Advancing Automated Content Analysis for a New Era of Media Effects Research: The Key Role of Transfer Learning

[1]  Wouter van Atteveldt,et al.  Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI , 2023, Political Analysis.

[2]  Marc Ziegele,et al.  Developing an Incivility Dictionary for German Online Discussions – a Semi-Automated Approach Combining Human and Artificial Knowledge , 2023, Communication Methods and Measures.

[3]  Hauke Licht.  Cross-Lingual Classification of Political Texts Using Multilingual Sentence Embeddings , 2023, Political Analysis.

[4]  grafzahl: fine-tuning Transformers for text data from within R , 2023, Computational Communication Research.

[5]  Tilman Beck,et al.  Investigating Opinions on Public Policies in Digital Media: Setting up a Supervised Machine Learning Tool for Stance Classification , 2022, Communication Methods and Measures.

[6]  P. Valkenburg,et al.  Promises and Pitfalls of Social Media Data Donations , 2022, Communication Methods and Measures.

[7]  Maximilian Wich,et al.  Creating and Comparing Dictionary, Word Embedding, and Transformer-Based Models to Measure Discrete Emotions in German Political Text , 2022, Political Analysis.

[8]  Aleksandra Urman,et al.  “Foreign beauties want to meet you”: The sexualization of women in Google’s organic and sponsored text search results , 2022, New Media Soc..

[9]  Samuel E. Bestvater,et al.  Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis , 2022, Political Analysis.

[10]  Ruben L. Bach,et al.  Understanding political news media consumption with digital trace data and natural language processing , 2022, Journal of the Royal Statistical Society: Series A (Statistics in Society).

[11]  Birgit Stark,et al.  Mapping Exposure Diversity: The Divergent Effects of Algorithmic Curation on News Consumption , 2022, Journal of Communication.

[12]  Margrit Betke,et al.  Proposing an Open-Sourced Tool for Computational Framing Analysis of Multilingual Data , 2022, Digital Journalism.

[13]  A. Gasparetto,et al.  A Survey on Text Classification Algorithms: From Text to Predictions , 2022, Inf..

[14]  Sam Davidson,et al.  Over-Time Trends in Incivility on Social Media: Evidence From Political, Non-Political, and Mixed Sub-Reddits Over Eleven Years , 2021, Frontiers in Political Science.

[15]  Ivandré Paraboni,et al.  Multi‐source BERT stack ensemble for cross‐domain author profiling , 2021, Expert Syst. J. Knowl. Eng..

[16]  Jakob-Moritz Eberl,et al.  Building the Bridge: Topic Modeling for Comparative Research , 2021, Communication Methods and Measures.

[17]  F. Esser,et al.  Selective exposure in different political information environments – How media fragmentation and polarization shape congruent news use , 2021, European journal of communication.

[18]  Magdalena Wojcieszak,et al.  Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks , 2021, The International Journal of Press/Politics.

[19]  Allycen R Kurup,et al.  Associations between Self-Reports and Device-Reports of Social Networking Site Use: An Application of the Truth and Bias Model , 2021, Communication methods and measures.

[20]  Mohammad Taher Pilehvar,et al.  Analysis and Evaluation of Language Models for Word Sense Disambiguation , 2021, CL.

[21]  Claes H. de Vreese,et al.  Linking Media Content and Survey Data in a Dynamic and Digital Media Environment – Mobile Longitudinal Linkage Analysis , 2021, Digital Journalism.

[22]  Emily M. Bender,et al.  On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 , 2021, FAccT.

[23]  Rini Wijayanti,et al.  Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis , 2021, ACM Trans. Asian Low Resour. Lang. Inf. Process..

[24]  Henry Nunoo-Mensah,et al.  Transformer models for text-based emotion detection: a review of BERT-based approaches , 2021, Artificial Intelligence Review.

[25]  Wouter van Atteveldt,et al.  The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms , 2021, Communication Methods and Measures.

[26]  James Zou,et al.  Persistent Anti-Muslim Bias in Large Language Models , 2021, AIES.

[27]  Mohammad Hossein Moattar,et al.  Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification , 2020, Knowl. Based Syst..

[28]  Kichun Lee,et al.  Knowledge distillation for BERT unsupervised domain adaptation , 2020, Knowledge and Information Systems.

