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@article{kenes2021proud,
title={The Proud Boys: Chauvinist Poster Child of Far-Right Extremism},
author={Kenes, Bulent},
year={2021},
publisher={ECPS}
}
@article{DBLP:journals/corr/abs-2009-10311,
author = {Alon Y. Halevy and
Cristian Canton{-}Ferrer and
Hao Ma and
Umut Ozertem and
Patrick Pantel and
Marzieh Saeidi and
Fabrizio Silvestri and
Ves Stoyanov},
title = {Preserving Integrity in Online Social Networks},
journal = {CoRR},
volume = {abs/2009.10311},
year = {2020},
url = {https://arxiv.org/abs/2009.10311},
eprinttype = {arXiv},
eprint = {2009.10311},
timestamp = {Wed, 23 Sep 2020 15:51:46 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2009-10311.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
noauthor={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
author={He et al., Kaiming},
booktitle={CVPR},
pages={770--778},
year={2016}
}
@article{gan2020large,
title={Large-scale adversarial training for vision-and-language representation learning},
noauthor={Gan, Zhe and Chen, Yen-Chun and Li, Linjie and Zhu, Chen and Cheng, Yu and Liu, Jingjing},
author={Gan et al., Zhe},
journal={NeurIPS},
volume={33},
pages={6616--6628},
year={2020}
}
@inproceedings{chen2019uniter,
title={Uniter: Universal image-text representation learning},
noauthor={Chen, Yen-Chun and Li, Linjie and Yu, Licheng and El Kholy, Ahmed and Ahmed, Faisal and Gan, Zhe and Cheng, Yu and Liu, Jingjing},
author={Chen et al., Yen-Chun},
booktitle={ECCV},
pages={104--120},
year={2020},
organization={Springer}
}
@inproceedings{kiela2021hateful,
title={The hateful memes challenge: competition report},
noauthor={Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Fitzpatrick, Casey A and Bull, Peter and Lipstein, Greg and Nelli, Tony and Zhu, Ron and others},
author={Kiela et al., Douwe},
booktitle={NeurIPS},
pages={344--360},
year={2021}
}
@article{su2019vl,
title={{VL-BERT}: Pre-training of generic visual-linguistic representations},
noauthor={Su, Weijie and Zhu, Xizhou and Cao, Yue and Li, Bin and Lu, Lewei and Wei, Furu and Dai, Jifeng},
author={Su et al., Weijie},
journal={arXiv:1908.08530},
year={2019}
}
@article{nockleby2000hate,
title={Hate speech},
author={Nockleby, John T},
journal={Encyclopedia of the American constitution},
volume={3},
number={2},
pages={1277--1279},
year={2000},
publisher={Macmillan Detroit}
}
@inproceedings{fersini2019detecting,
title={Detecting sexist MEME on the Web: A study on textual and visual cues},
noauthor={Fersini, Elisabetta and Gasparini, Francesca and Corchs, Silvia},
author={Fersini et al., Elisabetta},
booktitle={ACIIW},
pages={226--231},
year={2019}
}
@article{nakov2021detecting,
title={Detecting abusive language on online platforms: A critical analysis},
noauthor={Nakov, Preslav and Nayak, Vibha and Dent, Kyle and Bhatawdekar, Ameya and Sarwar, Sheikh Muhammad and Hardalov, Momchil and Dinkov, Yoan and Zlatkova, Dimitrina and Bouchard, Guillaume and Augenstein, Isabelle},
author={Nakov et al., Preslav},
journal={arXiv:2103.00153},
year={2021}
}
@inproceedings{wulczyn2017ex,
title={Ex machina: Personal attacks seen at scale},
noauthor={Wulczyn, Ellery and Thain, Nithum and Dixon, Lucas},
author={Wulczyn et al., Ellery},
booktitle={WWW},
pages={1391--1399},
year={2017}
}
@inproceedings{hammer2014detecting,
title={Detecting threats of violence in online discussions using bigrams of important words},
author={Hammer, Hugo Lewi},
booktitle={2014 IEEE Joint Intelligence and Security Informatics Conference},
pages={319--319},
year={2014},
organization={IEEE}
}
@INPROCEEDINGS{8877435, author={Hammer, Hugo L. and Riegler, Michael A. and Øvrelid, Lilja and Velldal, Erik}, booktitle={CBMI}, title={THREAT: A Large Annotated Corpus for Detection of Violent Threats}, year={2019}, volume={}, number={}, pages={1-5}, doi={10.1109/CBMI.2019.8877435}}
@article{doi:10.1080/23335777.2021.1940303,
author = {Huiling Yao and Xing Hu},
title = {A survey of video violence detection},
journal = {Cyber-Physical Systems},
volume = {0},
number = {0},
pages = {1-24},
year = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/23335777.2021.1940303},
URL = {
https://doi.org/10.1080/23335777.2021.1940303
},
eprint = {
https://doi.org/10.1080/23335777.2021.1940303
}
}
@article{yao2021survey,
title={A survey of video violence detection},
author={Yao, Huiling and Hu, Xing},
