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40 results on '"Wiegmann, Matti"'

1. If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level

Author

Wiegmann, Matti, Rakete, Jennifer, Wolska, Magdalena, Stein, Benno, and Potthast, Martin

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers. Since warnings about a document intuitively need to be shown before reading it, authors usually assign trigger warnings at the document level. What parts of their writing prompted them to assign a warning, however, remains unclear. We investigate for the first time the feasibility of identifying the triggering passages of a document, both manually and computationally. We create a dataset of 4,135 English passages, each annotated with one of eight common trigger warnings. In a large-scale evaluation, we then systematically evaluate the effectiveness of fine-tuned and few-shot classifiers, and their generalizability. We find that trigger annotation belongs to the group of subjective annotation tasks in NLP, and that automatic trigger classification remains challenging but feasible.

Published
2024

2. Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification Condensed Lab Overview

Author

Ayele, Abinew Ali, Babakov, Nikolay, Bevendorff, Janek, Casals, Xavier Bonet, Chulvi, Berta, Dementieva, Daryna, Elnagar, Ashaf, Freitag, Dayne, Fröbe, Maik, Korenčić, Damir, Mayerl, Maximilian, Moskovskiy, Daniil, Mukherjee, Animesh, Panchenko, Alexander, Potthast, Martin, Rangel, Francisco, Rizwan, Naquee, Rosso, Paolo, Schneider, Florian, Smirnova, Alisa, Stamatatos, Efstathios, Stakovskii, Elisei, Stein, Benno, Taulé, Mariona, Ustalov, Dmitry, Wang, Xintong, Wiegmann, Matti, Yimam, Seid Muhie, Zangerle, Eva, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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3. De-noising Document Classification Benchmarks via Prompt-Based Rank Pruning: A Case Study

Author

Wiegmann, Matti, Stein, Benno, Potthast, Martin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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4. Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search Engines

Author

Bevendorff, Janek, Wiegmann, Matti, Potthast, Martin, Stein, Benno, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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5. Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification : Extended Abstract

Author

Bevendorff, Janek, Casals, Xavier Bonet, Chulvi, Berta, Dementieva, Daryna, Elnagar, Ashaf, Freitag, Dayne, Fröbe, Maik, Korenčić, Damir, Mayerl, Maximilian, Mukherjee, Animesh, Panchenko, Alexander, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Smirnova, Alisa, Stamatatos, Efstathios, Stein, Benno, Taulé, Mariona, Ustalov, Dmitry, Wiegmann, Matti, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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6. Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection : Condensed Lab Overview

Author

Bevendorff, Janek, Borrego-Obrador, Ian, Chinea-Ríos, Mara, Franco-Salvador, Marc, Fröbe, Maik, Heini, Annina, Kredens, Krzysztof, Mayerl, Maximilian, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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7. Overview of PAN 2023: Authorship Verification, Multi-author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection : Extended Abstract

Author

Bevendorff, Janek, Chinea-Ríos, Mara, Franco-Salvador, Marc, Heini, Annina, Körner, Erik, Kredens, Krzysztof, Mayerl, Maximilian, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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8. Continuous Integration for Reproducible Shared Tasks with TIRA.io

Author

Fröbe, Maik, Wiegmann, Matti, Kolyada, Nikolay, Grahm, Bastian, Elstner, Theresa, Loebe, Frank, Hagen, Matthias, Stein, Benno, Potthast, Martin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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9. Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, and Style Change Detection

Author

Bevendorff, Janek, Chulvi, Berta, Fersini, Elisabetta, Heini, Annina, Kestemont, Mike, Kredens, Krzysztof, Mayerl, Maximilian, Ortega-Bueno, Reynier, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barrón-Cedeño, Alberto, editor, Da San Martino, Giovanni, editor, Degli Esposti, Mirko, editor, Sebastiani, Fabrizio, editor, Macdonald, Craig, editor, Pasi, Gabriella, editor, Hanbury, Allan, editor, Potthast, Martin, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2022
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10. Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, Style Change Detection, and Trigger Detection : Extended Abstract

Author

Bevendorff, Janek, Chulvi, Berta, Fersini, Elisabetta, Heini, Annina, Kestemont, Mike, Kredens, Krzysztof, Mayerl, Maximilian, Ortega-Bueno, Reyner, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hagen, Matthias, editor, Verberne, Suzan, editor, Macdonald, Craig, editor, Seifert, Christin, editor, Balog, Krisztian, editor, Nørvåg, Kjetil, editor, and Setty, Vinay, editor

