Do Stochastic Parrots have Feelings Too? Improving Neural Detection of Synthetic Text via Emotion Recognition
Authors: Alan Cowap
Yvette Graham
Jennifer Foster
Venue: EMNLP 2023 Findings (2023)
Keywords: synthetic text detection
neural text detection
emotion
affective deficit
TL;DR: We present a novel approach to synthetic text detection, using an emotionally-aware PLM, achieving state-of-the-art results detecting synthetic text in news and other domains.

Abstract: Recent developments in generative AI have shone a spotlight on high-performance synthetic text generation technologies. The now wide availability and ease of use of such models highlights the urgent need to provide equally powerful technologies capable of identifying synthetic text. With this in mind, we draw inspiration from psychological studies which suggest that people can be driven by emotion and encode emotion in the text they compose. We hypothesize that pretrained language models (PLMs) have an affective deficit because they lack such an emotional driver when generating text and consequently may generate synthetic text which has affective incoherence i.e. lacking the kind of emotional coherence present in human-authored text. We subsequently develop an emotionally aware detector by fine-tuning a PLM on emotion. Experiment results indicate that our emotionally-aware detector achieves improvements across a range of synthetic text generators, various sized models, datasets, and domains. Finally, we compare our emotionally-aware synthetic text detector to ChatGPT in the task of identification of its own output and show substantial gains, reinforcing the potential of emotion as a signal to identify synthetic text. Code, models, and datasets are available at https: //github.com/alanagiasi/emoPLMsynth

Do Stochastic Parrots have Feelings Too?
Improving Neural Detection of Synthetic Text via Emotion Recognition
Alan Cowap' and Yvette Graham² and Jennifer Foster'
School of Computing, Dublin City University SFI Centre for Research Training in Machine Learning at Dublin City University alan. . cowap2@mail. dcu.. ie, jenni jennifer. fosterêdcu. ie School of Computer Science and Statistics, Trinity College Dublin ygraham@tcd. ie
Abstract
can generate synthetic text at scale, across domains, and across languages.
Recent developments in generative Al have shone spollight on pigh-performance syn- thetic text generation technologies. The now wide availability and ease of use of such models highlights the urgent need to provide equally powerful technologies capable of identifying synthetic text. With this in mind, we draw inspiration from psychological studies which suggest that people can be driven by emotion and encode emotion in the text they compose. We hypothesize that pretrained language mod- els (PLMs) have an affective deficit because they lack such an emotional driver when gen- erating text and consequently may generate synthetic text which has affective incoherence ie. lacking the kind of emotional coherence present in human-authores text. We subse- quently develop an emotionally aware detec- tor by fine-tuning PLM on emotion. Ex- periment results indicate that our emotionally- aware detector achieves improvements across a range of synthetic text generators, various sized models, datasets, and domains. Finally, we compare our emotionally-
The increasing high quality of synthetic text from larger and larger PLMs brings with it an in- creasing risk of negative impact due to potential misuses. In this work, we focus on the task of synthetic text detection. Due the potentially profound consequences of global synthetic disin- formation we focus mainly, but not exclusively, on the detection synthetic text in the news domain.' Synthetic news has already been published on one highly reputable media website, only later to be withdrawn and apologies issued for the "breach of trust" (Crowley, 2023a,b).
Current approaches to synthetic text detection tend to focus on learning artefacts from the out- put distribution of PLMs (Gehrmann et al., 2019; Pillutla et al., 2021; Mitchell et al., 2023), e.g. increased perplexity caused by nucleus sampling (Zellers et al., 2019). However, PLM distributions are dependent on training data and numerous hy- perparameter choices including model architecture and sampling strategy. This gives rise to combina- torial explosion of possible distributions and makes the task of synthetic text detection very difficult. Furthermore, is not unexpected that performance decreases when classifying out-o--distiibution in- stances, and there is growing field of work inves- tigating this shortcoming (Yang et al., 2023).
synthetic
text detector to ChatGPT in the task of iden- tification of its own output and show substan- tial gains, reinforcing the potential of emotion as signal to identify synthetic text. Code, models, and datasets are available at https: //gi thub. com/ al aalaanniiii/eeeee heaa///mooPsttttt
1 Introduction
In this work, consider not only the PLM out- put distribution, but also the other side of the syn- thetic text detection coin human factors. We present a novel approach to the task of synthetic text detection which aims to exploit any difference between expression of emotion in human-authored and synthetic text. Neural word representations can have difficulty with emotion words, and PLM sam- pling strategies are stochastic rather than driven by emotion we use the term affective deficit refer
Modern PLMs can surpass human-level baselines across several tasks in general language understand- ing (Wang et al., 2018, 2019) and can produce syn- thetic text that can exceed human level quality, such synthetic propaganda thought to be more plausi- ble than human written propaganda (Zellers et al., 2019). PLMs have been used to generate disinfor- mation (Zellers et al., 2019; Brown et al., 2020), left- or right-biased news (Gupta et al., 2020), fake comments (Weiss, 2019), fake reviews (Adclani et al., 2019), and plagiarism (Gao et al., 2022) and
'The news domain is recognised as having high emotional content (Strapparava and Mihalcca, 2007: Bostan et al., 2020).

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these shortcomings. Thus, the resulting synthetic text can express emotion in an incoherent way, and we introduce the term affective incoherence to re- fer to this type of limitation. To be clear, we do not contend that synthetic text is devoid of emo- tion, rather that the emotional content of synthetic text may be affectively incoherent, and that this affective incoherence stems from the underlying affective deficit of the PLM.
models and datasets publicly available aid future rescarch.
Related Work
People are relatively poor at detecting synthetic text, and have been shown to score just above ran- dom chance (Gehrmann al., 2019; Uchendu al., 2021). Hybrid systems, such as GLTR (Gchrmann et al., 2019) for example, use automation to provide information to aid human classification, highlight- ing text sequence using colours to represent like- ness to the PLM output distribution such as GPT-2 (Radford et al., 2019). Gehrmann et al. (2019) reported an increase in detection accuracy of ap- proximately 18% (from 54% to 72%) using GLTR, while Uchendu et al. (2021) report an Fl score of 46% using GLTR with a heuristic based on an analysis of human text.
For the purpose of demonstration of the affec- tive deficit that we believe to be characteristic of text produced by PLMs, we provide the follow- ing simple example of human- versus machine- authored text with positive emotion words high- lighted in orange and negative emotion words in pink. One shows coherent emotion expected of numan-authhores text, while the other demonstrates affective incoherence (see footnote² to reveal which was http:///hmmmmmaaahhrrreeee text).
Both human and hybrid approaches involve hu- man decisions, which can be slow, expensive, sus- ceptible bias, and inconsistent. Automatic de- tection produces the best results for synthetic text detection. This usually involves training PLMs to detect other PLMs, but zero-shot detection meth- ods also exist, c.g. DetectGPT (Mitchell et al., 2023). Potentially the best supervised detector, BERT, can detect synthetic text from 19 different generators with mean Fl of 87.99% compared to 56.81 1 % for hybrid, and worst of all humans at 53.58% (Uchendu et al., 2021).
1. Roberts chuckled when asked if he was happy to on the other team now when Puig's 's name comes up. "Yeah, am happy," he said, smil- ing.
2. I'm really happy for him. Over the course of those three seasons, the 25-year-old has gone from rolling to poor to worse and old.
In this simple example, we have demonstrated one kind of affective incoherence present in synthetic text but we suspect that fine-tuning an emotionally- aware PLM could detect additional and more com- plex emotional patterns that might go undetected by humans. We hypothesise that the affective deficit of PLMs could result in synthetic text which is affectively incoherent, which could be useful in distinguishing it from human text.
Performance of SOTA detectors can however be inconsistent and unpredictable due to several factors specific to both the detector and generator, including: model size and architecture, training data and domain thereof, sampling strategy, hy- perparameter selection, and sentence length. As mentioned above, Uchendu et al. (2021) showed the best of these models (BERT) achieves mean Fl of 87.99% on 19 different synthetic text genera- tors. However, the mean score hides the wide range (3%) of Fl's, ranging from as low as 47.01 .111% to 99.97%, for distinct synthetic text generators. This volatility may be due in part to the detector simply learning artefacts of the generator distri- bution. Consequently, the task of synthetic text detection is somewhat of an arms race with detec- tors playing catch-up. forced learn ever-changing distributions due to the numerous factors that can potentially change those distributions.
We use a transfer learning (Pan and Yang, 2010) method to train an "emottonalll--waree" detector model. By fine-tuning a PLM first on emotion classification and then thetic text detection, we demonstrate improvements across range of synthetic text generators, vari- ous sized models, datasets and domains. Further- more, our emotionally-aware detector proves to be more accurate at distinguishing between human and ChatGPT text than (zero-shot) ChatGPT itself. Finally, we create two new datasets: NEWSsyuth, a dataset of 20k human and synthetic news articles, and ChatGPT100, testset of 100 human and Chat- GPT texts on a range of topics. We make all code,
target task of syn-
Existing approaches to synthetic text detection
²(1) is human-authored while (2) is synthetic text. Both are from the NEWStynth datasct (see $4.2).
Phttps://gtt thg.bbb... thub. www./ooo/aaaaeeeeaeeece http//aaaaaaa//e////aaaa LLyyth