journal={Cyber-Physical Systems},
pages={1--24},
year={2021},
publisher={Taylor \& Francis}
}
@article{krug2002world,
title={The world report on violence and health},
noauthor={Krug, Etienne G and Mercy, James A and Dahlberg, Linda L and Zwi, Anthony B},
author={Krug et al., Etienne G},
journal={The lancet},
volume={360},
number={9339},
pages={1083--1088},
year={2002},
publisher={Elsevier}
}
@article{ramzan2019review,
title={A review on state-of-the-art violence detection techniques},
noauthor={Ramzan, Muhammad and Abid, Adnan and Khan, Hikmat Ullah and Awan, Shahid Mahmood and Ismail, Amina and Ahmed, Muzamil and Ilyas, Mahwish and Mahmood, Ahsan},
author={Ramzan et al., Muhammad},
journal={IEEE Access},
volume={7},
pages={107560--107575},
year={2019},
publisher={IEEE}
}
@inproceedings{10.1145/3038912.3052555,
noauthor = {Wang, Yilin and Tang, Jiliang and Li, Jundong and Li, Baoxin and Wan, Yali and Mellina, Clayton and O'Hare, Neil and Chang, Yi},
author = {Wang et al., Yilin},
title = {Understanding and Discovering Deliberate Self-Harm Content in Social Media},
year = {2017},
isbn = {9781450349130},
url = {https://doi.org/10.1145/3038912.3052555},
doi = {10.1145/3038912.3052555},
abstract = {Studies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous and increasing volume of social media data, on-line self-harm content is likely to be buried rapidly by other normal content. To enable voices of self-harm users to be heard, it is important to distinguish self-harm content from other types of content. In this paper, we aim to understand self-harm content and provide automatic approaches to its detection. We first perform a comprehensive analysis on self-harm social media using different input cues. Our analysis, the first of its kind in large scale, reveals a number of important findings. Then we propose frameworks that incorporate the findings to discover self-harm content under both supervised and unsupervised settings. Our experimental results on a large social media dataset from Flickr demonstrate the effectiveness of the proposed frameworks and the importance of our findings in discovering self-harm content.},
booktitle = {WWW},
pages = {93–102},
numpages = {10},
keywords = {mental health, social media mining, self-harm detection, user modeling}
}
@article{Survey:2021:Abusive:Language,
author = {Preslav Nakov and
Vibha Nayak and
Kyle Dent and
Ameya Bhatawdekar and
Sheikh Muhammad Sarwar and
Momchil Hardalov and
Yoan Dinkov and
Dimitrina Zlatkova and
Guillaume Bouchard and
Isabelle Augenstein},
title = {Detecting Abusive Language on Online Platforms: {A} Critical Analysis},
journal = {arXiv/2103.00153},
year = {2021},
}
@inproceedings{parapar2021overview,
title={Overview of erisk 2021: Early risk prediction on the internet},
noauthor={Parapar, Javier and Mart{\'\i}n-Rodilla, Patricia and Losada, David E and Crestani, Fabio},
author={Parapar et al., Javier},
booktitle={CLEF},
pages={324--344},
year={2021}
}
@inproceedings{losada2020overview,
title={{Overview of eRisk at CLEF 2020:} Early Risk Prediction on the Internet (Extended Overview).},
noauthor={Losada, David E and Crestani, Fabio and Parapar, Javier},
author={Losada et al., David E},
booktitle={CLEF (Working Notes)},
year={2020}
}
@inproceedings{10.1145/3018661.3018706,
noauthor = {Wang, Tao and Brede, Markus and Ianni, Antonella and Mentzakis, Emmanouil},
author = {Wang et al., Tao},
title = {Detecting and Characterizing Eating-Disorder Communities on Social Media},
year = {2017},
isbn = {9781450346757},
url = {https://doi.org/10.1145/3018661.3018706},
doi = {10.1145/3018661.3018706},
abstract = {Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.},
booktitle = {WSDM},
pages = {91–100},
numpages = {10},
keywords = {text mining, mental health, eating disorder, social media, homophily, network analysis}
}
@article{doi:10.1177/1461444819850106,
noauthor = {Florian Arendt and Sebastian Scherr and Daniel Romer},
author = {Florian et al., Arendt},
title ={Effects of exposure to self-harm on social media: Evidence from a two-wave panel study among young adults},
journal = {New Media \& Soc.},
volume = {21},
number = {11-12},
pages = {2422-2442},
year = {2019},
doi = {10.1177/1461444819850106},
URL = {
https://doi.org/10.1177/1461444819850106
},
eprint = {
https://doi.org/10.1177/1461444819850106
}
}
@article{seko2018self,
