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(Computer-Mediated) Communication Submitted by Dipl-Ing. Robert Ecker Submitted at Analysis of Computer- Department of Telecooperation Mediated Discourses Supervisor and First Examiner Univ.-Prof. Mag. Dr. Focusing on Automated Gabriele Anderst-Kotsis Second Examiner Detection and Guessing o. Univ.-Prof. Dipl.-Ing. Dr. Michael Schrefl of Structural Sender- August 2017 Receiver Relations Doctoral Thesis to obtain the academic degree of Doktor der technischen Wissenschaften in the Doctoral Program Technische Wissenschaften JOHANNES KEPLER UNIVERSITY LINZ Altenbergerstraße 69 4040 Linz, Osterreich¨ www.jku.at DVR 0093696 Kurzfassung Formen der computervermittelten Kommunikation (CvK) sind allgegenwärtig und beein- flussen unser Leben täglich. Facebook, Myspace, Skype, Twitter, WhatsApp und YouTube produzieren große Mengen an Daten - ideal für Analysen. Automatisierte Tools für die Diskursanalyse verarbeiten diese enormen Mengen an computervermittelten Diskursen schnell. Diese Dissertation beschreibt die Entwicklung und Struktur einer Software- Architektur für ein automatisiertes Tool, das computervermittelte Diskurse analysiert, um die Frage “Wer kommuniziert mit wem?” zu jedem Zeitpunkt zu beantworten. Die Zuweisung von Empfängern zu jeder einzelnen Nachricht ist ein wichtiger Schritt. Direkte Adressierung hilft, wird aber nicht in jeder Nachricht verwendet. Populäre Kommunikationsmodelle und die am weitesten verbreiteten CvK-Systeme werden untersucht. Das zugrunde liegende Kommunikationsmodell verdeutlicht die wesentlichen Elemente von CvK und zeigt, wie diese Kommunikation abläuft. Mit diesem Verständnis werden mehrere Betrachtungsweisen definiert, die durch verschiedene At- tribute und unterschiedliche Leitfragen repräsentiert werden. Praktische Beispiele er- läutern, welche grundlegenden Informationen aus textbasierten Diskursen gewonnen werden können und wie dies stattfindet. Der Autor konzentriert sich hauptsächlich auf den Internet Relay Chat (IRC) als angewandtes Beispiel aufgrund seines frei zugänglichen und gut dokumentierten Protokolls. In Diskursen ist nicht immer klar, wer mit wem kommuniziert. Dies ist besonders bei automatischer Diskursanalyse problematisch. Es ist wichtig, die Nicknamen der Benutzer in einem schriftlichen Diskurs zu identifizieren, um die Absender und Empfänger von Nachrichten zu bestimmen. Jedoch sind die sprach- lichen Möglichkeiten in der Kreation von Nicknamen und auch deren Verwendung im Diskurs vielfältig. Um zu untersuchen, wie Nicknamen kreiert und in IRC verwendet wer- den, wurden Logs von 13 Gesprächskanälen (Channels) bestehend aus 8937 öffentlichen Chat-Nachrichten und 7936 einzigartigen Nicknamen detailliert analysiert. In dieser Dissertation wird beschrieben, welche grundlegende Struktur IRC Nicknamen haben, aus welchen Gruppen von Wortarten Nicknamen zusammengesetzt sind, und welche Teile der Nicknamen im Chat-Diskurs weggelassen werden. Dieses Wissen, in Kombination mit der Identität des eingeloggten Benutzers, führt zu einer besseren Vorhersage darüber, ob das untersuchte Wort im Diskurs eine verkürzte oder kreativ veränderte Form eines Nicknamens sein kann. Darüber hinaus verbessert diese Arbeit zwei weitere Funktionen: Erstens, die automatisierte Erkennung von geschriebenen Empfängernamen (oder Teilen davon) und ihre Zuordnung zu eingeloggten Benutzern. Zweitens, wenn kein Empfänger- name geschrieben wird, das automatisierte Erraten des Empfängernamens ohne Semantik. Die Architektur der Software wird im Detail beschrieben. Ein IRC-Diskurs mit 5605 Nachrichten wird manuell und automatisch analysiert, beide Ansätze erzielen ähnlich gute Ergebnisse für die Erkennung und das Erraten von Sender-Empfänger-Relationen. i Abstract Various forms of computer-mediated communication (CMC) have become ubiquitous, and influence our lives in many ways. Facebook, Myspace, Skype, Twitter, WhatsApp, and YouTube produce enormous amounts of traffic and data—which is ideal for analysis. Automated tools for discourse analysis process this tremendous amount of computer- mediated discourse quickly. The aim of this thesis is to describe and develop a software architecture for an automated tool that analyzes computer-mediated discourses to answer the question “Who is communicating with whom?” at any point in time. Assigning receivers to each message is an important step. While direct addressing is helpful, it is not used in every message. The author explores popular communication models and the most widely used CMC systems. The underlying communication model highlights the basic elements of CMC, and shows how this communication takes place. Based on this understanding, multiple views are defined by using different attributes and various guiding questions. Practical examples explain which basic information can be extracted from text-based discourses, and how that is done. The author mainly focuses on Internet Relay Chat (IRC) as an applied example because of its open and well-documented protocol. In discourses, it is not always clear who is communicating with whom; which