Published
2022
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11. Heuristic Feature Selection for Clickbait Detection

Author

Wiegmann, Matti, Völske, Michael, Stein, Benno, Hagen, Matthias, and Potthast, Martin

Subjects
Computer Science - Computation and Language
Abstract

We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the "clickbaitiness" of a given Twitter tweet on a scale from 0 (no clickbait) to 1 (strong clickbait). Unlike most other approaches submitted to the challenge, the baseline approach is based on manual feature engineering and does not compete out of the box with many of the deep learning-based approaches. We show that scaling up feature selection efforts to heuristically identify better-performing feature subsets catapults the performance of the baseline classifier to second rank overall, beating 12 other competing approaches and improving over the baseline performance by 20%. This demonstrates that traditional classification approaches can still keep up with deep learning on this task., Comment: Clickbait Challenge 2017

Published
2018

12. Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection

Author

Bevendorff, Janek, Chulvi, Berta, De La Peña Sarracén, Gretel Liz, Kestemont, Mike, Manjavacas, Enrique, Markov, Ilia, Mayerl, Maximilian, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Candan, K. Selçuk, editor, Ionescu, Bogdan, editor, Goeuriot, Lorraine, editor, Larsen, Birger, editor, Müller, Henning, editor, Joly, Alexis, editor, Maistro, Maria, editor, Piroi, Florina, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2021
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13. Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection : Extended Abstract

Author

Bevendorff, Janek, Chulvi, BERTa, Sarracén, Gretel Liz De La Peña, Kestemont, Mike, Manjavacas, Enrique, Markov, Ilia, Mayerl, Maximilian, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hiemstra, Djoerd, editor, Moens, Marie-Francine, editor, Mothe, Josiane, editor, Perego, Raffaele, editor, Potthast, Martin, editor, and Sebastiani, Fabrizio, editor

Published
2021
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14. Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection

Author

Bevendorff, Janek, Ghanem, Bilal, Giachanou, Anastasia, Kestemont, Mike, Manjavacas, Enrique, Markov, Ilia, Mayerl, Maximilian, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Joho, Hideo, editor, Lioma, Christina, editor, Eickhoff, Carsten, editor, Névéol, Aurélie, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2020
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15. Shared Tasks on Authorship Analysis at PAN 2020

Author

Bevendorff, Janek, Ghanem, Bilal, Giachanou, Anastasia, Kestemont, Mike, Manjavacas, Enrique, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jose, Joemon M., editor, Yilmaz, Emine, editor, Magalhães, João, editor, Castells, Pablo, editor, Ferro, Nicola, editor, Silva, Mário J., editor, and Martins, Flávio, editor

Published
2020
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16. TIRA Integrated Research Architecture

Author

Potthast, Martin, Gollub, Tim, Wiegmann, Matti, Stein, Benno, Zhai, ChengXiang, Series Editor, de Rijke, Maarten, Series Editor, Belkin, Nicholas J., Editorial Board Member, Clarke, Charles, Editorial Board Member, Kelly, Diane, Editorial Board Member, Sebastiani, Fabrizio, Editorial Board Member, Ferro, Nicola, editor, and Peters, Carol, editor

Published
2019
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17. Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-Domain Authorship Attribution and Style Change Detection

Author

Daelemans, Walter, Kestemont, Mike, Manjavacas, Enrique, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Tschuggnall, Michael, Wiegmann, Matti, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crestani, Fabio, editor, Braschler, Martin, editor, Savoy, Jacques, editor, Rauber, Andreas, editor, Müller, Henning, editor, Losada, David E., editor, Heinatz Bürki, Gundula, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2019
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19. Marco Polo's Travels Revisited: From Motion Event Detection to Optimal Path Computation in 3D Maps

Author

Niekler, Andreas, Wolska, Magdalena, Thiel, Marvin, Wiegmann, Matti, Stein, Benno, Burghardt, Manuel, Scholger, Walter, Vogeler, Georg, Tasovac, Toma, Baillot, Anne, Raunig, Elisabeth, Scholger, Martina, Steiner, Elisabeth, Centre for Information Modelling, and Helling, Patrick

Subjects
Paper, Long Presentation, spatial & spatio-temporal analysis, Travel Literature, Geography and geo-humanities, GIS, NLP, electronic literature production and analysis, Spatio-Temporal Analyisis, Literary studies, modeling and visualization, Philology, 3D
Abstract

In this work, we present a workflow for semi-automatic extraction of geo-references and motion events from the book "The Travels of Marco Polo". These are then used to create 3D renderings of the space and movement which allows readers to visually trace Marco Polo's route themselves to provide the exprience of the entirety of the journey.

