the essence a question and find pairs questions and answers from the data. This can be solved by a sequence to sequence model. However, the problem that will arise is the consistency of the answers. The existence of a lot of chat history certainly raises many explanations, even though the question's essence is the same. For this reason the CNN algorithm as a solution to the problem. This research uses convolutional sequence to sequence which will be applied for ranking responses. We compare the efficiency of this model. By making comparisons, this model shows an accuracy of 86.7%

Multi-Speaker Video Dialog with Frame-Level Temporal Localization

To simulate human interaction in real life, dialog systems are introduced to generate a response to previous chat utterances. There have been several studies for two-speaker video dialogs in the form of question answering. However, more informative semantic cues might be exploited via a multi-rounds chatting or discussing about the video among multiple speakers. So multi-speakers video dialogs are more applicable in real life. Besides, speakers always chat about a sub-segment of the long video fragment for a period of time. Current video dialog systems require to be directly given the relevant video sub-segment which speakers are chatting about. However, it is always hard to accurately spot the corresponding video sub-segment in practical applications. In this paper, we introduce a novel task of Multi-Speaker Video Dialog with frame-level Temporal Localization (MSVD-TL) to make video dialog systems more applicable. Given a long video fragment and a set of chat history utterances, MSVD-TL targets to predict the following response and localize the relevant video sub-segment in frame level, simultaneously. We develop a new multi-task model with a response prediction module and a frame-level temporal localization module. Besides, we focus on the characteristic of the video dialog generation process and exploit the relation among the video fragment, the chat history, and the following response to refine their representations. We evaluate our approach for both the Multi-Speaker Video Dialog without frame-level temporal localization (MSVD w/o TL) task and the MSVD-TL task. The experimental results further demonstrate that MSVD-TL enhances the applicability of video dialog in real life.

Cooperative Learning: Adaptive Group Formation for Collaborative Learning

Collaborative learning affects with lot of factors like student’s personality, their interaction patterns, learning styles etc. Grouping of students is one of the important factors. It is important to arrange groups by skills and/or backgrounds. Hence it is noteworthy to create groups based on specific skills of students. Generally the students can be randomly grouped or grouped themselves. But this method of grouping students based on certain features like personality traits can improve the efficiency of collaborative learning. The student’s data can be collected from social networking site like Facebook. The personality of each student can be identified by comparing the individual’s chat history with psycholinguistic databases. The main objectives of this paper are to identify the student’s personality. Based on that, the group of students can be formed using k-means clustering algorithm.

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

A New Test for Irony Detection: The Influence of Schizotypal, Borderline, and Autistic Personality Traits.

Irony has repeatedly been suggested as a language based social cognition task. It has been argued to show specific variances in psychiatric disorders and healthy adults with certain personality traits. Above that, irony comprehension is based on a complex interplay of the informational context, the relationship of the conversational partners, and the personality of the recipient. The present study developed a video-based German language test for a systematic examination of irony detection accuracy (Tuerony). The test includes (i) a stereotypical conversation partner (doctor, actor) in (ii) different perspectives (direct interaction, neutral observer) and (iii) a bilateral chat history on a conventional messenger service interface with ironic criticism, ironic praise, literal criticism, and literal praise. Based on the continuous approach of psychiatric symptoms, schizotypal, borderline, and autistic personality traits were associated with irony detection accuracy in a healthy sample. Given the often reported role of mentalization in irony detection, these associations were also investigated. First, a broad variance of irony comprehension in our healthy sample could be shown. Second, schizotypal and borderline, but not autistic traits were significantly negatively associated with irony detection accuracy. Finally, in the current healthy sample, neither variation of the conversational context nor mentalization characteristics were significantly associated with performance beyond personality traits. The current results therefore highlight two aspects for future research in irony comprehension: the importance of ecological valid tests and the role of the individual personality of the recipient.

Information Propagation Analysis of Social Network Using the Universality of Random Matrix

Spectral graph theory gives an algebraical approach to analyze the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrix with the feature of social networks. As such random matrix, we use normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As the results, we show that the worst-case speed of the information propagation changes at most 2 if the structure (i.e., relationship among people) of a social network changes.

Perubahan Budaya Komunikasi pada Pengguna Whatsapp di Era Media Baru

Abstract
 A total of 63 million Indonesians are currently active as internet users and 95% of Indonesians use the internet to access social networks. This new media era makes social media a prima donna primarily to establish social relationships in cyberspace. With smartphones that support the WhatsApp app as one of the contemporary social media that is widely used by digital natives. As a creative generation in using technology, digital natives make use of WhatsApp as a communication medium today. WhatsApp users can continuously form a habit that eventually becomes a culture of communication. In this study, researchers interested to examine the digital communication culture natives formed by the use of social media WhatsApp.
 The purpose of this research is 1) To know the culture of digital communication natives on the use of WhatsApp. 2) To know the communication culture formed from the use of WhatsApp by digital natives.
 This type of research uses qualitative research with constructivist paradigm using virtual ethnography research approach. The data collection techniques are done by interviewing online and offline and make observations made through chat history and activities use WhatsApp features by informants.
 The results showed that there is a change in communication culture in the conventional era into the new medi era, ie from face to face communication to computer mediated communication. For digital natives, the established communication culture is a textile culture by building the value of emotional closeness and ease in conveying information, and embracing the norm of speed, entertainment, customization and freedom. Keywords: Communication Culture, New Media Era, WhatsApp, Digital Natives.

Understanding collaborative tourism information searching to support online travel planning

PurposeCollaborative information searching is common for people when planning their group trip. However, little research has explored how tourists collaborate during information search. Existing tourism Web portals or search engines rarely support tourists’ collaborative information search activities. Taking advantage of previous studies of collaborative tourism information search behavior, in the current paper the purpose of this paper is to propose the design of a collaborative search system collaborative tourism information search (ColTIS) to support online information search and travel planning.Design/methodology/approachColTIS was evaluated and compared with Google Talk-embedded Tripadvisor.com through a user study involving 18 pairs of participants. The data included pre- and post-search questionnaires, web search logs and chat history. For quantitative measurement, statistical analysis was performed using SPSS; for log data and the qualitative feedback from participants, the content analysis was employed.FindingsResults suggest that collaborative query formulation, division of search tasks, chatting and results sharing are important means to facilitate tourists’ collaborative search. ColTIS was found to outperform Tripadvisor significantly regarding the ease of use, collaborative support and system usefulness.Originality/valueThe innovation of the study lies in the development of an integrated real-time collaborative tourism information search system with unique features. These features include collaborative query reformulation, travel planner and automatic result and query sharing that assist multiple people search for holiday information together. For system designers and tourism practitioners, implications are provided.

A comparison of three think-aloud protocols used to evaluate a voice intelligent agent that expresses emotions

ABSTRACTThis study proposes two adapted think-aloud protocols for the evaluation of a voice intelligent agent. In the adapted retrospective think-aloud (RTA) protocol, users verbalise their thoughts based on the chat history after task-completion. In the adapted interactive think-aloud (ITA) protocol, users verbalise their thoughts regarding the intelligent agent being evaluated without the help of a facilitator. This study compares these two protocols with the classical think-aloud protocol (CTA) for evaluating an intelligent agent in terms of task time and verbal utterances. The influence of the intelligent agent’s emotional expression is also considered. The results suggest RTA is suitable for collecting user experience and causal explanation of utterances, CTA for collecting recommendation and prediction utterances, and ITA for collecting problem formulation and recommendation utterances. Furthermore, CTA and RTA can collect more total utterances, while CTA and ITA are influenced less the VIA’s emotional expression. This study provides guidelines by which future evaluators can choose suitable think-aloud protocols.

The Linguistic Features Uniqueness of the Students’ Written Discourse in Online Learning

This research aimed at finding and describing the types and the uniqueness of linguistic features used by the students in their written discourse in BritishEnglishClass.com. The researcher applied a descriptive qualitative method. The data collection was done using document analysis in form of chat history in Whatsapp and taking ten students as the participants of this study. The result of the analysis showed that the students used six types of linguistic features namely (1) Lexical features in terms of the use of interjection, abbreviation, word letter replacement, word combination, code switching, code mixing and diction, (2) Orthographic features in terms of word spelling and capitalization, (3) Grammatical features in terms of ellipsis, passive voice, verb use, and personal pronoun, (4) Discourse features in terms of the use of interactional features and the stream of consciousness, (5) Paralinguistic and Graphic in terms of emoticon usage and excessive punctuation and, (6) other features in terms of written out laughter . These features are unique because they are different from the standard form of the language. It can be seen also from the use of abbreviation and ellipsis that shorten the students’ sentence, the use of emoticon representing the emotions and psychical activity, the use of lower and upper case to represent the sound and the intonation of the student’ sentence, the abandonment of convention of capitalization of proper nouns and the first words of sentences, and the creative orthography of some words. The linguistic features used by the students were different from the standard form and their function that allowed the student to express and emphasize their intention, meaning and emotion in the chat room make the students’ written discourse in online learning is uniqueKeywords: uniqueness, linguistic features

Forensic analysis of Kik messenger on iOS devices

Instant messaging applications continue to grow in popularity as a means of communicating and sharing multimedia files. The information contained within these applications can prove invaluable to law enforcement in the investigation of crimes.Kik messenger is a recently introduced instant messaging application that has become very popular in a short period of time, especially among young users. The novelty of Kik means that there has been little forensic examination conducted on this application.This study addresses this issue by investigating Kik messenger on Apple iOS devices. The goal was to locate and document artefacts created or modified by Kik messenger on devices installed with the latest version of iOS, as well as in iTunes backup files. Once achieved, the secondary goal was to analyse the artefacts to decode and interpret their meaning and by doing so, be able to answer the typical questions faced by forensic investigators.A detailed description of artefacts created or modified by Kik messenger is provided. Results from experiments showed that deleted images are not only recoverable from the device, but can also be located and downloaded from Kik servers. A process to link data from multiple database tables producing accurate chat histories is explained. These outcomes can be used by law enforcement to investigate crimes and by software developers to create tools to recover evidence.