title={The self—harmed, visualized, and reblogged: Remaking of self-injury narratives on Tumblr},
author={Seko, Yukari and Lewis, Stephen P},
journal={New media \& society},
volume={20},
number={1},
pages={180--198},
year={2018},
publisher={SAGE Publications Sage UK: London, England}
}
@Inbook{Laffier2016,
author="Laffier, Jennifer L.",
editor="Walrave, Michel
and Ponnet, Koen
and Vanderhoven, Ellen
and Haers, Jacques
and Segaert, Barbara",
title="Social Relations: Exploring How Youth Use Social Media to Communicate Signs and Symptoms of Depression and Suicidal Ideation",
bookTitle="Youth 2.0: Social Media and Adolescence: Connecting, Sharing and Empowering",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="161--178",
abstract="Over the last decade it has become apparent that social media may act as a vehicle for youth to communicate signs and symptoms of mental health distress. Therefore, this qualitative case study explored (1) how signs and symptoms of suicide ideation and severe depression can present within the context of social media and (2) how on-line signs and symptoms can be used to create screening tools for the general public. Content analysis of media reports, newspapers, and social media sites revealed several themes such as (1) loneliness, depression and hopelessness were key symptoms, (2) personality characteristics and frequency of social media use acted as mediators to identifying escalation of mental health distress, and (3) engaging in social media may provide both support and further pain for a person experiencing depression. These insights are used to make recommendations for future studies and the development of online screening tools.",
isbn="978-3-319-27893-3",
doi="10.1007/978-3-319-27893-3_9",
url="https://doi.org/10.1007/978-3-319-27893-3_9"
}
@inproceedings{singh2017toward,
title={Toward multimodal cyberbullying detection},
noauthor={Singh, Vivek K and Ghosh, Souvick and Jose, Christin},
author={Singh et al., Vivek K},
booktitle={CHI EA},
pages={2090--2099},
year={2017}
}
@article{rosa2019automatic,
title={Automatic cyberbullying detection: A systematic review},
noauthor={Rosa, Hugo and Pereira, N{\'a}dia and Ribeiro, Ricardo and Ferreira, Paula Costa and Carvalho, Joao Paulo and Oliveira, Sofia and Coheur, Lu{\'\i}sa and Paulino, Paula and Sim{\~a}o, AM Veiga and Trancoso, Isabel},
author={Rosa et al., Hugo},
journal={Computers in Human Behavior},
volume={93},
pages={333--345},
year={2019},
publisher={Elsevier}
}
@article{vogels2021state,
title={The state of online harassment},
author={Vogels, Emily A},
journal={Pew Research Center},
volume={13},
year={2021}
}
@article{10.1371/journal.pone.0243300,
doi = {10.1371/journal.pone.0243300},
author = {Vidgen, Bertie AND Derczynski, Leon},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Directions in abusive language training data, a systematic review: Garbage in, garbage out},
year = {2021},
month = {12},
volume = {15},
url = {https://doi.org/10.1371/journal.pone.0243300},
pages = {1-32},
abstract = {Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.},
number = {12},
}
@article{husain2021survey,
title={A Survey of Offensive Language Detection for the Arabic Language},
author={Husain, Fatemah and Uzuner, Ozlem},
journal={ACM-TALLIP},
volume={20},
number={1},
pages={1--44},
year={2021},
publisher={ACM New York, NY, USA}
}
@INPROCEEDINGS{7920246,
noauthor={Haidar, Batoul and Chamoun, Maroun and Yamout, Fadi},
author={Haidar et al., Batoul},
booktitle={EMS},
title={Cyberbullying Detection: A Survey on Multilingual Techniques},
year={2016},
volume={},
number={},
pages={165-171},
doi={10.1109/EMS.2016.037}
}
@inproceedings{afridi2021multimodal,
title={A Multimodal Memes Classification: A Survey and Open Research Issues},
noauthor={Afridi, Tariq Habib and Alam, Aftab and Khan, Muhammad Numan and Khan, Jawad and Lee, Young Koo},
author={Afridi et al., Tariq Habib},
booktitle={SCA},
pages={1451--1466},
year={2021}
}
@article{alam2021survey,
title={A Survey on Multimodal Disinformation Detection},
noauthor={Alam, Firoj and Cresci, Stefano and Chakraborty, Tanmoy and Silvestri, Fabrizio and Dimitrov, Dimiter and Martino, Giovanni Da San and Shaar, Shaden and Firooz, Hamed and Nakov, Preslav},
author={Alam et al., Firoj},
journal={arXiv :2103.12541},
year={2021}
}
@article{hegde2021images,
title={Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification},