Published
2023
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22. Analyzing Persuasion Strategies of Debaters on Social Media

Author

Wiegmann, Matti, Al Khatib, Khalid, Khanna, Vishal, Stein, Benno, and Computational Linguistics (CL)

Abstract

Existing studies on the analysis of persuasion in online discussions focus on investigating the effectiveness of comments in discussions and ignore the analysis of the effectiveness of debaters over multiple discussions. In this paper, we propose to quantify debaters effectiveness in the online discussion platform: “ChangeMyView” in order to explore diverse insights into their persuasion strategies. In particular, targeting debaters with different levels of effectiveness (e.g., good vs. bad), various behavioral characteristics (e..g, engagement) and text stylistic features (e.g., used frames) of debaters are carefully examined, leading to several outcomes that can be the backbone of writing assistants and persuasive text generation.

Published
2022

24. Overview of PAN 2022 : authorship verification, profiling irony and stereotype spreaders, style change detection, and trigger detection

Author

Bevendorff, Janek, Chulvi, Berta, Fersini, Elisabetta, Heini, Annina, Kestemont, Mike, Kredens, Krzysztof, Mayerl, Maximilian, Ortega-Bueno, Reyner, Pezik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, and Zangerle, Eva

Subjects
Computer. Automation
Abstract

The paper gives a brief overview of the four shared tasks to be organized at the PAN 2022 lab on digital text forensics and stylometry hosted at the CLEF 2022 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, author profiling, and content profiling. Some of the tasks continue and advance past editions (authorship verification and multi-author analysis) and some are new (profiling irony and stereotypes spreaders and trigger detection). The general goal of the PAN shared tasks is to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.

Published
2022

28. Opportunities and Risks of Disaster Data from Social Media: A Systematic Review of Incident Information

Author

Wiegmann, Matti, Kersten, Jens, Senaratne, Hansi, Potthast, Martin, Klan, Friederike, Stein, Benno, Figueiredo, Rui, Schröter, Kai, Martina, Mario Lloyd Virgilio, and Galasso, Carmine

Subjects
021110 strategic, defence & security studies, opportunities, social media, 0211 other engineering and technologies, Incident databases, 02 engineering and technology, risks, Bürgerwissenschaften
Abstract

Compiling and disseminating information about incidents and disasters is key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether, and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 large disaster and incident databases covering 27 incident types, organize the contained data and its collection process, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: impact assessment and verification of model predictions, narrative generation, enabling enhanced citizen involvement, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance. Aside from this analysis, we discuss the advantages and disadvantages of the use of social media data for closing information gaps related to incidents and disasters.

Published
2021

29. Analysis of Detection Models for Disaster-Related Tweets

Author

Wiegmann, Matti, Kersten, Jens, Klan, Friederike, Potthast, Martin, Stein, Benno, Hughes, Amanda Lee, McNeill, Fiona, and Zobel, Christopher

Subjects
Crisis Management, Tweet Filtering, Evaluation Framework, Bürgerwissenschaften
Abstract

Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus 2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46 disasters covering 9 disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of 3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability.

Published
2020

30. Disaster Tweet Corpus 2020

Author

Wiegmann, Matti, Kersten, Jens, Klan, Friederike, Potthast, Martin, and Stein, Benno

Subjects
Twitter, data set, disaster-relatedness, disasters
Published
2020

31. Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection

Author

Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Swiss National Science Foundation, Bevendorff, Janek, Ghanem, Bilal Hisham Hasan, Giachanou, Anastasia, Kestemont, Mike, Manjavacas, Enrique, Markov, Ilia, Mayerl, Maximilian, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Zangerle, Eva, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Swiss National Science Foundation, Bevendorff, Janek, Ghanem, Bilal Hisham Hasan, Giachanou, Anastasia, Kestemont, Mike, Manjavacas, Enrique, Markov, Ilia, Mayerl, Maximilian, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, and Zangerle, Eva

Abstract

[EN] We briefly report on the four shared tasks organized as part of the PAN 2020 evaluation lab on digital text forensics and authorship analysis. Each tasks is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 230 registrations, yielding 83 successful submissions. This, and the fact that we continue to invite the submissions of software rather than its run output using the TIRA experimentation platform, marks for a good start into the second decade of PAN evaluations labs.