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exploit properties of synthetic text. Synthetic text can be incoherent and degrade as the length of generated text increases (Holtzman et al., 2020), perplexity increases with increasing length unlike human text (Zellers et al., 2019), and PL.Ms are susceptible to sampling bias, induction bias, and exposure bias (Ranzato et al., 2016). For example, exposure bias can contribute to brittle text which is repetitive, incoherent, even containing hallucina- tions (Arora al., 2022). Synthetic text can have an inconsistent factual structure, such as mention- ing irrelevant entities (Zhong et al., 2020). Perhaps unsurprisingly, synthetic text detection is less diffi- cult with longer excerpts of generated text, for both humans and machines (Ippolito et al., 2020).
Past work in synthetic text detection has focused on the properties of synthetic text generators and is yet to take advantage of the factors that potentially influence human-authored text, such as the emo- tions humans express in the text they write. Our work exploits this PLM affective deficit to improve synthetic text detection.
3 Equipping PLMs with Emotional Intelligence
Our method is illustrated in Figure 1. The process works as follows:
1. PLMSYNTH: In the leftmost column of Figure 1, human articles and synthetic articles are used to fine-tune a PLM to discriminate between the two kinds of text. This is indicated by the blue nodes in the PLM illustration.
One aspect of writing that has not, up to now, been focus of synthetic text detection efforts is the expression of emotion. The problem of en- coding emotion was first identified in neural NLP with static embeddings such as word2vec (Mikolov et al., 2013; Wang et al., 2020a). Static word em- beddings have difficulty distinguishing antonymns from synonyms (Santus et al., 2014). This deficit is present in embeddings for words which repre- sent opposing emotions (e.g. joy-sadness) (Seyed- itabari and Zadrozny, 2017). Furthermore, words representing opposing emotions can have closer embeddings relative to words representing similar emotions (Agrawal et al., 20. There have been various approaches to address this affective deficit in embeddings, such as transfer learning from sen- timent analysis (Kratzwald et al., 2015 8), an addi- tional training phase using emotional lexicon and psychological model of emotions (Seyeditabari et al., 2019), and combining separately-learned se- mantic and sentiment embedding spaces (Wang et al., 2020a).
2. EMOPLM: In the middle column of Figure 1, second dataset annotated for emotions with Ekman's emotions (Ekman, 1992, 1999, 2016) is used fine-tune PLM on the task of emotion classification. This makes our model emotionally aware, as indi- cated by the red nodes in the PLM illustration. 3. EMOPLMSYNTH: The multi-class (6 head) classification layer from emoPLM is removed and replaced with a binary classification layer. The emotionally-aware PLM is then fine- tuned on the task of discriminating between human and synthetic articles. The PLM is still emotionally while also being able to detect synthetic text as indicated by the red and blue nodes respectively in the PLM.
We conduct experiments using various PLM sizes, architectures, datasets, and domains for syn- thetic text generation and detection.
Addressing potential affective deficits of PLMs is also the goal of work aiming to make dialogue systems more empathetic. For example Huang et al. (2018) force dialogue generation to express emo- tion based on the emotion detected in an utterance, while Rashkin et al. (2019) follow a similar ap- proach with a transformer architecture to make the system more empathetic. In contrast, Wang et al. (2020b) report that human text can display consistency in emotional content whereby similar emotions tend to occur adjacent to each other while dissimilar emotions seldom do."
News Domain Experiments
4.1 Generator and Detector Models
To generate synthetic text, we use the Grover causal PLM (GPT-2 architecture) pretrained on 32M news articles from the RealNews dataset (Zellers et al., 2019). We choose BERT (Devlin et al.. 2019) as our main detector model since it is freely available and performs well in several tasks including se- quence classification. A baseline BERT model (we call this BERTsy is fine-tuned the task of
FFor a comprehensive survey of sentiment control in syn- thetic text see (I (oranndi and Belz, 2023) and for studies of emotion in human writing, see (Brand, 1985, 1987, 1991;
Bohn-Gettler and Rapp. 2014; Knaller, 2017).

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PLMsynth
emoPLM
emoPLMsynth
Human
Synthetic
Joy
Sad
Disgust
Human
Synthetic
Classification Layer
Classification Layer
Classification Layer
HUMAN & SYNTHETIC TEXT
HUMAN SYNTHETIC TEXT
EMOTION
Figure 1: The emotionally-aware PLM (emoPLMsynth) takes advantage of its prior fine-tuning emotion to improve performance on the task synthetic text detection. In contrast, the standard PL.M fine-tuned only on synthetic text detection (PLMsynth) has training emotion. Our experiments show the emotionally- PLM (emoPLMLsynth) outperforms the standard PLM (PLMsynth) in multiple scenarios.
synthetic text detection, while our proposed model is the same BERT model, firstly fine-tuned on emo- tion classification (we call this intermediate model emoBERT) before further fine-tuning for synthetic text detection. This final proposed model referred to as synth
generated by a larger generator model, and against results reported for other models on this dataset.
We use the GoodNewsEEveyooe dataset (Bostan et al., 2020) to train eMoBERT. This dataset con- tains 5k news headlines, and was chosen since it is within the target domain (news) and language (En- glish) and is annotated with categorical emotions. The 15 emotion labels from GoodNewsEveeyooe were reduced to 11 emotions using the mapping schema of (Bostan and Klinger, 2018), and fur- ther reduced to 6 emotions based on the Plutchik Wheel of Emotion (Plutchik, 1980, 2001) see Table I and Figure in Appendix A resulting in 5k news headlines labelled with Ekman's ba- sic emotions, the most frequently used categorical emotion model in psychology literature (Ekman, 1992, 1999, 2016).
4.2 Datasets
We create and release NEWSsynth, a dataset con- taining 10k human and 10k synthetic news articles. 10k human-authore news articles were taken from the RealNews- Test dataset (Zellers et al., 2019) and used prompt Groverbase generate a cor- responding 10k synthetic articles. The prompt in- cludes the news article, headline, date, author, web domain etc. as described by Zellers et al. (2019). The dataset was split 10k-2k-8k for train, valida- tion, and test respectively, the same ratio used by Zellers et al. (2019) with 50:50 human:synthetic text in each split, Appendix B.3 for details. An investigation of length of human vs synthetic text is provided in Appendix E.
4.3 Training BERTsynth
We train BERTsynth, a BERT -Cased model fine-tuned for synthetic text detection (using the NEW NEWSsynth or RealNews-Test dataset). Input quence length was maintained at the BERT max- imum of 512 tokens (~ 384 words). Five train- ing runs were conducted. Each training run was 4 epochs the most possible within GPU time con- straints and similar to those of Zellers et al. (2019)
In a second experiment, we also use the full RealNews-Test dataset itself, which com-- articles used in
prises the same 10k human NEWSsynth and 10k synthetic articles generated by Grover The use of synthetic text generated by Grover instead of Groverbase allows com- parison of BERTsynth and emoBERTsynte on text

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GovrNonssvveryone
Ekman
4.5
Training emoBERTsynth
disgust
disgust (8%)
We train emoBERTsynth, an emoBERT model fine- tuned for synthetic text detection (using the NEWSsynth or RealNews-Test dataset). The best emoBERT model (checkpoint) from each of the 5 training runs had its emotion classification head (6 outputs) replaced with binary classification head (2 outputs) for human synthetic text clas- sification, see Figure 1. Each model was then fine- tuned on the synthetic text detection task using the exact same process and set of random seeds (for dataset shuffling) as the 5 best models described in $4.3, This allowed a direct comparison between the 5 BERT models (trained on syntheric text detection only) and the 5 MooBRRTsynt models (fine-tuned emotion classification followed by synthetic text detection).
fear
fear (8%)
sadness, guilt, shame
sadness (14%)
joy, trust, pride, love/like, posi-
happiness (17%)
tive anticipation/ootimisn
anger, annoyance, negative an- ticipation/pessmmism
anger (24%)
negative surprise, positive sur- prise
surprise (30%)
Table 1: Emotion Mapping Schema: GovdNewsEvery- one (15 emotions) to Ekman 6 basic emotions. % shows the emotion label distribution the dataset.
who used 5 epochs. For each training run, unique seed used for model initialization, and unique set of three seeds were used for the dataset shuffle one seed each for train, validation, and test splits. Furthermore, the HuggingFace library shuffles the training data between epochs. The reproducibility of the training and validation results using seeds was verified by conducting multiple runs of training and validation. Hyperparameter values are listed in Appendix C.
4.6 Results
The results in Figure and Table show the per- formance of BERTsynth and emoBERTsynth when fine-tuned on the NEWSsynth dataset. The results support the hypothesis that emotion can help de- tect synthetic text. emoBERTsynth outperforms BERTsynth in head-to-head for accuracy and Fl in all 5 runs.
Looking at precision and recall, emoBERTsynth outperforms BERTsynth in precision in all 5 runs, while the opposite the case for recall. is worth
4.4 Training emoBERT
We train emoBERT, a BERTbase basssedd model fine- tuned on the single label multiclass task of emo- tion classification using the GoodNewsEvveyonee dataset. Fine-tuning emoBERT followed similar process to fine-tuning BERTsynth described in $4.3, This time, there were 5k examples and fine-tuning was for 10 epochs.
0.500
Model
BERlsynth
0.25
emoBERBBynth
0.900
Classification accuracy is not the end goal for eMoBERT. Its purpose is to reduce the affective deficit of the PLM by modifying the representa- tions of words conveying emotions and improve performance in the task synthetic text detection by transfer learning. The mean Fl, for emoBERT is 39.4% on the Validation set more than double mean chance (16.7%) and within the range 31% to 98% reported for within-corpus emotion classifica- tion in UnifiedEmotion (Bostan and Klinger, 201 8). See Appendix D for more details.
0.855
0.800
0.75
Accuracy
Precision
Metric
Figure 2:
Test results for BERTsynth and emoBERTsynth emoBERTsynth is higher for Accuracy, Precision and F1, while BERTsynth is higher for Recall.
the NEWSsynth dataset.
comparing the relative difference in recall and precision between moBBERTsynth and BERTsynth models in Table 2. emoBERTsynth has difference between the mean recall and mean precision of 4.76 (89.04 84.28) while the difference for BERTsynth is more than double that at 10.81 (91.63 80.82).
*After each epoch the model (checkpoint) was run against the validation set for Accuracy, and the checkpoint and Ac- curacy results were saved (in addition to F1, Precision and Recall). The checkpoint with the highest Accuracy score was then run on the Test set.

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Precision
Recall
F1
Accuracy emoBs
Run
Bs
emoBs
Bs
emoBs
emoBs
Bs
1
80.30
81.25
92.40
92.20
85.92
86.38
84.86
85.46
82.26
84.30
90.90
89.83
86.37
89.77
85.65
86.55
78.01
82.88
92.40
88.20
84.60
85.45
83.18
84.99
77.44
85.84
94.85
88.20
85.27
87.00
83.61
86.83
86.09
87.14
87.58
86.75
86.83
86.95
86.71
86.98
Mean 80.82
84.28
91.63
89.04
85,80
87.11
84.80
86.16
(9.89)
(4.35)
(5.70)
(3.45)
(0.62)
(2.08)
(1.68)
(0.63)
+3.46
-2.59
+1.31
+1.36
Table 2: Comparison of BERTsynth (Bs) and emoBERTsynth (emoBs) against the NEWSsynth test set. (Variance is shown in brackets under the mean). emoBs outperforss Bs in head-to-head for all 5 runs in Accuracy, Fl, and Precision; while Bs outperforms emoBs head-to-head runs Recall.
Thus, we suggest our emotionally-aware PLM. emoBERTsynth, is better performing model than the standard PLM, BERTsynth, because it has better balance between precision and recall.
thetic news articles in RealNews- -est are shorter than 1024 tokens. Thus, they may not benefit from that extended input length and the model may in fact be somewhat reliant those later input tokens for prediction.
In Table we compare BERTsynth and emoBERTsynth on the RealNews-Test dataset. Recall that this dataset contains synthetic ar- ticles generated by Grover instead of the smaller Groverbase- We also compare against the FastText, GPT-2 and BERT detector models reported by Zellers et al. (2019) on this dataset. emoBERTsynth has the highest accuracy, outper- forming synth by 1.4%, BERTease by 9.03%, GPT-2base by 10.03%, and FastText by 12.43%. These results support the hypothesis that emotion can improve synthetic text detection.
Size
Model
Acc.
11M
FastText
63.80
124M
GPT-2base
66.20
BERT base
67.20
BERTsynth 74.83
emoBERTsynth
76.23
Table emoBERT synth outperforms other model chitectures and sizes detecting human and Grovermega (1.5B) synthetic text from the RealNews Detector model sizes include IM and 124M parame- ters and architectures include FastText, GPT- -oaseasee and BERTbase- The FastText, GPT-2base and BERTease re- sults reported by Zellers et al. (2019).
dataset.
There is a 7.63 point difference between our BERTsynth model and the BERT model reported by Zellers et al. (2019), despite both models be- ing BERTbase and fine-tuned on the same dataset and splits. However, there are differences in how the models were treated before this fine-tuning, and there may be some hyperparameter differences for fine-tuning. We described in $4.3 how fine-tune a randomly initialised BERT model to create BERTsynth. Zellers et al. (2019) reported their BERT models were domain adapted to News (by training on RealNews) at length of 1024 WordPiece tokens. It is possible that this addi- tional adaataato and extended input se- quence length actually harmed the performance of the BERT base model on the synthetic detection task. The performance of synthetic text detectors can improve with length (Ippolito et al., 2020) and the longer input sequence length could help in this re- gard. However, the vast majority of human and syn-
4.7 Analysis
In this section, we perform a further set of experi- ments aid in interpreting our main results.
4.7.1 Length of Human vs Synthetic articles
We investigate whether PLMs simply learn some- thing about the length of articles as a proxy for discrimination between human and synthetic text. An analysis of NEWSsynth articles (train and valida- tion splits) reveals no obvious correlation (Pearson 0.20) between the number of words in human article and the resulting synthetic article. 64% of human articles are longer than their corresponding synthetic article, while 34% synthetic articles

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are longer. Human articles are longer overall, but have slightly shorter sentences than synthetic text; and human articles have more sentences per article - which accounts for their longer mean length. Sim- ilar observations were made for RealNews-Test by Bhat and Parthasarathy (2020). See Table 10 and Figs. 5 to 8 in Appendix E. Overall, these results point neither to article length nor sentence length as reliable discriminator for synthetic text suggest- ing that detector models are not simply learning length as proxy for human vs synthetic text.
between BERTsynth and emoBERTsynth can be attributed emotion or to the process of fine-tuning on an arbitrary classification task with the GNE data;
AT involves fine-tuning with the Affec- tive dataset comprising 1.5k news head- lines in English annotated with respect to Ek- man's 6 emotions (Strapparava and Mihalcca, 2008);
GA is GNE and AT combined;
SST-2 involves fine-tuning on the task of sen- timent polarity classification using the SST-2 dataset of 68,221 movie reviews in English (Socher et al., 2013);
4.7.2
Size of fine-tuning splits
Split
Prec.
Recall
F1
Acc.
GAS is GNE, AT, and SST-2 combined; with SST-2 positive sentiment mapped to joy and negative sentiment mapped to sadness; S-GA involves first fine-tuning on sentiment using SST-2 and then fine-tuning emo- tion using GA. This experiment is inspired by Kratzwald et al. (2018) who report that emo- tion classification can be improved by transfer learning from sentiment analysis;
5-1-4k
78.39
79.85
78.89
78.58
Var.
(24.10)
(17.33)
(3.17)
(6.51)
10-2-8k
80.82
91.63 (S.70)
85,80
84,80
Var.
(9.89)
(0.62)
(1.68)
A
+2,43 +11.78
+6.91
+6.22
Table 4: BERTsynth metrics for different split sizes, using the NEWSsyuth dataset averaged over 5 runs (with variance shown in brackets).
GAS++ GAS but mapped to positive and negative sentiment."
The ByRTsynth fine-tuning regime ($4.3) was re- peated using all (20k) and half (10k) of NEWS NEWSsynth. In all 5 runs, the BERTsynth model trained on the larger 20k dataset performed better than the equiv- alent model trained on the smaller 10k dataset- see Table 4. There was a modest improvement in precision (+2.43%) with a much larger increase in recall (+11.78%). The results suggest that recall is most sensitive to the size of the training set. This is perhaps because the PLM is already trained on human text during pretraining but synthetic text (exposure bias), so more exposure to synthetic text increases the model's s ability to detect synthetic text correctly with fewer false negatives.
The results (Table 5) reveal that the best- performing emoBERT synth models are those fine- tuned using GNE or using GNE and Affective combined (GA). The latter achieves the highest ac- curacy and the former the highest F1. We attribute the relatively poor performance of Affective on its own to its small size, comprising only 1.5k head- lines (split 625 125 for training and dev splits respectively) compared 5k for GNE and 68k for SST-2.
Table also shows that fine-tuning on GNE outperforms fine-tuning with randomised labels (GNE,). The 1.1 point drop in accuracy of GNE, compared to GNE suggests that the emotion clas- sification task does play role in the improved performance of emoBERTsynth versus syRhh. The results in Table 5 suggest that fine-tuning on sentiment is not particularly helpful. The poor performance of GAS could be due to the crude mapping of negative sentiment to sadness (because
4.7.3 Alternative forms of emoBERT
What is the effect of using different emotion datasets to fine-tune our emotionally aware PLMs the downstream task of synthetic text detection? We conduct experiments on synth by fine- tuning eight alternative emoBERT models:
GNE involves fine-tuning using the Good- NewsEveryone dataset ($4.2) as in the main experiments;
'Happiness was mapped to positive sentiment; sadness, fear, anger and disgust were mapped to negative sentiment; surprise was mapped to sentiment using a DistilBERT (base- uncased) (Sanh et al., 2020) sentiment classifier fine-tuned on the SST-2 dataset and available on HuggingFace. https: / //huggingface co/ co/distilbber wewbbbettasee-eeee bas--baseueee bacssed 14.05% of 'surprise" mapped to positive, while the remaining 85.95% mapped to negative sentiment.
GNE, involves fine-tuning with a version of GNE with randomised labels. We do this to examine the extent to which the difference

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Prec.
Rec.
F1
Acc.
Prec.
Rec.
F1
Acc.
BLOOMsynth
81.90
85.95
83.79
83,40
GAS
81.95
85.58
83.72
83.36
(4.76)
(12.22)
(1:23)
(0.93
S-GA
82.60
87.80
85.12
84.65
emoBL.O0Msynth
85.98
88.02
86.90
86.75
GAS+
82.41
88.30
85.25
84.73
(5.72)
(9.96)
(0.27)
AT
85.52
83.88
84.69
84.84
A
+4.08
+2.07
+3.11
+3.35
SST-2
82.85
88.38
85.52
85.04
Table 6: Comparison of BLO0Msynth and 00Msynth against the NEWSsynth test set averaged over 5 runs (with variance in brackets). AmoBLO0Msym outperforms BLOOMsynth Accuracy, F1, Recall, and Precision.
GNEr
82.44
89.93
86.02
85.39
GNE 83.84
90.40
87.00
86.49
GA
85.34
88.18
86.73
86.51
Table 5: Ablation experiments, using different emo- tion datasets for fine-tuning emoBERT, comparing emoBERTsynth (eBs) detectors on the task of synthetic text detection on the NEWSsyuth dataset. GNE is the GoodNoewSvveyyone dataset which is used in the main experiments. GNE, is GNE with randomised labels. AT is AffectiveText. GA is GNE and AT combined. SST-2 is the SST-2 sentiment dataset. GAS is the combined GNE, AT, and SST-2 datasets. S-GA is first tunettuned sentiment using the SST-2 dataset, and then fine- tuned on emotion using the GNE and AT datasets, and finally fine-tuned on synthetic text detection. GAS+- is GAS but mapped to positive and negative sentiment.
aligned Large Language Model (LLM) which has undergone a second training or "alignment" phase using Reinforcement Learning from Human Feed- back on top of an underlying LLM (GPT 3.5 in our case) (OpenAI, 2022; Ouyang et al., 2022). We cre- ate custom dataset comprising human articles and ChatGPT synthetic text from multiple non-news domains, and use compare our BERTsynth and emoBERTsynth models against ChatGPT (in zero- shot setting) on the task of detecting ChatGPT's own synthetic text.?
it could be any 1 of 5 Ekman emotions), which results a large dataset imbalance across emo- tion labels. When we go in the opposite direction and mapped the emotion labels to sentiment labels (GAS+)), the results improved. Overall, however, the results suggest that mixing emotion and sen- timent datasets not good idea (particularly if they are disproportionate in size and imbalanced), and that sentiment alone not sufficient.
ChatGPT100 We create and release Chat- GPT100 dataset comprising human articles and synthetic articles generated by ChatGPT, Following Clark et al. (2021) who collected 50 human articles and generated 50 articles using GPT2 and GPT3, we also collect 50 human articles, and we then use ChatGPT to generate 50 synthetic ones. The hu- man written articles are from 5 different domains: Science, Entertainment, Sport, Business, and Phi- losophy. We used reputable websites for the human text which was gathered manually, Table 8 in Appendix B.3. The synthetic text was generated by providing ChatGPT with a prompt such as "In less than 400 words, tell me about moral philoso- phy." where human text on the same topic, moral philosophy in this case, had already been found online. The data generated by ChatGPT is seman- tically correct and was checked manually. Subject areas in which the authors are knowledgeable were chosen that the correctness of the synthetic text could be checked. To be comparable with the de- tectors presented in our earlier experiments, the articles were limited to a maximum of 384 words ( 512 tokens) and truncated at a natural sentence boundary. The two articles were then made to be approximately the same length.
4.7.4 A larger detector model
We next investigate what happens when we use a PLM larger than BERT to detect synthetic text. Using the same experimental setup described in $4, we substituted BLOOM (Scao et al., 2023) in place of BERT for the synthetic text detector. BLOOM is an open-seience causal PLM alterna- tive GPT-3 (Brown al., 2020). We use the BLOOM 560M size model. The results in Ta- ble show that the emotionally-aware BLOOM PLM (emoBL00Msynth) outperforms the standard BLOOM (BL00Msynth) in all metrics.
5 ChatGPT Experiments
All experiments far have involved PLMs pre- trained with the self-supervised objective of predict- ing the next token or a masked token. We conduct a final experiment with ChatGPT, a more human-
"We use ChatGPT-3.5 (Mar-14-202) 14-2023 version) between dates 16-Mar-2023 and 24-Mar-2023.