noauthor={Hegde, Siddhanth U and Hande, Adeep and Priyadharshini, Ruba and Thavareesan, Sajeetha and Sakuntharaj, Ratnasingam and Thangasamy, Sathiyaraj and Bharathi, B and Chakravarthi, Bharathi Raja},
author={Hegde et al., Siddhanth U},
journal={arXiv:2108.03886},
year={2021}
}
@inproceedings{suryawanshi-etal-2020-dataset,
title = "A Dataset for Troll Classification of {T}amil{M}emes",
noauthor = "Suryawanshi, Shardul and
Chakravarthi, Bharathi Raja and
Verma, Pranav and
Arcan, Mihael and
McCrae, John Philip and
Buitelaar, Paul",
author = "Suryawanshi et al., Shardul",
booktitle = "WILDRE",
nomonth = may,
year = "2020",
url = "https://aclanthology.org/2020.wildre-1.2",
pages = "7--13",
abstract = "Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.",
language = "English",
ISBN = "979-10-95546-67-2",
}
@book{shifman2013memes,
title={Memes in digital culture},
author={Shifman, Limor},
year={2013},
publisher={MIT press}
}
@inproceedings{mubarak2017abusive,
title={Abusive language detection on Arabic social media},
author={Mubarak, Hamdy and Darwish, Kareem and Magdy, Walid},
booktitle={WALO},
pages={52--56},
year={2017}
}
@inproceedings{kumar2018benchmarking,
title={Benchmarking aggression identification in social media},
noauthor={Kumar, Ritesh and Ojha, Atul Kr and Malmasi, Shervin and Zampieri, Marcos},
author={Kumar et al., Ritesh},
booktitle={TRAC},
pages={1--11},
year={2018}
}
@article{fortuna2018survey,
title={A survey on automatic detection of hate speech in text},
author={Fortuna, Paula and Nunes, S{\'e}rgio},
journal={CSUR},
volume={51},
number={4},
pages={1--30},
year={2018},
publisher={ACM New York, NY, USA}
}
@INPROCEEDINGS{9455994,
author={Zhou, Yi and Chen, Zhenhao and Yang, Huiyuan},
booktitle={ICMEW},
title={Multimodal Learning For Hateful Memes Detection},
year={2021},
volume={},
number={},
pages={1-6},
doi={10.1109/ICMEW53276.2021.9455994}}
@inproceedings{pramanick-etal-2021-momenta-multimodal,
title = "{MOMENTA}: A Multimodal Framework for Detecting Harmful Memes and Their Targets",
noauthor = "Pramanick, Shraman and
Sharma, Shivam and
Dimitrov, Dimitar and
Akhtar, Md. Shad and
Nakov, Preslav and
Chakraborty, Tanmoy",
author = "Pramanick et al., Shraman",
booktitle = "EMNLP (Findings)",
nomonth = nov,
year = "2021",
url = "https://aclanthology.org/2021.findings-emnlp.379",
doi = "10.18653/v1/2021.findings-emnlp.379",
pages = "4439--4455",
abstract = "Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. In particular, we focus on two tasks: (i)detecting harmful memes, and (ii) identifying the social entities they target. We further extend the recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.",
}
@inproceedings{mroueh2015deep,
title={Deep multimodal learning for audio-visual speech recognition},
author={Mroueh, Youssef and Marcheret, Etienne and Goel, Vaibhava},
booktitle={ICASSP},
pages={2130--2134},
year={2015},
organization={IEEE}
}
@article{liu2021cptr,
title={Cptr: Full transformer network for image captioning},
author={Liu, Wei and Chen, Sihan and Guo, Longteng and Zhu, Xinxin and Liu, Jing},
journal={arXiv preprint arXiv:2101.10804},
year={2021}
}
@article{zhu2021deep,
title={Deep audio-visual learning: A survey},
author={Zhu, Hao and Luo, Man-Di and Wang, Rui and Zheng, Ai-Hua and He, Ran},
journal={International Journal of Automation and Computing},
pages={1--26},
year={2021},
publisher={Springer}
}
@inproceedings{10.1088/1742-6596/1237/2/022144,
title={A review of audio-visual fusion with machine learning},
author={Song, Xiaoyu and Chen, Hong and Wang, Qing and Chen, Yunqiang and Tian, Mengxiao and Tang, Hui},
booktitle={J. Phys. Conf. Ser.},
volume={1237},
number={2},
pages={022144},
year={2019},
organization={IOP Publishing}
}
@article{chen2021heu,
title={HEU Emotion: a large-scale database for multimodal emotion recognition in the wild},
author={Chen, Jing and Wang, Chenhui and Wang, Kejun and Yin, Chaoqun and Zhao, Cong and Xu, Tao and Zhang, Xinyi and Huang, Ziqiang and Liu, Meichen and Yang, Tao},
journal={Neural Computing and Applications},
pages={1--17},
year={2021},
publisher={Springer}
}
@article{summaira2021recent,
title={Recent Advances and Trends in Multimodal Deep Learning: A Review},
author={Jabeen Summaira and Xi Li and Amin Muhammad Shoib and Songyuan Li and Jabbar Abdul},
year={2021},
journal={arXiv: 2105.11087},
eprint={2105.11087},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{ngiam2011multimodal,