Published
2020

32. GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules.

Author

Hu, Xuke, Al-Olimat, Hussein S., Kersten, Jens, Wiegmann, Matti, Klan, Friederike, Sun, Yeran, and Fan, Hongchao

Subjects
GEOGRAPHIC names, MICROBLOGS, DEEP learning, EMERGENCY management
Abstract

Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. [ABSTRACT FROM AUTHOR]

Published
2022
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35. Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection

Author

Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Agencia Estatal de Investigación, Daelemans, Walter, Kestemont, Mike, Manjavacas, Enrique, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Tschuggnall, Michael, Wiegmann, Matti, Zangerle, Eva, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Agencia Estatal de Investigación, Daelemans, Walter, Kestemont, Mike, Manjavacas, Enrique, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Specht, Günther, Stamatatos, Efstathios, Stein, Benno, Tschuggnall, Michael, Wiegmann, Matti, and Zangerle, Eva

Abstract

[EN] We briefly report on the four shared tasks organized as part of the PAN 2019 evaluation lab on digital text forensics and authorship analysis. Each task is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 373 registrations, yielding 72 successful submissions. This, and the fact that we continue to invite the submission of software rather than its run output using the TIRA experimentation platform, demarcates a good start into the second decade of PAN evaluations labs.

Published
2019

38. Opportunities and Risks of Disaster Data from Social Media: A Systematic Review of Incident Information.

Author

Wiegmann, Matti, Kersten, Jens, Senaratne, Hansi, Potthast, Martin, Klan, Friederike, and Stein, Benno

Subjects
SOCIAL media, META-analysis, DISASTER relief, ACQUISITION of data, DISASTERS, MISSING data (Statistics)
Abstract

Compiling and disseminating information about incidents and disasters is key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether, and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 large disaster and incident databases covering 27 incident types, organize the contained data and its collection process, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: impact assessment and verification of model predictions, narrative generation, enabling enhanced citizen involvement, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance. Aside from this analysis, we discuss the advantages and disadvantages of the use of social media data for closing information gaps related to incidents and disasters. [ABSTRACT FROM AUTHOR]

Published
2020
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39. Castle-Sized Interfaces

Author

Fischer, Patrick Tobias, primary, von der Heide, Anke, additional, Hornecker, Eva, additional, Zierold, Sabine, additional, Kästner, Andreas, additional, Dondera, Felix, additional, Wiegmann, Matti, additional, Millán, Fernando, additional, Lideikis, Jonas, additional, Čergelis, Aidas, additional, Verde, Reinaldo, additional, Drews, Christoph, additional, Fastnacht, Till, additional, Lünsdorf, Kai Gerrit, additional, Merad, Djamel, additional, Khosravani, Aryan, additional, and Jannesar, Hesam, additional

Published
2015
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40. GazPNE2: A general place name extractor for microblogs fusing gazetteers and pretrained transformer models

Author

Hu, Xuke, Zhou, Zhiyong, Sun, Yeran, Kersten, Jens, Klan, Friederike, Fan, Hongchao, Wiegmann, Matti, Hu, Xuke, Zhou, Zhiyong, Sun, Yeran, Kersten, Jens, Klan, Friederike, Fan, Hongchao, and Wiegmann, Matti

Abstract

The concept of ‘human as sensors’ defines a new sensing model, in which humans act as sensors by contributing their observations, perceptions, and sensations. This is crucial for the development of social Internet of Things, which is an integral part of Cyber-Physical-Social systems. Online social media platforms, as the most active places where users act as social sensors, are responsive to real-world events and are useful for gathering situational information in real-time. Unfortunately, posts rarely contain structured geographic information, thus hindering their usage for contributing to various challenges, such as emergency response. We address this limitation by introducing a general approach for extracting place names from tweets, named GazPNE2. It combines global gazetteers (i.e., OpenStreetMap and GeoNames), deep learning, and pretrained transformer models (i.e., BERT and BERTweet), which requires no manually annotated data. It can extract place names at both coarse (e.g., city) and fine-grained (e.g., street and POI) levels and place names with abbreviations. To fully evaluate GazPNE2 and compare it with 11 competing approaches, we use 19 public tweet datasets, containing 38,802 tweets and 22,197 places across the world. The results show GazPNE2 achieves much higher F1 (0.8) than the other approaches. Furthermore, we apply GazPNE2 to three large unannotated tweet datasets related to over 20 crisis events (e.g., COVID-19), containing 560,040 tweets. An F1 of 0.84 is achieved on 3,000 tweets, which are randomly selected from the three datasets and then manually annotated. Code and data are available on GitHub page: https://github.com/uhuohuy/GazPNE2.

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