Organizations: Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing 401331, China · School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China · Department of Plastic Surgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China · Informatics Research Centre, University of Reading, Reading, United Kingdom
Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive patterns. In this work, we focus on low resource fake news detection, where only a small labeled set is available for training, while evaluation is performed under a fixed and unchanged test protocol. This setting is motivated by costly, ambiguous fact-checking and emerging topics and domains that are poorly covered by existing benchmarks. Existing data augmentation methods, such as synonym substitution, generative adversarial networks, and large language model (LLM)-based generation, often produce fluent but overly generic content, lacking the task-specific relevance needed for accurate detection. To address this, we propose SharP, a boundary-aware text generation framework guided by soft and hard prompts, which leverages LLMs as tools adapted to produce task-specific synthetic samples. SharP employs a dual prompt mechanism: hard prompts provide structured guidance and factual constraints, while soft prompts introduce learned semantic patterns from the data. These two types of prompts are combined to guide the generation process, enabling the model to produce samples that closely align with the characteristics of news. To further enhance detection performance, we incorporate a boundary-aware strategy that steers generation toward areas where the classifier is less confident, helping to clarify subtle distinctions between real and fake content. In addition, we adopt a dual objective optimization that balances semantic alignment with the source data and classifier feedback. This encourages the generation of samples that are both domain-consistent and helpful for refining decision boundaries, ultimately improving model robustness and generalization in low resource fake news detection.
Figures & tables
Dataset
Source
Lang.
Scale
Labels / Structure
Social rumor verification
PHEME ( Zubiaga et al., 2016 )
Twitter events
EN
330/4.8k
Conversation threads; rumor verification / rumor vs. non-rumor.
Twitter15/16 ( Ma et al., 2017a ; Ma et al., 2017b )
Twitter events
EN
1490/818
Event-level rumor detection with reply structure.
Weibo (rumor) ( Ma et al., 2016 )
Sina Weibo
ZH
2,560 (1280 fake + 1280 true)
Post-level rumor vs. non-rumor (common benchmark variants).
News/claim fake news detection
LIAR ( Wang, 2017 )
PolitiFact claims
EN
12.8k
6-way truthfulness; often binarized in detection.
Table 1: Representative benchmark datasets for fake news and rumor detection
Work
LLM role
Datasets / key results (as reported in the original papers)
Chen et al. (2025)
Prompted LLM as detector
On FND23, GPT-3.5-turbo with a task-specific prompt reports Acc=0.875 and Macro- F1 =0.528; the paper additionally benchmarks multiple datasets/prompting variants.
Raza et al. (2025)
LLMs vs. BERT-like (zero-/few-shot, fine-tune)
The authors report F1 : on their GPT-labeled dataset vs. NELA-GT, RoBERTa achieves 0.897 (vs. 0.836), while Mistral-7B-v0.2 fine-tuned achieves 0.802 (vs. 0.737).
Choi et al. (2025)
LLM-based data augmentation
Finds LLM-generated augmentation yields limited or inconsistent gains for COVID-19 misinformation stance detection across settings, cautioning that fluency does not guarantee task-useful diversity.
PHEME (5 events, 1%–70% labeled): Acc improves from 0.760 (1%) to 0.938 (70%); consistently outperforms strong baselines under low resource splits (see Table 6 ).
Table 2: Representative LLM-related studies for fake news detection and their reported results
Figure 1: Model Architecture of SharP
Symbol
Description
DLt=(XL,YL)
Labeled pool at round t , where XL and YL denote labeled texts and their labels.
DUt=XU
Unlabeled pool at round t , where XU denotes unlabeled texts.
St
Queried unlabeled batch selected from DUt at round t .
St′
Augmented labeled set generated from St (inherits labels).
b
Query batch size in each active learning round.
T
Total number of active learning rounds.
Table 3: Notation used in the Method section
Figure 2: Method Flow
Figure 3: Architecture of Data Augmentation Module
Figure 4: Vocabulary Mapping
LD=LossD(D(xL),yL)
Algorithm 1 Model Pre-training Process
St={(xu(i),yu(i))}i=1b.
Algorithm 2 Model Fine-tuning Process
Dataset
Cha.
Fer.
Ger.
Ott.
Syd.
Total
Labeled Fake News
318
144
95
330
382
1269
Labeled Real News
1481
719
94
280
559
3133
Unlabeled Fake News
140
140
143
140
140
703
Unlabeled Real News
140
140
137
140
140
697
Table 4: Dataset statistics for Group A (PHEME) used in our labeled/unlabeled pool setting
Dataset
LIAR
Twitter15/16
Labeled Fake News
5,514
445
Labeled Real News
6,997
429
Unlabeled Fake News
143
134
Unlabeled Real News
137
146
Total
12,791
1,154
Table 5: Dataset statistics for Group B (LIAR and Twitter15/16) used in our labeled/unlabeled pool setting
Dataset
Stra.
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
70%
Germanwings-crash
HAN
0.500
0.734
0.713
0.691
0.691
0.745
0.734
0.755
0.723
0.766
0.745
0.713
dEFEND
0.670
0.745
0.723
0.702
0.745
0.755
0.745
0.787
0.681
0.745
0.755
0.713
HMCAN
0.250
0.272
0.652
0.757
0.766
0.712
0.610
0.727
0.651
0.750
0.768
0.813
DDT
0.740
0.729
0.760
0.781
0.792
0.823
0.833
0.854
0.865
0.854
0.885
0.927
Deepseek-7b
0.617
0.617
0.617
0.617
0.628
0.628
0.628
0.617
0.606
0.617
0.617
0.606
SharP
0.760
0.802
0.792
0.833
0.833
0.813
0.854
0.885
0.875
0.875
0.917
0.938
Table 6: Performance comparison on Group A datasets (Acc.)
Figure 5: Sample Space Visualization
Dataset
Stra.
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
70%
PHEME (Germanwings-crash)
HAN
0.500
0.734
0.713
0.691
0.691
0.745
0.734
0.755
0.723
0.766
0.745
0.713
dEFEND
0.670
0.745
0.723
0.702
0.745
0.755
0.745
0.787
0.681
0.745
0.755
0.713
HMCAN
0.250
0.272
0.652
0.757
0.766
0.712
0.610
0.727
0.651
0.750
0.768
0.813
DDT
0.740
0.729
0.760
0.781
0.792
0.823
0.833
0.854
0.865
0.854
0.885
0.927
Deepseek-7b
0.617
0.617
0.617
0.617
0.628
0.628
0.628
0.617
0.606
0.617
0.617
0.606
SharP
0.760
0.802
0.792
0.833
0.833
0.813
0.854
0.885
0.875
0.875
0.917
0.938
Table 7: Performance comparison on Group B datasets (Acc.)
Figure 6: Ablation Study
Figure 7: Hyperparameter Sensitivity Analysis
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Item
Content
Template Th(x,st)
Rewrite this ONE sentence using <s_t> word choices (minimal substitutions). Keep meaning/facts; keep names/numbers unchanged. Return ONE sentence only, no extra text. <x>
Style tokens
st∈{common,professional,intense,neutral}
Example instantiation (common style)
Prompt ( st=common )
Rewrite this ONE sentence using common word choices (minimal substitutions). Keep meaning/facts; keep names/numbers unchanged. Return ONE sentence only, no extra text. The government announced a $5 million relief plan on Tuesday.
Appendix
Table 8: Hard prompt template used for style-controlled rewriting, and an example instantiation with the common style (the best-performing style token in our experiments)