title={Multimodal deep learning},
author={Ngiam, Jiquan and Khosla, Aditya and Kim, Mingyu and Nam, Juhan and Lee, Honglak and Ng, Andrew Y},
booktitle={ICML},
year={2011}
}
@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking: A Survey",
author = "Kotonya, Neema and Toni, Francesca",
booktitle = "COLING",
nomonth = dec,
year = "2020",
noaddress = "Barcelona, Spain (Online)",
nopublisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.474",
doi = "10.18653/v1/2020.coling-main.474",
pages = "5430--5443",
abstract = "A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality {--} that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.",
}
@inproceedings{dimitrov2021detecting,
title = "Detecting Propaganda Techniques in Memes",
noauthor = "Dimitrov, Dimitar and
Bin Ali, Bishr and
Shaar, Shaden and
Alam, Firoj and
Silvestri, Fabrizio and
Firooz, Hamed and
Nakov, Preslav and
Da San Martino, Giovanni",
author = "Dimitrov et al., Dimitar",
booktitle = "ACL-IJCNLP",
nomonth = aug,
year = "2021",
noaddress = "Online",
url = "https://aclanthology.org/2021.acl-long.516",
doi = "10.18653/v1/2021.acl-long.516",
pages = "6603--6617",
abstract = "Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. However, it is with the advent of the Internet and the social media that propaganda has started to spread on a much larger scale than before, thus becoming major societal and political issue. Nowadays, a large fraction of propaganda in social media is multimodal, mixing textual with visual content. With this in mind, here we propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes. We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both. Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques. This is further confirmed in our experiments with several state-of-the-art multimodal models."
}
@inproceedings{waseem-hovy-2016-hateful,
title = "Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on {T}witter",
author = "Waseem, Zeerak and
Hovy, Dirk",
booktitle = "Proceedings of the {NAACL} Student Research Workshop",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-2013",
doi = "10.18653/v1/N16-2013",
pages = "88--93",
}
@inproceedings{qian-etal-2018-hierarchical,
title = "Hierarchical {CVAE} for Fine-Grained Hate Speech Classification",
author = "Qian, Jing and
ElSherief, Mai and
Belding, Elizabeth and
Wang, William Yang",
booktitle = "EMNLP",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
url = "https://aclanthology.org/D18-1391",
doi = "10.18653/v1/D18-1391",
pages = "3550--3559",
abstract = "Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories. In this paper, we propose a novel method on a fine-grained hate speech classification task, which focuses on differentiating among 40 hate groups of 13 different hate group categories. We first explore the Conditional Variational Autoencoder (CVAE) as a discriminative model and then extend it to a hierarchical architecture to utilize the additional hate category information for more accurate prediction. Experimentally, we show that incorporating the hate category information for training can significantly improve the classification performance and our proposed model outperforms commonly-used discriminative models.",
}
@article{cooper2007concise,
title={A Concise History of the Fauxtography Blogstorm in the 2006 Lebanon War},
author={Cooper, Stephen D},
journal={American Communication Journal},
volume={9},
number={2},
year={2007}
}
@inproceedings{kazemi-etal-2021-claim,
title = "Claim Matching Beyond {E}nglish to Scale Global Fact-Checking",
author = "Kazemi, Ashkan and
Garimella, Kiran and
Gaffney, Devin and
Hale, Scott",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
series = "ACL-IJCNLP~'21",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.347",
doi = "10.18653/v1/2021.acl-long.347",
pages = "4504--4517",
}
@inproceedings{wan-etal-2021-dqn,
title = "A {DQN}-based Approach to Finding Precise Evidences for Fact Verification",
author = "Wan, Hai and
Chen, Haicheng and
Du, Jianfeng and
Luo, Weilin and
Ye, Rongzhen",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
series = "ACL-IJCNLP~'21",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.83",
doi = "10.18653/v1/2021.acl-long.83",
pages = "1030--1039",
}
@inproceedings{jiang-etal-2021-exploring-listwise,