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Model
Prec.
Rec.
F1
Acc.
of synthetic text. An emotionally-aware PLM fine- tuned on emotion classification and subsequently trained on synthetic text detection (emoPLMsynthe outperformed a model with identical fine-tuning on synthetic text detection, but without emotion train- ing, (PLMsynth). The results hold across differ- ent synthetic text generators, model sizes, datasets and domains. This work specifically demonstrates the benefits of considering emotion in the task of detecting synthetic text, contributes two new datasets (NEWSsynrh and ChatGPT100) and, more generally, it hints at the potential benefits of consid- ering human factors in NLP and Machine Learning. Is it possible that some other proxy for synthetic text is at play? We ruled out some potential prox- ies related to article length in $4.7.1. In ablation studies in $4.7.3, we showed that the emotion la- bels result in an improvement in performance com- pared to randomized labels for the same emotion dataset. Other potential proxies are nonsensical sen- tences, repetitive text, etc. However, we account for these by comparing our emotionally-awan PLMs (emoPLMsynth) against standard PLMs fine-tuned on synthetic text detection only (PLMsynth). Thus, any advantage or disadvantage of sentences with- out meaning (or any other factor) is also available to the sit--cootonnlll-aaaan model against which we compare our emotionally-awann model.
ChatGPT 75.00
30.00
42.86
60.00
BERTsynth
60.24
100.00
75.19
67.00
emoBERTsyytth
67.57
100.00
80.65
76.00
Table 7: Our emotionally aware PI.M (emoBERTsynth) outperforms ChatGPT and ByRhsynth at detecting syn- thetic text in the ChaiGPTION dataset. Note that Chat- GPT performing the task zero-shot.
Detection task Each article was appended to the following prompt ChatGPT: "Was the follow- ing written by human computer, choose hu- man or computer only?" Having tested ChatGPT, we then tested our BERTsynth and synth models (the models fine-tuned on RealNews-Test from Table 3).
The results are shown in Table 7. The first thing to note is that no model performs par- ticularly well. ChatGPT tends to misclassify its own synthetic text as human (hence the low recall score of 30%).* 8 BERTsynth and emoBERTsynth on the other hand, tend to classify text as machine- written and they both obtain 100% recall. We pre- viously saw ($4.7.2) that recall is most sensitive fine-tuning set size. The emoBERTsynth and emoBERTsynth models have been exposed to syn- thetic text during fine-tuning, whereas ChatGPT is performing the task zero-shot. This could ex- plain some of the difference in recall between the ChatGPT and the two fine-tuned models.
Results
Future work will investigate further the affective profile (i.e. emotional content and characteristics) of human and synthetic text; and attempt deter- mine if there are measurable differences which may prove useful the task of synthetic text detection.
Finally, as with our experiments with Grover- generated text, emoBERT synth outperforms BERTsynth on all metrics. The dataset is small we must be careful not to conclude too much from this result, but does suggest that fine-tuning on emotion could be beneficial when detecting synthetic text from LLMs and more sophisticated generators, in non-news domains. This is in line with the results of our carlier experiments using variously size PLMs (such as Grover, BERT, BLOOM), used as generators and detectors in the news domain, and shows the potential for our approach with different generator models and in different domains.
Limitations
The datasets used in this work (synthetic text datasets, emotion datasets, and sentiment dataset) are English language and model performance in other languages may vary. We primarily focus on the news domain and, while performance in other domains may vary (Merchant al., 2020), we in- clude experiments in several non-news domains ($5).
The emotion datasets are imbalanced across emo- tion labels which can impact overall performance, and we conducted ablation experiments to find the best combination of emotion and sentiment datasets ($4.7.3). GoodNewsEEveyonne's 15 emo- tions were mapped to Ekman's 6 emotions (Ekman, 1992, 1999, 2016), factoring in Plutchik's wheel of emotion (Plutchik, 1980, 2001), but there no
6 Conclusion
We conducted experiments investigating the role that emotion recognition can play in the detection
*ChatGPT's responses suggest it may use fact-checking as a proxy during synthetic text detection.

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firm agreement in the literature as to which is the 'correct' or "best' emotion model (Ekman, 2016). The emotion models used in this work are the two most popular in the literature.
include the caveat below with the released datasets (NEWSsynth and ChatGPT100) and the released language models (emoPLMsynth, PLMsynth):
The maximum input sequence length of BERT is 512 tokens and articles longer than this are trun- cated, which may negatively affect performance the synthetic text detection task (Ippolito et al., 2020). However, we also saw that increasing the input sequence length may actually contribute to poorer performance (54.6).
Care must be taken when using these language models (emoPLMsynth and PLMsynth), and datasets (NEWSsynth and CharGPT100) as they may produce contain ethically problematic content. Data scraped from the web may con- tain content which is ethically problem- atic such as adult content, bias, toxic- ity etc. and web-scraped data is used in the pre-trained language models such as BERT, BLOOM and Grover. PLMsynth and emoPLMsynth are based on BERT or BLOOM PLMs, while NEWSsynth generated by Grover. Consequently, emoPLMsyme and PLMsynth could pro- duce text which is ethically problematic, while NEWSsynth may contain ethically problematic content. As a result, any use of the language models CemoPLMsynthe PLMsynth) or the datasets (NEWSsynth or ChaiGPTI00) should employ appro- priate checks and test regimes to handle potential harmful content.
Ethical Considerations
We release multiple PLMs (emoBERTsynth, BERTsynth, emoBLo0Msynth and BLOOMsynth) which we refer to generically emoPLMsynth and PLMsynth. emoPLMsynth and PLMsynth are BERT or BLOOM models with versions fine-tuned on NEWSsynth or the RealNews- (Zellers al., 2019) datascts; emoPLMsynth is also fine-tuned on combinations of the GoodNewsEvveyone (Bostan et al., 2020), AffectiveText (Strapparava and Mi- halcea, 2008), and SST-2 (Socher et al., 2013) datasets.
We release ChaiGPT100, dataset comprising 100 English language articles in various non-news domains. 50 articles are human written, and 50 articles are generated by ChatGPT. The 100 articles have all been manually curated and do not contain toxic content. Furthermore, ChatGPT has content filter which flags potentially harmful content.
The intended use of the emPPLMsytth and PLMsynth models, and the NEWSsynth and Chat- GPT100 datasets, for research purposes and ben- eficial downstream tasks such as identifying syn- thetic text perhaps in online news, reviews, com- ments, plagiarism etc. Online platforms could use this identification to decide whether or not to pub- lish such content, where surface it via rec- ommender algorithms etc. This could help protect public confidence in online discourse.
We release, NEWSsynth, a dataset comprising 40k English language articles in the news domain. 20k news articles are human (from RealNews- Test) and 20k generated by Grover. Publishing synthetic text is a risk, but NEWSsynth is clearly labelled as containing synthetic text. This is a similar pre- caution to synthetic text from Grover which has already been published and is publicly available (Zellers al., 2019).
Energy usage was reduced by training on smaller models and for a relatively small number of epochs where possible, by using random search rather than an exhaustive grid search, and by using freely avail- able managed compute resources where possible.
The potential harms, such as toxic synthetic text (Gehman et al., 2020), of PLMs pretrained on web- crawled data has been the subject of much discus- sion (Bender et al., 2021). Since emoPLMsynth and PLMsynth (and Grover) were pretrained and/or fine- tuned on web-crawled data there is possibility they could produce inappropriate synthetic text and this includes the NEWSsynth dataset. We recog- nise these potential harms and to mitigate them "We include 20k artieles in addition to the 20k used in this
A Acknowledgements
This publication has emanated from research con- ducted with the financial support of Science Foun- dation Ireland under Grant number 18/CRT/6183. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
work

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We thank Rowan Zellers for his permission use 20k human articles from RealNews-Test (Zellers et al., 2019) in the NEWSsynth dataset which we release with this paper.
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We thank the reviewers for their valuable feed- back which helped improve the paper.
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on Empirical Methods in Natural Language Process- ing (EMNLP), pages 2461-2470, Online. Association for Computational Linguistics.
hardest (so chose top-p=0.95 as is in the mid- dle of this difficult detection zone). Source code, installation, and generation instructions for Grover can be found on the Grover github. 10
A Plutchik Wheel of Emotion
Model: GROVERBASE
The Plutchik Wheel of Emotion (Plutchik, 1980, 2001) is shown in Figure 3 and is the most monly used dimensional model in psychology liter-
Model parameters: 124M
Top-p 0.95
ature.
Metadata: Full contextual metadata (from
RealNews-Test dataset)
Time to generate 20k synthetic articles is ap- proximately 90 hours on single GPU (Tesla K40, or RTX2080ti) with 30GB RAM.
optimism
love
serenity
B.2 Metrics
Interest
Joy
agginsinaaans
Irist
submiissionn
Accuracy, Precision, Recall, F1, (and Fl,, for emo- tion classification) were calculated using scikit- learn.
acstasy
arroyancc
Angar
Eerror
fear
aporeeension
B.3 Datasets
loathing
griel
NEWSsynth We release NEWSsynth a dataset comprising 40k English language human and syn- thetic news articles. The experiments in this paper use the first 20k of these articles, an additional 20k articles are provided in the dataset. The human articles are taken from the RealNews-Tess dataset (Zellers al., 2019) they have not been seen by Grover which generated the synthetic news articles as described $4.3 and earlier in this Ap- pendix.
disqust
surprise
boredom
sadress
distract
vensiveness
despprovol
Figure 3: Plutchik Wheel of Emotion. The middle ring includes Ekman's emotions plus 'trust' & "antic- ipation'. Similar emotions lie adjacent spokes c.g. anger-disgust, while opposing emotions are placed on opposing spokes e.g. joy-sadness.
Regarding the emotional content and journalis- tic content of news articles in NEWSsynth: Previ- ous authors have specifically chosen the news do- main because of its high emotional content (Strap- parava and Mihalcea, 2007; Bostan al., 2020). is long established that different emotions lead to different actions (Spielberger, 1972) including what we write (Brand, 1985). Emotion can be ex- ploited, for example "engagement based ranking" tends to favour content that evokes anger (Hau- gen, 2021). While some journalistic reporting is objective, opinion editorials (op-ed) are opinions pushing agenda and, for example, tabloids tend
B Reproducibility
All code. models, and datasets (including NEWSsynth and ChatGPT100) avail- able https://githubb.co alanagiasi/ emoPLMsynth.
B.1 Parameters used for generating synthetic text with Grover
GroverBASE was used for generating synthetic text news articles in NEWSsynth. Full contextual meta- data was used, in addition to top-p value of 0.95 because both make discrimination more dif- ficult. According Zellers et al. (2019) contex- tual data decreased perplexity by 0.9 points for GroveTBASE, and top-p value in the range 0.92 to 0.98 is a Goldilocks zone where discrimination is
/nttps:///ithhbb http://tttbbbmmm//o//////g/errrr "sciikitleaam describes the metrics: //scikit-learn.orl evaluation. html#tcommon cases c inee-aaueee As noted in $4.4 when fine-tuning emoBERT on emo- tions: micro averaging over a single-label multi-class evaluation means that Accuracy, Precision. Recall and Fl all have the same value. https://scikit-i hwwstaable/ooule///odoeo weteeeeeeeeeeeeelllaaeeeeeeeeeee www.leetian.nn..e html# multiclass andecllsss-----eeeeeee mondmmullllbblll www.aaaaeeellllaiiiiaaaa classification
https:

---

specifically exploit emotion. The 10k news ar- tieles the NEWSsynth training split, for exam- ple, come from 150 online sources which also in- clude: movie reviews and entertainment such as rollingstone.com com, hollywoodlife.com com, bollywood- hungama.com and mashable.com and tabloids such as thedailymail.co.. lystar.net etc. which cover many types of news including journalism, op-eds, reviews, opinions etc. In short, NEWSsyuth is not limited non- emotional objective fact reporting, it contains broad spectrum of journalistic styles and content.
Domain
Count
britannica.com
investopedia.com
6
plato.stanford.edu
6
fandom.com
2
forbes.com
2
dailystar.co.uk thedai-
olympics.com
allmusic.com
arpansa.gov.au
arsenal.com
atptour.com
bbc.com
ChatGPT100 We release ChatGPTI00, dataset comprising 100 English language articles in var- ious non-news domains (Science, Entertainment (Music, Movies), Sport, Business, and Philosophy). 50 articles are human written, and 50 articles are generated by ChatGPT. The 100 articles have all been manually curated and do not contain toxic content. Furthermore, ChatGPT has content filter which flags potentially harmful content.
bhf.org.ub .u..o...kkk
bleacherreport.com
cambridge.
canarahsbclife.ccom
empireonline.com
gaa.ie
hotpress.com
kaspersky.com
laureus.com
The 50 human articles contained in CharGPT100 were gathered between 16-24 March 2023 from the domains shown Table The synthetic articles contained CharGPT100 were generated using ChatGPT 3.5 (March 14 2023 version) on dates between 16-24 March 2023.
mayfieldclinic.com
oah.org
oceanservice noa...ov
open.Jib.umn.cdu
phys.org
science.nasa.go
sixnationsrugby.com
RealNews and RealNews-Test These datasets were released with Grover and are described there detail (Zellers et al., 2019).
slf.rocks
u2.com
Total:
50
Emotion and Sentiment Datasets GoodNew- sEveryone is described in detail (Bostan et al.. 2020) with modifications made to the dataset for this work described in $4.2. The distribution of emotion intensity shown in Table showing al- most all are 'medium' while examples have no emotion. AffectiveTexxt was released as part of Se- mEval 2008 and is described in detail (Strapparava and Mihalcea, 2008), while the SST-2 sentiment dataset is described in detail (Socher et al., 2013).
Table 8: Domains used for human text in CharGPT100 dataset released with this paper. Articles were gathered between 16-24 March 2023.
loaded from HuggingFace.
emoBERT, BERTsynth, and emoBERTsynth were all trained using freely available Google Colab with a single GPU (Tesla K80 or Tesla T4) with no guar- antee on available RAM 14 or an NVIDIA GefFrce RTX3090 GPU with 24GB RAM.
C Hyperparameters used for Fine-tuning
All models were trained and evaluated for runs using different seeds for each of the runs. The seeds used are listed below.
The hyperparameters used for PLM fine-tuning are listed below. If not specifically listed, the hyper- parameter value used was the default using Hug- gingFace Transformer libraries. The BART-ASE- cased and BERTLARGE-Caser models were down-
htw.:i/gaaa...ccbbbbbbbbeeee co/bert--ase-cased,
https://hugginn face co/ber ti-aarge-ccsse
"GPU time was typically limited to hours or less which limited the number of epochs the PLMs could be trained to 4.
"hhttp:://uugingtee wtt.://accco//ttaafforrrrr

---

Emotion Intensity
Count
Data seeds [17, 38, 5, 91, 59] #n, n-6, n+6 for train-val-test seeds respectively
None
2
Metric for best model: Fl,
low
3
medium
4991
Training Validation time: 11mins (for epochs)
high
4
Total:
5000
Inference time: 22s (for 2k examples)
Table 9: Distribution of emotion intensity in GoodNew- sEveryone.
emoBERT Emotion Classification Results
The mean Fl,, for emoBERT is 39.4% on the Valida- tion set more than double mean chance (16.7%) and within the range 31 to 98% (mean = 62.6%) reported for within-corpus emotion classification in UnifiedEmotion (Bostan and Klinger, 2018). Good- NewsEveryone does not report news headline emo- tion classification (Bostan et al., 2020).
C.1
syRTh, emoBERTsynth
Model: BERTBAsE-cased LERELARGE-Cased
Model parameters: 110M 355M
Input sequence length: 512 tokens padded
Train-Val-Test split size: 10k, 2k, 8k
Epochs: 4
Heatmap of Emotion Classification (Combined over 5 models on Validation)
Batch Size: 7. 15
Batch Gradient Accumulation:
Hppiness
7
Warmup steps: 500
Sadness
133
11
Weight decay: 0.01
True Emotion
Seeds [179, 50, 124, 253, 86]. 5 seeds
seed per training run.
Anger
Data seeds = [17, 38, 5, 91, 59] #n, n-6, n+6 for train-val-test seeds respectively
100
Disgust
Surprise
122
50
219
Metric for best model: Accuracy
Hppiness Sadness
Fear
Anger
Disgust
Surprise
Training + Validation time: 150mins (for epochs)
Predicted Emotion
Inference time: 10mins (for 8k examples)
Figure 4: Combined Confusion Matrix for Emotion Classification GoodNewsEvveyone Validation set.
C.2 emoBERT
Figure 4 depicts the combined results of the best performing model (in Validation) from the 10 epochs, in each of the 5 training runs. The im- balance across emotion labels (shown in the first column of Table 1) is reflected in performance in Figure 4. Anger and Surprise the two emotions best classified and best represented in the dataset 24% and 30% respectively; while Fear and Disgust are the two emotions most poorly classified and least represented in the dataset at 8% each. The 4 emotions Happiness, Sadness, Anger, and Surprise are classified correctly more often than as any of the other emotions. Fear and Disgust are most likely to be misclassified Anger.
Model: BERTBASE- BERTLARGE
Model parameters: 110M 3555M
Input sequence length: 512 tokens padded
Train-Val- split size: 2.5k, 0.5k, 2k for
GNE
Epochs: 10
Batch Size:
Batch Gradient Accumulation:
Warmup steps: 500
Weight decay: 0.01
Seeds [179, 50, 124, 253, 86]. seeds seed per training run.
We see correlation between class size and per- formance on that class those classes with more examples performed better than those with fewer examples. Outright performance not the end
"Experiments showed was the largest batch size possible given 5122 input sequence length with the RAM available, and is similar to that reported by Google on BERT github: https://get /ttps:// com/ cow/googleereeeerreeeeeoeoeoee reseerrc/berrrrrrreeee

---

goal for emoBERT. The purpose of emoBERT is reduce the affective deficit of the PLM by modify- ing the word representations words representing emotions and to improve performance in the task synthetic text detection by transfer learning.
Length of human vs synthetic articles E
in NEWSsynth
700
Figures 5 illustrate the relative lengths human and synthetic articles and sentences in NEWSsyuth (train and validation splits) as described in $4.7.1 and shown in Table 10.
100
800
Nuniordsatechinee
Numneenteneeskumaaaauhhnne
800
Figure 7: Number of sentences per article for human (green) and synthetic (orange) text in NEWSsynth.
400
500
1000
1500
2000
2500
3000
3500
4000
NumWVerdsHuman
Figure 5: Scatter plot of number of words per article pair of synthetic text vs. human text in NEWSsynth (Pearson r 0.20).
Count
10000
8000
MachinetkmannCout
NumiWordsHammMMehhinee
Figure 6: Number of words per article for human (green) and synthetic (orange) text in NEWSsynth.
2000
NumtWordsPereeeencee
Figure 8: Number of words per sentence for human (green) and synthetic (orange) text NEWSsynth.