[29]  Yi Tay,et al.  Efficient Transformers: A Survey , 2020, ACM Comput. Surv..

[30]  Johannes Breuer,et al.  New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research , 2020, Media and Communication.

[31]  Jingyu Wang,et al.  Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis , 2020, ACL.

[32]  Claes H. de Vreese,et al.  Online News User Journeys: The Role of Social Media, News Websites, and Topics , 2020, Digital Journalism.

[33]  Aylin Caliskan,et al.  Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases , 2020, AIES.

[34]  Emily Denton,et al.  Social Biases in NLP Models as Barriers for Persons with Disabilities , 2020, ACL.

[35]  Daniel Rosen,et al.  How psychosocial well-being and usage amount predict inaccuracies in retrospective estimates of digital technology use , 2020, Mobile Media & Communication.

[36]  Theo Araujo,et al.  What’s the Tone? Easy Doesn’t Do It: Analyzing Performance and Agreement Between Off-the-Shelf Sentiment Analysis Tools , 2019, Communication Methods and Measures.

[37]  Guda van Noort,et al.  Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media , 2019, International Journal of Research in Marketing.

[38]  Barbara Poblete,et al.  Hate Speech Detection is Not as Easy as You May Think: A Closer Look at Model Validation , 2019, SIGIR.

[39]  Christopher Cochrane,et al.  Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora , 2019, Political Analysis.

[40]  Martijn Schoonvelde,et al.  No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications , 2018, Political Analysis.

[41]  Andreu Casas,et al.  Images that Matter: Online Protests and the Mobilizing Role of Pictures , 2018, Political Research Quarterly.

[42]  Michael Sedlmair,et al.  More than Bags of Words: Sentiment Analysis with Word Embeddings , 2018 .

[43]  Wouter van Atteveldt,et al.  When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science , 2018 .

[44]  Gianluca Stringhini,et al.  Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior , 2018, ICWSM.

[45]  Blake Lemoine,et al.  Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.

[46]  Claes H. de Vreese,et al.  Linking Survey and Media Content Data: Opportunities, Considerations, and Pitfalls , 2017 .

[47]  Michael Scharkow,et al.  How Measurement Error in Content Analysis and Self-Reported Media Use Leads to Minimal Media Effect Findings in Linkage Analyses: A Simulation Study , 2017 .

[48]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[49]  John D. Wilkerson,et al.  Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges , 2017 .

[50]  Prakash Ishwar,et al.  Big Social Data Analytics in Journalism and Mass Communication , 2016 .

[51]  Damian Trilling,et al.  Taking Stock of the Toolkit , 2016, Rethinking Research Methods in an Age of Digital Journalism.

[52]  Erik Cambria,et al.  Fusing audio, visual and textual clues for sentiment analysis from multimodal content , 2016, Neurocomputing.

[53]  Fabrício Benevenuto,et al.  A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods , 2015, ArXiv.

[54]  Claes H. de Vreese,et al.  Using Supervised Machine Learning to Code Policy Issues , 2015 .

[55]  Georgios Paltoglou,et al.  Signals of Public Opinion in Online Communication , 2015 .

[56]  Daan Odijk,et al.  Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis , 2014 .

[57]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[58]  Justin Grimmer,et al.  Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.

[59]  S. McAdams,et al.  News, Politics, and Negativity , 2012 .

[60]  Terry Flew,et al.  THE PROMISE OF COMPUTATIONAL JOURNALISM , 2012 .

[61]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[62]  Jennifer S. Holmes,et al.  Political Science Computing: A Review of Trends in Computer Evolution and Political Science Research , 2009 .

[63]  M. Prior,et al.  The Immensely Inflated News Audience: Assessing Bias in Self-Reported News Exposure , 2009 .

[64]  W. Bennett,et al.  A New Era of Minimal Effects? The Changing Foundations of Political Communication , 2008 .

[65]  Jan Kleinnijenhuis,et al.  A test of rivaling approaches to explain news effects: News on isue positions of parties, real-world developments, support and criticism, and success and failure , 2007 .

[66]  S. Nangi,et al.  TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes , 2022, SEMEVAL.

[67]  Michael L. Wick,et al.  Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models , 2022, ACL.

[68]  Qi Hao,et al.  Measuring smartphone usage and task switching with log tracking and self-reports , 2019 .

[69]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .