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Utilisation of Audio Mining Technologies for Researching Public Communication on Multimedia Platforms
www.ssoar.info Utilisation of Audio Mining Technologies for Researching Public Communication on Multimedia Platforms Eble, Michael; Stein, Daniel Erstveröffentlichung / Primary Publication Sammelwerksbeitrag / collection article Empfohlene Zitierung / Suggested Citation: Eble, M., & Stein, D. (2015). Utilisation of Audio Mining Technologies for Researching Public Communication on Multimedia Platforms. In A. Maireder, J. Ausserhofer, C. Schumann, & M. Taddicken (Eds.), Digitale Methoden in der Kommunikationswissenschaft (pp. 329-345). Berlin https://doi.org/10.17174/dcr.v2.14 Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer CC BY Lizenz (Namensnennung) zur This document is made available under a CC BY Licence Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden (Attribution). For more Information see: Sie hier: https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0/deed.de Digital Communication Research.de Suggested Citation: Eble, M., & Stein, D. (2015). Utilisation of Audio Mining Technologies for Researching Public Communication on Multimedia Platforms. In A. Mai reder, J. Ausserhofer, C. Schumann, & M. Taddicken (Eds.), Digitale Meth- oden in der Kommunikationswissenschaft (S. 329-345). doi: 10.17174/dcr.v2.14 Abstract: The number and volume of spoken language corpora which are gen- erally available for research purposes increase signiicantly. That is due to the wide adoption of audio-visual communication on news websites and social web platforms. The respective messages that are published by professional and indi- vidual communicators are subject to online content analysis. To date, such analy- ses strongly rely on manually operated processes which come along with a huge effort for transcribing spoken language corpora into textual content. Hence, chal- lenges like the ever increasing volume, velocity and variety of multimedia content need to be faced. -
Forecasting the Popularity of Applications an Analysis of Textual and Graphical Properties
Forecasting the Popularity of Applications An analysis of textual and graphical properties Harro van der Kroft Master Thesis for Econometrics - Big Data Track Faculty of Economics and Business Section Econometrics Abstract This thesis contributes to the scarce literature pertaining to App Store content popularity prediction. By scraping data from the Apple App Store, we form feature sets pertaining to the textual and graphical domain. The methodology employed allows for the use of other data, from other online content sources, and fuses these feature sets by means of late fusion. This thesis researches the predictive power of Neural Networks and Support Vector Machines in parallel, and by layering different feature sets it ascertains that there is an added benefit in combining different feature sets. We reveal that there is predictive power in using the methodology outlined in this thesis. I II Acknowledgments I would like to sincerely thank my supervisor Prof. Dr. M. Worring for his supervision, patience, and enthusiasm. The passion entertained by Marcel has furthered my interests in the field of AI more than I could have hoped for. Furthermore, I would like to thank Leo Huberts, Diederik van Krieken, Frederique Arntz, and Dominique van der Vlist for their input and constructive criticism. II Statement of Originality This document is written by Student Harro van der Kroft who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. -
Anti-Immigration Speech Detection on Twitter
machine learning & knowledge extraction Article Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter Nikolaos Pitropakis 1,* , Kamil Kokot 1, Dimitra Gkatzia 1 , Robert Ludwiniak 1, Alexios Mylonas 2 and Miltiadis Kandias 3 1 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK; [email protected] (K.K.); [email protected] (D.G.); [email protected] (R.L.) 2 School of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK; [email protected] 3 Department of Informatics, Athens University of Economics and Business, 104 34 Athina, Greece; [email protected] * Correspondence: [email protected] Received: 25 June 2020; Accepted: 30 July 2020; Published: 3 August 2020 Abstract: The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. -
Detecting Variation of Emotions in Online Activities
Expert Systems With Applications 89 (2017) 318–332 Contents lists available at ScienceDirect Expert Systems With Applications journal homepage: www.elsevier.com/locate/eswa Detecting variation of emotions in online activities ∗ Despoina Chatzakou a, , Athena Vakali a, Konstantinos Kafetsios b a Department of Informatics, Aristotle University, Thessaloniki GR 54124, Greece b Department of Psychology, University of Crete, Rethymno GR 74100, Greece a r t i c l e i n f o a b s t r a c t Article history: Online text sources form evolving large scale data repositories out of which valuable knowledge about Received 27 December 2016 human emotions can be derived. Beyond the primary emotions which refer to the global emotional sig- Revised 25 July 2017 nals, deeper understanding of a wider spectrum of emotions is important to detect online public views Accepted 26 July 2017 and attitudes. The present work is motivated by the need to test and provide a system that categorizes Available online 27 July 2017 emotion in online activities. Such a system can be beneficial for online services, companies recommen- Keywords: dations, and social support communities. The main contributions of this work are to: (a) detect primary Emotion detection emotions, social ones, and those that characterize general affective states from online text sources, (b) Machine learning compare and validate different emotional analysis processes to highlight those that are most efficient, Lexicon-based approach and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and Hybrid process implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. -
Targeted Readings Recommender Using Global Auto-Learning
Targeted Readings Recommender using Global Auto-Learning Muhammad Irfan Malik1, Muhammad Junaid Majeed2, Muhammad Taimoor Khan3 FAST-NUCES, Peshawar Pakistan [email protected] [email protected] [email protected] Shehzad Khalid Bahria University, Islamabad Pakistan [email protected] ABSTRACT: Huge volume of content is produced on multiple online sources every day. It is not possible for a user to go through these articles and read about topics of interest. Secondly professional articles, blog and forum have many topics discussed in a single discussion. Therefore, a targeted readings recommender system is proposed that analyze all the docu- ments and discussions to highlight key issues discussed as topics. The topics are extracted with an Automatic knowledge- based topic modeling that allows multiple users to help grow the knowledgebase of the model which benefit new readers as well. On selecting the issues of interest the user is taken to those sections of the articles and discussions that are specifically of interest to the reader in relevance to the issue selected. The application has an ever growing knowledge-base to which every task from every user help the model grow in experience and improve its quality of learning. Key words: Recommender Systems, Knowledge Models, Online Content, Learning, Reading Received:7 March 2016, Revised 14 April 2016, Accepted 23 April 2016 © 2016 DLINE. All Rights Reserved 1. Introduction With the advent of social media platforms like Facebook, Twitter, LinkedIn, Amazon etc. users share their feelings, suggestions and trends with other like-minded users. They support discussions on topics of interest among multiple users arguing on topics through comments. -
20TH GENERAL ONLINE RESEARCH CONFERENCE 28 FEBRUARY to 2 MARCH 2018 in COLOGNE Bella Struminskaya, Florian Keusch, Otto Hellwig, Cathleen M
Organized by 20TH GENERAL ONLINE RESEARCH CONFERENCE 28 FEBRUARY TO 2 MARCH 2018 IN COLOGNE Bella Struminskaya, Florian Keusch, Otto Hellwig, Cathleen M. Stützer, Meinald Thielsch, Alexandra Wachenfeld-Schell (Eds.) 20th GENERAL ONLINE RESEARCH CONFERENCE Proceedings, Cologne 2018 IMPRINT All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of the publisher: ISBN 978-3-9815106-6-9 Bella Struminskaya, Florian Keusch, Otto Hellwig, Cathleen M. Stützer, Meinald Thielsch, Alexandra Wachenfeld-Schell (Eds.) German Society for Online Research Deutsche Gesellschaft für Online-Forschung (DGOF) e.V. (www.dgof.de) Editing: Birgit Bujard, Anja Heitmann Layout and typeset: ro:stoff media GbR www.rostoff-media.de Copyright Photos: Title – fotolia/© ADDICTIVE STOCKP. Urheber: REDPIXEL P. 8 – fotolia/ © rcfotostock P. 23,24 – Foto Mario Callegaro: ©Gareth Davies P. 12,13 – © Costa Belibasakis/TH Köln 3. TABLE OF CONTENT 20th GENERAL Organisation 6 International Board 7 ONLINE Greeting from the DGOF 8 RESEARCH About DGOF 10 Portraits of the Board 11 CONFERENCE Greetings from the Local Partner 12 Sponsors & Organizers 14 Programme Overview 16 Workshops 19 Panel Discussion 23 Keynotes 24 GOR Best Practice Award 2018 27 Abstracts GOR Best Practice Award 2018 28 GOR Poster Award 2018 32 GOR Thesis Award 2018 33 Abstracts Thursday 35 Abstracts Friday 76 4. Lorem ipsum ORGANISATION DGOF BOARD DGOF OFFICE Dr. Otto Hellwig (Chairman DGOF board, respondi AG, Germany) Birgit Bujard, Anja Heitmann Prof. Dr. Florian Keusch (University of Mannheim, Germany) Dr. Bella Struminskaya (Utrecht University, Netherlands) SUPPORT Dr.