---

Source
Words Per Article
Sentences Per Article
Words Per Sentence
0
Human
594.56 417.98
503.07
27.05
25.23
21.98
15.98
Synthetic
162.09
18.34
8.64
22.79
16.60
Figure 6
Figure 7
Figure 8
Table 10: Comparison of Human and synthetic text in the NEWSsyuth dataset showing the mean (x)) and standard deviation (o)
Word Per Article, Sentences Article, and Words Per Sentence. Human articles are longer overall, but have slightly shorter sentences than synthetic text; and Human articles have more Sentences Per Article which accounts for their longer mean length.


Reviews:
[{"review_id": "drd19i6EJA", "reviewer": "Reviewer_8S6u", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the feedback and suggestions.\n\n**Reasons to Reject:**\nWe will clarify that the affective deficit is in the LLM, and this can result in affective incoherence in synthetic text generated by the LLM. We do not suggest that synthetic text contains no emotion, but that incongruencies can occur in text where emotion is being expressed. We will make this point clearer in the paper.\nWe acknowledge that some other proxy could be at play, however in Table 5 the GNE vs GNEr results suggest emotion is playing a role because replacing the emotion detection task (GNE) with random labels (GNEr) causes a drop in accuracy.\n\nThank you for the suggestion, we will include some examples where emoLLMsynth detected synthetic text that BERTsynth didn’t and there is affective coherence.\n* E.g. 1 \"Marshal Yanda looked \"**happy** and refreshed\" before Wednesday's minicamp practice but made some notes. After a **disappointing** season, the 37-year-old Yanda might be \"just getting **better and better and better**\" physically, veteran linebacker Whitney Mercilus said.”\n* E.g. 2 \"Bethenny Frankel, who hosted her daughter, Brynn, on ‘Bethenny Ever After,’ opened up about how she found balance when dealing with **two recent heartbreaks**. Bethenny Frankel, 39, has had a **rocky relationship** with her child, Bryn, 11, but all the drama behind the scenes has her engaged in **peace and happiness**.”\n\nThank you for the suggestion, we will move Appendix E on sentence length etc. to §4.2\n\nThe ChatGPT experiments were included to check performance of our emotionally-aware models in non-news domains, and test against a different synthetic text generator and detector (ChatGPT). In addition, with this paper we release the ChatGPT100 dataset which may be useful to other researchers.\n\n**Missing Refs:**\nThank you for pointing this out, we will include the Ekman references where Ekman is first cited on p3.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is generally well-written and legible. Although the presented study has some weaknesses, it was accurately conducted and reported. The idea of exploiting emotional qualities to identify human-generated content is novel and exciting and addresses a current issue. \n\nThe drawn conclusion does not entirely convince me, but I believe this study provides some very interesting insights and is a valuable contribution to the overall discussion. \nThe released data sets are an additional contribution of this submission and might be relevant for future research.", "reasons_to_reject": "The authors showcase the affective deficit in Sec. 3. However, the example rather shows a content-related incongruency (that would not happen to a human, but occasionally for machine-generated text) than a deficit of emotion words. \nThis example, in fact, underpins my main reservation: There are model improvements, but I am not entirely convinced that they can be traced to the emotional awareness of the model or if there might be any proxy. The authors address this issue and show the different characteristics of human and synthetic text in the appendix. There are some considerable differences in the data distribution, which could already be mentioned in Sec. 4.2. It can also be assumed that there are content differences.\nAlthough the possibility of a proxy is discussed in the paper, it still needs to be solved and definitely remains for future research. \nThe results of the ChatGPT study are a nice addition but contribute little to the overall message of the paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors hypothesize that synthesized text, unlike human-generated text, lacks the implicit expression of emotion. Thus, they aim to exploit that quality to improve synthetic text classifiers. \nThe basic concept comprises three experimental steps: first, training a classifier on human and synthetic data (LLMsynth); second, training a classifier on emotion data (emoLLM); and third, an \"emotionally-aware\" classifier where the classification layer is replaced to be binary and trained on human vs synthetic data. \nThe training process and data acquisition are well-described. The composed data sets are part of the contribution. The trained models are compared and evaluated in different conditions, resulting in the overall conclusion that the emotionally-aware classifier indeed outperforms compared systems. \n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "I would prefer to refer to specific sections with \"Sec. 5\" instead of \"§5\". That might be a personal choice, but I feel like that's uncommon. \n"}, "extra_text": {"Missing_References": "(Minor) Ekman's 6 basic emotions are referenced many times throughout the paper, but the source is only referenced at the very end (limitations section). It would make sense to have that reference earlier (p. 3). "}}, {"review_id": "Qv42yCS6IJ", "reviewer": "Reviewer_TNw9", "summary": "", "questions": "What is the prompt used for data generation?\nAre data generated semantically correct?\nLLMs models you sed are very small and sentences without meaning are often generated. Can your results be influenced by this issue?\nWhy do you not evaluate other simplest strategies for text classification as baseline?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the feedback and suggestions.\n\n**Reasons to Reject:**\n>“No replicability of results”\n\nWe are not sure what you mean by this? All of the experiments are reproducible. All hyperparameters etc. are included in Appendices B and C. All code, datasets, and models will be available publicly (Github, HuggingFace) outside of the anonymity period.\n\n>“Only one dataset has been used”\n\nWe used several datasets: RealNews-Test, NEWSsynth, and ChatGPT100 were used for the synthetic text detection task (we release the NEWSsynth, and ChatGPT100 with this paper). GoodNewsEveryone, AffectiveText, and SST-2 were used for the emotion and sentiment tasks.\n\n>“No statistical validation of results are performed”\n\nWe will include the statistical validation of results. However, Table 2 for example is averaged over 5 runs with all the results provided, so we are not sure it is necessary in such a case.\n\n>“More classic machine learning models should be used as baselines.”\n\nWe take your point, however in this paper we use LLMs as they provide state-of-the-art results and outperform traditional ML models (Uchendu et al, 2021). Hence, the focus of this paper is transfer learning (of emotion) in a state of the art supervised detection method - which is fine-tuning an LLM. We want to use transfer learning to look directly at the impact of emotion in a direct head to head i.e. emoLLMsynth vs LLMsynth, so LLMsynth is our baseline.\n\n\n**Questions For The Authors:**\n>“What is the prompt used for data generation? Are data generated semantically correct?“\n\nAn example prompt for text generation is given on lines 537-538 “In less than 400 words, tell me about moral philosophy.” An example prompt for the text classification task is given on lines 548-550 “Was the following written by a human or a computer, choose human or computer only?”\nThe data generated by ChatGPT is semantically correct and was checked manually, the subject areas were chosen as the authors are knowledgeable in those areas and could check the correctness of the synthetic text. We will include this description in the paper.\nFor Grover-generated synthetic text we use human written news articles as a prompt including the news article, headline, date, author, web domain etc. as described by Zellers et al (2019).\n\n>“LLMs models you [u]sed are very small and sentences without meaning are often generated. Can your results be influenced by this issue?”\n\nSome models are smaller, but we also use BERTlarge, BLOOM and ChatGPT for detection, and for generation we use Grover-base, Grover-mega, and ChatGPT.\nIt is harder to detect larger LLMs, detection accuracy goes down for larger LLMs per results in Table 2 (Grover-base) and Table 3 (Grover-mega). For this reason, we use larger generator models and larger detector models and we look at trends across all these model sizes.\n\nSentences without meaning could be a proxy which the LLM discriminator uses for classification, however, we account for this by comparing our emotionally-aware LLMs (emoLLMsynth) against standard LLMs fine-tuned on synthetic text detection only (LLMsynth). Thus, any advantage or disadvantage of sentences without meaning (or any other proxy) is also available to the non emotionally-aware model against which we compare our emotionally-aware model.\n\n>“Why do you not evaluate other simplest strategies for text classification as baseline?”\n\nAs mentioned above, we focus on transfer learning using state of the art supervised detection i.e. fine-tuning a LLM, and our baseline is the fine-tuned LLM without emotion transfer learning i.e. LLMsynth.