Identifying Automatically Generated Headlines using Transformers (2021)

Abstract: False information spread via the internet and social media influences public opinion and user activity, while generative models enable fake content to be generated faster and more cheaply than had previously been possible. In the not so distant future, identifying fake content generated by deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8% of the cases. However, the most accurate automatic approach, transformers, achieved an overall accuracy of 85.7%, indicating that content generated from language models can be filtered out accurately.

## introduction fake content has been rapidly spreading across the internet and social media, misinforming and affecting users' opinion @xcite . such content includes fake news articles foot_0 and truth obfuscation campaigns foot_1 . while much of this content is being written by paid writers @xcite , content generated by automated systems is rising. models can produce text on a far greater scale than it is possible to manually, with a corresponding increase in the potential to influence public opinion. there is therefore a need for methods that can distinguish between human and computer-generated text, to filter out deceiving content before it reaches a wider audience. while text generation models have received consistent attention from the public as well as from the academic community @xcite , interest in the detection of automatically generated text has only arisen more recently @xcite . generative models have several shortcomings and their output text has characteristics that distinguish it from humanwritten text, including lower variance and smaller vocabulary @xcite ; @xcite ). these differences between real and generated text can be used by pattern recognition models to differentiate between the two. in this paper we test this hypothesis by training classifiers to detect headlines generated by a pretrained gpt-2 model @xcite . headlines were chosen as it has been shown that shorter generated text is harder to identify than longer content @xcite . the work described in this paper is split into two parts: the creation of a dataset containing headlines written by both humans and machines and training of classifiers to distinguish between them. the dataset is created using real headlines from the million headlines corpus foot_2 and headlines generated by a pretrained gpt-2. the training and development sets consist of headlines from 2015 while the testing set consists of 2016 and 2017 headlines. a series of baselines and deep learning models were tested. neural methods were found to outperform humans, with transformers being almost 35% more accurate. our research highlights how difficult it is for humans to identify computer-generated content, but that the problem can ultimately be tackled using automated approaches. this suggests that automatic methods for content analysis could play an important role in supporting readers to understand the veracity of content. the main contributions of this work are the development of a novel fake content identification task based on news headlines 4 and analysis of human evaluation and machine learning approaches to the problem. holtzman et al. (2020) investigated the pitfalls of automatic text generation, showing that sampling methods such as beam search can lead to low quality and repetitive text. gehrmann et al. (2019) showed that automatic text generation models use a more limited vocabulary than humans, tending to avoid low-probability words more often. consequently, text written by humans tends to exhibit more variation than that generated by models. in @xcite , neural fake news detection and generation are jointly examined in an adversarial setting. their model, called grover, achieves an accuracy of 92% when identifying real from generated news articles. human evaluation though is lacking, so the potential of grover to fool human readers has not been thoroughly explored. in @xcite , news articles generated by their largest model (175b parameters) managed to fool humans 48% of the time. the model, though, is prohibitively large to be applied at scale. further, @xcite showed that shorter text is harder to detect, both for humans and machines. so even though news headlines are a very potent weapon in the hands of fake news spreaders, it has not been yet examined how difficult it is for humans and models to detect machine-generated headlines. ## classification for our classifier experiments, we used the three sets of data @xcite we had previously compiled. specifically, for training we only used the 2015 set, while the 2016 and 2017 sets were used for testing. splitting the train and test data by the year of publication ensures that there is no overlap between the sets and there is some variability between the content of the headlines (for example, different topics/authors). therefore, we can be confident that the classifiers generalize to unknown examples. furthermore, for hyperparameter tuning, the 2015 data was randomly split into training and development sets on a 80/20 ratio. in total, for training there are 129, 610 headlines, for development there are 32, 402 and for testing there are 303, 965. ## conclusion this paper examined methods to detect headlines generated by a gpt-2 model. a dataset was created using headlines from abc and a survey conducted asking participants to distinguish between real and generated headlines. real headlines were identified as such by 55.9% of the participants, while generated ones were identified with a 47.8% rate. various models were trained, all of which were better at identifying generated headlines than humans. bert scored 85.7%, an improvement of around 35% over human accuracy. our work shows that whereas humans cannot differentiate between real and generated headlines, automatic detectors are much better at the task and therefore do have a place in the information consumption pipeline.