title = "Exploring Listwise Evidence Reasoning with T5 for Fact Verification",
author = "Jiang, Kelvin and
Pradeep, Ronak and
Lin, Jimmy",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
series = "ACL-IJCNLP~'21",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.51",
doi = "10.18653/v1/2021.acl-short.51",
pages = "402--410",
}
@inproceedings{si-etal-2021-topic,
title = "Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification",
author = "Si, Jiasheng and
Zhou, Deyu and
Li, Tongzhe and
Shi, Xingyu and
He, Yulan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.128",
doi = "10.18653/v1/2021.acl-long.128",
pages = "1612--1622",
}
@article{shaar2021assisting,
title={Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document},
author={Shaden Shaar and Firoj Alam and Giovanni Da San Martino and Preslav Nakov},
year={2021},
journal={arXiv preprint arXiv:2109.07410},
eprint={2109.07410},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@Article{claim:retrieval:context:2021,
author = {Shaden Shaar and Firoj Alam and Da San Martino, Giovanni
and Preslav Nakov},
title = {The Role of Context in Detecting Previously Fact-Checked
Claims},
journal = {Arxiv/2104.07423},
year = {2021}
}
@inproceedings{ma2016detecting,
title={Detecting rumors from microblogs with recurrent neural networks},
author={Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J and Wong, Kam Fai and Cha, Meeyoung},
booktitle={IJCAI International Joint Conference on Artificial Intelligence},
volume={2016},
pages={3818--3824},
year={2016}
}
@inproceedings{ma2017detect,
title = "Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning",
author = "Ma, Jing and
Gao, Wei and
Wong, Kam-Fai",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1066",
doi = "10.18653/v1/P17-1066",
pages = "708--717",
}
@InProceedings{10.1007/978-3-030-73696-5_3,
author="Patwa, Parth
and Sharma, Shivam
and Pykl, Srinivas
and Guptha, Vineeth
and Kumari, Gitanjali
and Akhtar, Md Shad
and Ekbal, Asif
and Das, Amitava
and Chakraborty, Tanmoy",
editor="Chakraborty, Tanmoy
and Shu, Kai
and Bernard, H. Russell
and Liu, Huan
and Akhtar, Md Shad",
title="Fighting an Infodemic: COVID-19 Fake News Dataset",
booktitle="Combating Online Hostile Posts in Regional Languages during Emergency Situation",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="21--29",
abstract="Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We perform a binary classification task (real vs fake) and benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32{\%} F1-score with SVM on the test set. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection.",
isbn="978-3-030-73696-5"
}
@article{zarocostas2020fight,
title={How to fight an infodemic},
author={Zarocostas, John},
journal={The lancet},
volume={395},
number={10225},
pages={676},
year={2020},
publisher={Elsevier}
}
@inproceedings{sathe-etal-2020-automated,
title = "Automated Fact-Checking of Claims from {W}ikipedia",
author = "Sathe, Aalok and
Ather, Salar and
Le, Tuan Manh and
Perry, Nathan and
Park, Joonsuk",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
series = "ACL~'20",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.849",
pages = "6874--6882",
abstract = "Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. To this end, we present WikiFactCheck-English, a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations, as well as 34k+ manually written claims that are refuted by the evidence documents. This is the largest fact checking dataset consisting of real claims and evidence to date; it will allow the development of fact checking systems that can better process claims and evidence in the real world. We also show that for the NLI subtask, a logistic regression system trained using existing and novel features achieves peak accuracy of 68{\%}, providing a competitive baseline for future work. Also, a decomposable attention model trained on SNLI significantly underperforms the models trained on this dataset, suggesting that models trained on manually generated data may not be sufficiently generalizable or suitable for fact checking real-world claims.",
language = "English",
ISBN = "979-10-95546-34-4",
}
@inproceedings{atanasova-etal-2020-generating-fact,
title = "Generating Fact Checking Explanations",
author = "Atanasova, Pepa and
Simonsen, Jakob Grue and
Lioma, Christina and
Augenstein, Isabelle",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