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper il easy to read and understand\n- The novelty is good enough\n- Results seems encouraging", "reasons_to_reject": "- No replicability of results\n- No statistical validation of results are performed\n- Only one dataset has been used\n- More classic machine learning models should be used as baselines.", "questions_to_authors": "What is the prompt used for data generation?\nAre data generated semantically correct?\nLLMs models you sed are very small and sentences without meaning are often generated. Can your results be influenced by this issue?\nWhy do you not evaluate other simplest strategies for text classification as baseline?", "paper_topic_and_main_contributions": "The authors propose to perform a fine-tuning phase of LLMs on the emotion detection task before performing an additional fine-tuning phase on data for synthetic text identification.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "Yes", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "I suggest authors to read the paper carefully and fix typos.", "Justification_for_Ethical_Concerns": "Authors already stated an ethical discussion. Probably these claims should be properly avaluated."}, "extra_text": {}}, {"review_id": "Zc42vZIpJC", "reviewer": "Reviewer_DyWC", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the feedback and suggestions.\n\n**Reasons to Reject:**\n>“The experiments are only conducted in the domain of news …”\n\nWe conducted experiments in 5 non-news domains as described in lines 531-534 and release them in the ChatGPT100 dataset with this work. We highlight the unique significance of the news domain in lines 045-048 “*Due to the potentially profound consequences of global synthetic disinformation we focus mainly, but not exclusively, on the detection of synthetic text in the news domain.*” Furthermore, our emotionally-aware models, fine-tuned in the news-domain, outperform ChatGPT at detecting non-news synthetic text - which supports the claim of this work beyond the news domain. See also our answer below regarding the varied news domains included in the NEWSsynth dataset which we release with this work.\n\n>“... in the domain of news, where texts are usually more objective and there are less emotion expressions …”. \n\nWe disagree with this assertion. On the contrary, other authors have specifically chosen the news domain because of its high emotional content (Strapparava and Mihalcea, 2007) (Bostan et al., 2020). It is long established that different emotions lead to different actions (Spielberger, 1972) including what we write (Brand, 1985). Emotion can be exploited, for example “engagement based ranking” tends to favour content that evokes anger (Haugen, 2021).\nWhile some journalistic reporting is objective, opinion editorials (op-ed) are opinions pushing an agenda and, for example, tabloids tend to specifically exploit emotion. The 10k news articles in NEWSsynth training split come from 150 online sources including: “*movie reviews*” (which your review mentioned) and entertainment such as rollingstone.com, hollywoodlife.com, bollywoodhungama.com and mashable.com; and tabloids such as thedailymail.co.uk, dailystar.co.uk, thedailystar.net etc. which cover many types of news including journalism, op-eds, reviews, opinions etc.\n\n>“Table 6 gives contradicting evidence. …”\n\nJust to clarify, we believe it is Table 5 you are referring to. AT is a small dataset of 1.5k news headlines, of which 625 + 125 were used in the training and dev splits respectively. We attribute the poorer performance of AT to the dataset size being insufficiently large (rather than emotion itself underperforming).\nGAS includes the SST-2 movie review sentiment dataset which is orders of magnitude larger than the AT and GNE datasets. Table 5 shows that, apart from AT alone, any combination of datasets containing sentiment underperforms the models fine-tuned on emotion alone. For GAS, we mapped positive sentiment to joy and negative sentiment to sadness, which is crude because it could be 1 of 5 emotions, and which results in a dataset imbalance. (Apologies we did not make this clear in the paper but will do so in the revision). When we went in the opposite direction and mapped the emotion labels to sentiment labels (GAS+-),  the results improved. Overall, however, we conclude that mixing emotion and sentiment datasets is not a good idea, and that sentiment alone is not sufficient. \n\n>“...  Is it because synthetic texts do not have much emotion, or is it because it contains inconsistent emotions? …”\n\nThis is an important point, we will update the paper with examples of emotion incongruence and/or lack of emotion. See examples in our response to Reviewer 8S6u, repeated below for convenience:\n\nExamples where emoLLMsynth detected synthetic text that BERTsynth didn’t and there is affective coherence.\n- E.g. 1 \"Marshal Yanda looked \"**happy** and refreshed\" before Wednesday's minicamp practice but made some notes. After a **disappointing** season, the 37-year-old Yanda might be \"just getting **better and better and better**\" physically, veteran linebacker Whitney Mercilus said.”\n- E.g. 2 \"Bethenny Frankel, who hosted her daughter, Brynn, on ‘Bethenny Ever After,’ opened up about how she found balance when dealing with **two recent heartbreaks**. Bethenny Frankel, 39, has had a **rocky relationship** with her child, Bryn, 11, but all the drama behind the scenes has her engaged in **peace and happiness**.”", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* The idea is interesting that there is a lack of / there are different patterns of emotions in synthetic texts, which could be leveraged to aid detection of synthetic texts. \n\n* The proposed method is simple. The main experiments show that fine-tuning on an emotion classification dataset could improve the detection performance. \n\n* The authors also conducted several ablation experiments to help better understand the factors that affect the model's performance.", "reasons_to_reject": "The main weakness is that there is not enough analysis to support its main claim - emotion-awareness could be useful in detecting synthetic texts. Here are the reasons: \n\n*  The experiments are only conducted in the domain of news, where texts are usually more objective and there are less emotion expressions. So detecting emotion might help distinguish synthetic news and human-written news (as emotion is less frequent in this domain, the generation model is less likely to learn emotion during pretraining). The effects of emotion-awareness remain unclear for other domains such as movie reviews and blog posts. As emotion expressions are more frequent in these domains, synthetic generators pretrained on data in these domains might have already learned to generate texts with emotions. As a result, experiments in other domains are important to support the main claim in this work.  \n\n* Table 6 gives contradicting evidence. For example, AT fine-tunes the detection model with emotion classification data of news headlines, but it achieves a worse F1 score (84.69)  than BERTsynth (85.8 F1 in Table 2). This suggests that emotion-awareness might not be useful. Another example is GAS, which achieves the lowest F1 score of 83.72 in spite of the fact that it fine-tunes the model with 3 emotion classification datasets. As a result, some results do not support the claim that emotion-awareness could be useful for synthetic text detection. \n\n* Although the paper conducts several experiments to understand the effects of different factors on the model performance, it still lacks some analysis of why using emotion data improves the detection performance. Is it because synthetic texts do not have much emotion, or is it because it contains inconsistent emotions? At least give some examples where the predictions were corrected after fine-tuning over the emotion data, so readers could better understand how emotion-awareness might help in these cases.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper studies the task of distinguishing synthetic texts from human-written texts. The key idea in the paper is that synthetic texts might lack emotional patterns that are present in human-written texts. Based on the idea, they developed a detection model that is aware of emotion by fine-tuning a detection model first on an emotion classification dataset and then the dataset of synthetic and human-written texts. They conducted experiments and showed that the additional fine-tuning on emotion classification improves the detection model.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "There are some grammatical errors in this paper (e.g., row 5 - highlight). Please improve the writing."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper under discussion attempts to detect LLM synthetic texts. We hypothesize that LLM synthetic texts, compared to human-generated content, may lack emotion present. The authors hypothesize that this difference can be leveraged to create a more effective synthetic text classifier. 

Pros:
1. All three reviewers acknowledge the novelty of the paper's idea – using the emotional difference between human and synthetic content to distinguish between them.
2. Clear writing and presentation. The proposed method is clearly described. 
3. The datasets composed by the authors are a valuable contribution to the field and are likely to aid future research.

Cons:
1. Incomplete Analysis: Reviewer 1 and 3 highlights a main drawback in that there is insufficient analysis backing the central claim of the paper, particularly about the role of emotion in synthetic text detection.
2. Validation Concerns: Reviewer 2 brings up concerns about the lack of statistical validation. While the authors respond that the results in Table 2 are an average of five runs, it could be more robust if they reported variance or a significance test.
Decision: Accept-Findings