Looking for Traces of Textual Deepfakes in B ulgarian on Social Media (2023)

Abstract: Textual deepfakes can cause harm, especially on social media. At the moment, there are models trained to detect deepfake messages mainly for the English language, but no research or datasets currently exist for detecting them in most low-resource languages, such as Bulgarian. To address this gap, we explore three approaches. First, we machine translate an English-language social media dataset with bot messages into Bulgarian. However, the translation quality is unsatisfactory, leading us to create a new Bulgarian-language dataset with real social media messages and those generated by two language models (a new Bulgarian GPT-2 model – GPT-WEB-BG, and ChatGPT). We machine translate it into English and test existing English GPT-2 and ChatGPT detectors on it, achieving only 0.44-0.51 accuracy. Next, we train our own classifiers on the Bulgarian dataset, obtaining an accuracy of 0.97. Additionally, we apply the classifier with the highest results to a recently released Bulgarian social media dataset with manually fact-checked messages, which successfully identifies some of the messages as generated by Language Models (LM). Our results show that the use of machine translation is not suitable for textual deepfakes detection. We conclude that combining LM text detection with fact-checking is the most appropriate method for this task, and that identifying Bulgarian textual deepfakes is indeed possible.

## introduction the term "deepfake", comes from "deep learning" and "fake" and indicates (potentially) fake texts, images, or videos, generated using deep learning models @xcite . among them, "textual deepfakes" (tdf) refer to texts generated automatically with the help of generative models (gms, and lately with large language models -llms), which may also contain fake or untrue content. this makes those of them, which are spread with the intention to deceive, the automatic variant of disinformation (as defined by the european commission (ec) foot_1 . according to this ec's definition, "disinformation" is "false or misleading content that is spread with an intention to deceive or secure economic or political gain and which may cause public harm". there exist useful gms and llms applications @xcite . however, textual deepfakes can be a serious problem when spread on official information channels, as they can reach a large number of people. they are also problematic because, when fluent, they are hard to recognize by humans @xcite . tdfs can be used by politicians in their political fights and destroy a person's reputation, or to influence a large number of people about sensitive topics such as a war or health. tdfs can be especially problematic on social media, as anybody can have access to such platforms and freely post information, which can be easily spread to a larger number of population subgroups including those who do not usually follow the official media channels (such as teenagers). there is natural language processing (nlp) research on detecting lm-generated texts and tdfs for english and other languages @xcite @xcite @xcite @xcite . however, to the best of our knowledge, there is no such research for bulgarian. differently from most previous works, we consider textual deepfakes (tdfs) not just as any texts generated by lms, but specifically those lm-generated texts that contain fake information. however, we also consider detecting lm-generatedness as an important aspect in detecting tdfs. detecting textual deepfakes for bulgarian is a challenging task, as there are no lm-generated and textual deepfakes datasets in bulgarian. while some english-language methods can use lmgenerated messages actually posted on twitter by self-proclaimed bots @xcite , we could not identify such in bulgarian. we test three approaches to detect lm-generated texts. two of them use machine translation (mt), as this is a very frequent method in lower-resourced settings. while we suspect that we might not get good results in translating already broken lmgenerated texts, we experiment with mt due to the lack of appropriate bulgarian datasets and lmgenerated text detectors for bulgarian. approach 1 (described in section 4.1) tests mt for translating into bulgarian an existing english-language dataset of actually occurring lms-generated tweets (tweepfake foot_3 @xcite ). our subsequent plan is to build classifiers on the machine-translated messages. as the results of machine translating the tweep-fake messages into bulgarian are not satisfactory, we test approach 2 (explained in section 4.2). we generate a bulgarian language dataset composed of human-written messages and those generated by chatgpt and gpt-web-bg. next, we machine translate this dataset into english. we do this to test existing lm detectors for english, which are already trained on much more data. as the english-language lm detectors in approach 2 show a low accuracy, we apply approach 3 by training classifiers (see section 4.3) on our bulgarian lm-generated dataset. finally, in order to add the "fakeness" aspect of textual deepfakes to them being generated by an lm, we run a final experiment (described in section 4.4). we apply the classifier with the highest test results from approach 3 on a recently published bulgarian social media dataset manually fact-checked and annotated for containing untrue information and disinformation. we do this to check if the classifier would recognize any untrue messages or such containing disinformation as lmgenerated. the rest of the article is structured as follows: section 2 discusses the related work. section 3 introduces the existing datasets used. section 4 presents each approach with its results. section 5 provides a discussion, conclusions, and