series = "ACL~'20",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.656",
doi = "10.18653/v1/2020.acl-main.656",
pages = "7352--7364",
abstract = "Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process {--} generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.",
}
@inproceedings{alam2020fighting,
title={Fighting the {COVID}-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society},
author={Firoj Alam and Shaden Shaar and Fahim Dalvi and Hassan Sajjad and Alex Nikolov and Hamdy Mubarak and Giovanni Da San Martino and Ahmed Abdelali and Nadir Durrani and Kareem Darwish and Abdulaziz Al-Homaid and Wajdi Zaghouani and Tommaso Caselli and Gijs Danoe and Friso Stolk and Britt Bruntink and Preslav Nakov},
booktitle = {Findings of EMNLP 2021},
year={2021},
publisher = "Association for Computational Linguistics",
series = {EMNLP~'21},
}
@InProceedings{alam2020call2arms,
title = {Fighting the {COVID}-19 Infodemic in Social Media: A
Holistic Perspective and a Call to Arms},
author = {Alam, Firoj and Dalvi, Fahim and Shaar, Shaden and
Durrani, Nadir and Mubarak, Hamdy and Nikolov, Alex and {Da
San Martino}, Giovanni and Abdelali, Ahmed and Sajjad,
Hassan and Darwish, Kareem and Nakov, Preslav},
year = {2021},
pages = {913-922},
nomonth = {May},
NOvolume = {15},
booktitle = {Proceedings of the International {AAAI} Conference on Web
and Social Media},
series = {ICWSM~'21},
nourl = {https://ojs.aaai.org/index.php/ICWSM/article/view/18114}
}
@book{ireton2018journalism,
title={Journalism, fake news \& disinformation: handbook for journalism education and training},
author={Ireton, Cherilyn and Posetti, Julie},
year={2018},
publisher={Unesco Publishing}
}
@book{cooke2018fake,
title={Fake news and alternative facts: Information literacy in a post-truth era},
author={Cooke, Nicole A},
year={2018},
publisher={American Library Association}
}
@inproceedings{nakov-da-san-martino-2020-fact,
title = "Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era",
author = "Nakov, Preslav and
Da San Martino, Giovanni",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-tutorials.2",
doi = "10.18653/v1/2020.emnlp-tutorials.2",
pages = "7--19",
}
@inproceedings{
Chen2020TabFact:,
title={TabFact: A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen and Hongmin Wang and Jianshu Chen and Yunkai Zhang and Hong Wang and Shiyang Li and Xiyou Zhou and William Yang Wang},
booktitle={ICLR},
year={2020},
url={https://openreview.net/forum?id=rkeJRhNYDH}
}
@article{harkin2012deciphering,
title={Deciphering user-generated content in transitional societies},
author={Harkin, Juliette and Anderson, Kevin and Morgan, Libby and Smith, Briar},
journal={Philadelphia, PA: Center for Global Communication Studies, Annenberg School for Communication, University of Pennsylvania},
year={2012}
}
@article{doi:10.1177/2056305117717888,
author = {Adrian Rauchfleisch and Xenia Artho and Julia Metag and Senja Post and Mike S. Schäfer},
title ={How journalists verify user-generated content during terrorist crises. Analyzing Twitter communication during the Brussels attacks},
journal = {Social Media + Society},
volume = {3},
number = {3},
pages = {2056305117717888},
year = {2017},
doi = {10.1177/2056305117717888},
URL = {https://doi.org/10.1177/2056305117717888},
eprint = {
https://doi.org/10.1177/2056305117717888},
abstract = { Social media, and Twitter in particular, have become important sources for journalists in times of crises. User-generated content (UGC) can provide journalists with on-site information and material they otherwise would not have access to. But how they source and verify UGC has not yet been systematically analyzed. This study analyzes sourcing and verification practices on Twitter during the Brussels attacks in March 2016. Based on quantitative content analysis, we identified (1) the journalists and news organizations sourcing during the attacks, (2) classified different forms of sourcing and verification requests, and (3) analyzed the sourced UGC. Results show that sourcing on Twitter has become a global phenomenon. During the first hours of the attack, journalists rely on UGC. Their sourcing and verification practices vary widely and often lack basic verification procedures, which leads to a discussion about the ethical implications of sourcing practices. }
}
@inproceedings{banko-etal-2020-unified,
title = "A Unified Taxonomy of Harmful Content",