especially affects the automatic analysis of discourses. It is important to identify the users’ nicknames in written discourse in order to determine who the respective senders and receivers are. However, the linguistic possibilities in nickname creation, and of using nicknames in the discourse, are various. To study how nicknames are created and used in IRC, logs of 13 channels, consisting of 8937 public chat messages and 7936 unique nicknames, are analyzed in detail. This thesis shows the basic structure of IRC nicknames, which parts of speech group are used to compound nicknames, and which parts of speech of a nickname are omitted within the chat discourse. This knowledge leads to a better prediction as to whether there is a link between a current logged-in user and the examined word in discourse, which can be a shortened or creatively changed form of a nickname. Additionally, this work improves two other aspects: first, automated detection and mapping of written receiver names (or parts thereof) for logged-in users; and second, automated receiver guessing without semantics if no receiver name is specified. The architecture of the automated software is described in detail. An IRC discourse with 5605 messages is manually and automatically analyzed, and both approaches achieve similar results in detecting and guessing sender-receiver relations. iii Dedicated to my family and all my friends. Thank you for bringing color to my life. Thank you also to the staff of the hospital “Krankenhaus der Barmherzigen Schwestern Ried”. Everybody can make their dreams come true. — Walter Elias “Walt” Disney (1901–1966) Acknowledgment I am grateful to many people who were directly or indirectly involved in the preparation of this thesis. In particular, I want to thank • my supervisor Univ.-Prof. Mag. Dr. Gabriele Anderst-Kotsis for her guidance, valuable feedback, and support that she provided throughout this work, • o. Univ.-Prof. Dipl.-Ing. Dr. Michael Schrefl for reading this thesis as the second examiner and giving many valuable hints on various topics, • Mag. Dr. Michael Karlinger for helpful discussions and feedbacks, • Dewi Williams for proofreading my three papers and an earlier version of this thesis, • Susan Gall, Leila Johnston, and Hashim Zakiullah for proofreading the thesis, • Ewa Joanna Bogad, Carolin Zeitler, and Paul Froemel for reading the German abstract, • the peer-reviewers for their helpful comments, and • all my student colleagues for making the time at university as enjoyable as it was. ix Contents Abstract iii Contents xi List of tables xix List of figures xxiii 1 Introduction 1 1.1 Research background and motivation . 1 1.2 Objectives, research questions, and guiding questions . 2 1.3 Scope and constraints . 4 1.4 Research approach . 4 1.5 Structure of the thesis . 6 1.5.1 (Computer-mediated) communication . 6 1.5.2 Computer-mediated discourse analysis . 6 1.5.3 Automated discourse analysis with a focus on IRC . 6 1.6 List of original publications . 7 1.7 Chapter summary . 7 I (Computer-mediated) communication9 2 Communication and communication models 11 2.1 Definitions . 11 2.1.1 Communication . 11 2.1.2 Communication model . 12 2.2 Communication models . 12 2.2.1 Aristotle . 13 2.2.2 Ferdinand de Saussure . 13 2.2.3 Karl Bühler . 13 2.2.4 Wendell Johnson . 14 2.2.5 Harold Dwight Lasswell . 15 2.2.6 Claude Elwood Shannon and Warren Weaver . 15 2.2.7 Jürgen Ruesch and Gregory Bateson . 16 2.2.8 Theodore Mead Newcomb . 16 2.2.9 Wilbur Schramm . 16 2.2.10 Charles Egerton Osgood and Wilbur Schramm . 17 2.2.11 George Gerbner . 18 2.2.12 Bruce H. Westley and Malcolm S. MacLean . 19 2.2.13 John W. Riley and Matilda White Riley . 19 2.2.14 David Kenneth Berlo . 19 2.2.15 Roman Ossipowitsch Jakobson . 20 xi Contents 2.2.16 Gerhard Maletzke . 20 2.2.17 Melvin Lawrence DeFleur . 21 2.2.18 Frank E. X. Dance . 21 2.2.19 Samuel L. Becker . 22 2.2.20 Elizabeth G. Andersch, Lorin C. Staats, and Robert N. Bostrom . 22 2.2.21 Dean C. Barnlund . 23 2.2.22 Ray Eldon Hiebert, Donald F. Ungurait, and Thomas W. Bohn . 24 2.2.23 D. Lawrence Kincaid . 24 2.2.24 Friedemann Schulz von Thun . 25 2.3 Chapter summary . 25 3 Computer-mediated communication (CMC) 27 3.1 Definition . 27 3.2 Characteristics of CMC . 28 3.3 CMC systems . 29 3.3.1 Electronic mailing . 30 3.3.2 Electronic mailing list . 33 3.3.3 Usenet . 34 3.3.4 Forum . 36 3.3.5 Blog . 37 3.3.6 Wiki . 38 3.3.7 Online chat . 39 3.3.8 Instant messaging . 40 3.3.9 Text messaging . 42 3.3.10 Social network site . 44 3.3.11 Other CMC systems . 44 3.4 Advantages and disadvantages of CMC . 44 3.4.1 Technology . 44 3.4.2 Costs . 45 3.4.3 Availability . 45 3.4.4 Place . 45 3.4.5 Mobility . 45 3.4.6 Time . 45 3.4.7 Access . 46 3.4.8 Data exchange . 46 3.4.9 Interactivity . 46 3.4.10 Shared knowledge . 46 3.4.11 Amount of data . 46 3.4.12 Richness of communication . 46 3.4.13 Anonymity . 46 3.4.14 Storage of data . 47 3.5 Ethical issues in CMC research . 47 3.6 Chapter summary . 47 4 Communication model for CMC 49 4.1 Computer-mediated communication process .
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