future work, and the following unnumbered sections contain the limitations of this work, the ethical and legal statements, the broader impact assessment, and the acknowledgments. ## related work in comparison with detecting deepfake images and videos, until recently there was a limited number of natural language processing (nlp) works on detecting textual deepfakes, and efforts were focused mostly on english @xcite . with the recent appearance of several large language models (llms), including the freely available for many languages chatgpt foot_4 , the amount of nlp works detecting llms-generated and deepfake texts has increased @xcite . detectors for new languages foot_5 have also appeared @xcite . the work on detecting textual deepfakes usually checks if the texts have been generated by one or more lms, generally training classifiers on lm-generated and human texts @xcite @xcite , with recent zero-shot approaches appearing too @xcite . the most recent language models are the deep learning ones: bidirectional encoder representations from transformers (bert) @xcite , the generative pre-trained transformer 2 (gpt-2) @xcite , @xcite , chatgpt foot_6 , the google's pathways language model 2 (palm 2), used in bard foot_7 and bloom @xcite . lm detectors check also for texts, generated with older neural lms, such as recurrent neural networks (rnn), long short-term memory (lstm), and convolutional neural networks (cnn). most lms can generate content in english, but there are also models for generating text in other languages such as chinese, bengali, arabic, russian, korean, slovak, spanish, czech, german, french, and macedonian. pre-trained large language models (llms) can be found online (e.g. on the hugging face platform). the newest lms for bulgarian are openai's gpt-3.5 and gpt-4, google's bard, and the gpt-web-bg foot_8 (gpt-2based) model, which we use together with chat-gpt (model 3.5) in this article. there are also 3 older pre-trained bulgarian gpt-2 lms (all by the same author) -2 small and one medium foot_9 . these models were trained on bulgarian data from books, wikipedia, and the oscar corpus. we considered them unsuitable for our task, as they generated texts with unsatisfactory quality. with the fast advances in text generation models came the need for synthetic text detectors. lms detection methods fall into three major categories: 1) simple classifiers; 2) zero-shot classifiers and 3) fine-tuning neural lms (nlms) @xcite . simple classifiers use classical machine learning methods to train models from scratch to discriminate between synthetic text generated from lms and human-written texts. zero-shot classifiers use a pre-trained generative model (e.g. gpt-2 output detector, detectgpt @xcite ) to detect if a text has been generated by the model used or by a similar model. these detectors do not require further training. in the nlm fine-tuning method a pre-trained lm (e.g., bert, roberta) is fine-tuned to detect text generated from itself or similar models. these detectors do require additional training. several pre-trained models for synthetic text detection (mostly for english) are available online (for example in hugging face) -bert, cltr, grover, open-ai gpt-2, ai text classifier, detectgpt foot_10 and roft foot_11 (human detector in the form of a game). until our work, to the best of our knowledge, there weren't any synthetic text detectors that could work with bulgarian. although there are generators for different languages, the existing datasets for detectors training are mostly in english @xcite , with chinese @xcite and russian foot_12 @xcite also available. however, there are no datasets with bulgarian lm-generated texts, especially social-media-like. among the works, which are the most simi-lar to ours are those on detecting lm-generated texts in other languages (e.g. russian, chinese, french) @xcite @xcite , but they only detect lm-generated texts, and ignore any fakeness of their content. similar to ours is also the new research on detecting chatgpt. an example is @xcite , which tests a large number of available english lms detectors and discovers that they are all not good at detecting chatgpt (@xmath0. however, this research detects english chatgpt only and does not work with bulgarian, nor does it detect textual deepfakes. finally, there are also approaches that detect (usually human-written) fake texts in social media, without taking into account the lm-generation aspect of textual deepfakes. these methods are usually based on detecting a specific style or analyzing the behavior of source accounts, comparing the messages with external news sources, and performing various types of (semi-)automatic fact-checking @xcite . ## datasets used this section describes the existing datasets used in our experiments. ## discussion, conclusions and future work this article presents the first experiments aiming to find a solution to answer the challenging question of whether textual deepfakes in bulgarian can be found in social media. we tested three approaches for detecting the "lm-generatedness" and one for the fakeness of textual deepfakes. the results indicate that utilizing machine translation (mt) in either language pair direction is not a viable solution, as textual deepfakes style may get lost in the process and the accuracy of english lm detectors is low. we conclude that the most appropriate approach for detecting textual deepfakes in bulgarian should be one involving creating our own lm-generated dataset, in combination with factchecking. in future work, we plan to generate more data with more models and on more topics. applying the classifier with the highest accuracy on bulgarian fact-checked social media texts posted after chatgpt's release is also a possible future work.