noauthor = "Banko, Michele and
MacKeen, Brendon and
Ray, Laurie",
author = "Banko et al., Michele",
booktitle = "WOAH",
nomonth = nov,
year = "2020",
noaddress = "Online",
url = "https://aclanthology.org/2020.alw-1.16",
doi = "10.18653/v1/2020.alw-1.16",
pages = "125--137",
abstract = "The ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse. While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation. As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.",
}
@article{LEWANDOWSKY2017353,
title = {Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era},
journal = {J. App. Res. in Mem. and Cogn.},
volume = {6},
number = {4},
pages = {353-369},
year = {2017},
issn = {2211-3681},
doi = {https://doi.org/10.1016/j.jarmac.2017.07.008},
url = {https://www.sciencedirect.com/science/article/pii/S2211368117300700},
author = {Stephan Lewandowsky and Ullrich K.H. Ecker and John Cook},
keywords = {Misinformation, Fake news, Post-truth politics, Demagoguery},
abstract = {The terms “post-truth” and “fake news” have become increasingly prevalent in public discourse over the last year. This article explores the growing abundance of misinformation, how it influences people, and how to counter it. We examine the ways in which misinformation can have an adverse impact on society. We summarize how people respond to corrections of misinformation, and what kinds of corrections are most effective. We argue that to be effective, scientific research into misinformation must be considered within a larger political, technological, and societal context. The post-truth world emerged as a result of societal mega-trends such as a decline in social capital, growing economic inequality, increased polarization, declining trust in science, and an increasingly fractionated media landscape. We suggest that responses to this malaise must involve technological solutions incorporating psychological principles, an interdisciplinary approach that we describe as “technocognition.” We outline a number of recommendations to counter misinformation in a post-truth world.}
}
@inproceedings{van-hee-etal-2015-detection,
title = "Detection and Fine-Grained Classification of Cyberbullying Events",
noauthor = "Van Hee, Cynthia and
Lefever, Els and
Verhoeven, Ben and
Mennes, Julie and
Desmet, Bart and
De Pauw, Guy and
Daelemans, Walter and
Hoste, Veronique",
author = "Van Hee et al., Cynthia",
booktitle = "RANLP",
month = sep,
year = "2015",
url = "https://aclanthology.org/R15-1086",
pages = "672--680",
}
@inproceedings{schmidt-wiegand-2017-survey,
title = "A Survey on Hate Speech Detection using Natural Language Processing",
author = "Schmidt, Anna and
Wiegand, Michael",
booktitle = "SocialNLP",
nomonth = apr,
year = "2017",
url = "https://aclanthology.org/W17-1101",
doi = "10.18653/v1/W17-1101",
pages = "1--10",
abstract = "This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.",
}
@inproceedings{joksimovic-etal-2019-automated,
title = "Automated Identification of Verbally Abusive Behaviors in Online Discussions",
noauthor = "Joksimovic, Srecko and
Baker, Ryan S. and
Ocumpaugh, Jaclyn and
Andres, Juan Miguel L. and
Tot, Ivan and
Wang, Elle Yuan and
Dawson, Shane",
author = "Joksimovic et al., Srecko",
booktitle = "WALO",
nomonth = aug,
year = "2019",
url = "https://aclanthology.org/W19-3505",
doi = "10.18653/v1/W19-3505",
pages = "36--45",
abstract = "Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.",
}
@inproceedings{brooke-2019-condescending,
title = "{``Condescending, Rude, Assholes''}: Framing gender and hostility on {S}tack {O}verflow",
author = "Brooke, Sian",
booktitle = "WALO",
nomonth = aug,
year = "2019",
url = "https://aclanthology.org/W19-3519",
doi = "10.18653/v1/W19-3519",
pages = "172--180",
abstract = "The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.",
}
@InProceedings{d18-1389,
author = "Baly, Ramy and Karadzhov, Georgi and Alexandrov, Dimitar
and Glass, James and Nakov, Preslav",
title = "Predicting Factuality of Reporting and Bias of News Media
Sources",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing",
series = {EMNLP~'18},
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
pages = "3528--3539",
addres = "Brussels, Belgium",
nonourl = "http://aclweb.org/anthology/D18-1389"
}
@inproceedings{baly2020written,
title = "What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context",