Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation

Source code and data of our EMNLP 2022 paper Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation.

Abstract Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing interest in automatic fact-checking, it is still unclear if automated approaches can realistically refute harmful real-world misinformation. Here, we contrast and compare NLP fact-checking with how professional fact-checkers combat misinformation in the absence of counter-evidence. In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims. We then define two requirements that the evidence in datasets must fulfill for realistic fact-checking: It must be (1) sufficient to refute the claim and (2) not leaked from existing fact-checking articles. We survey existing fact-checking datasets and find that all of them fail to satisfy both criteria. Finally, we perform experiments to demonstrate that models trained on a large-scale fact-checking dataset rely on leaked evidence, which makes them unsuitable in real-world scenarios. Taken together, we show that current NLP fact-checking cannot realistically combat real-world misinformation because it depends on unrealistic assumptions about counter-evidence in the data.

Installation

1. Clone this repository and change the working directory:

git clone [email protected]:UKPLab/emnlp2022-missing-counter-evidence.git && cd emnlp2022-missing-counter-evidence 

2. Create a new virtual environment:

python3.6 -m venv missing-evidence-env
source missing-evidence-env/bin/activate

3. Install all dependencies within the new environment:

pip install --upgrade pip
pip install -r requirements.txt
pip install torch==1.10.0+cu113 torchvision==0.11.0+cu113 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

Getting the Data

This section describes how to get the data used for the experiments and analyses. We base our experiments and analysis on the MultiFC dataset by Augenstein et al. (2019).

1. Download the file multi_fc_publicdata.zip as provided by Hansen et al., (2021) (Link to the repository) and place it at ./data/download/multi_fc_publicdata.zip. The file includes the MultiFC dataset and separate splits for the experiments on Snopes and PolitiFact.

2. To prepare the data for the experiments, run the following command:

python create-dataset.py ./data/download/multi_fc_publicdata.zip ./data

It will extract the zip file and create directories for claims from Snopes (snes) and PolitiFact (pomt) within the ./data directory. It will create train/dev/test splits in the required format for both directories.

3. Verify that the command produced two new directories:
   • ./data/snes with data splits using Snopes data.
   • ./data/pomt with data splits using PolitiFact data.

Pattern-based detection of leaked evidence

This section describes how we identified leaked evidence within the MultiFC dataset using patterns for content and URLs.

Setup the config file

Verify that the paths in configs/multifc-analyze-config.json point to the correct directories (default values should work fine):

{
  "path_multifc": "./data/download/multi_fc_publicdata",
  "analysis_dir": "./data/analysis-results"
}

Description of the fields:

Field	Description
path_multifc	Points to the extracted zip directory containing the MultiFC dataset.
analysis_dir	Points to an existing directory. Outputs will be stored in this directory.

Run pattern-based detection

Run the script:

python analyze-multifc.py identify configs/multifc-analyze-config.json

This script will run over all snippets in MultiFC to determine whether they contain leaked evidence based on the used patterns. It produces a file leaking-all-multifc.csv. The file contains the following columns:

Column	Description
claim_id	Unique MultiFC ID for the claim for which the evidence snippet was retrieved.
snippet_id	Numeric ID (1-10) of the rank of the evidence snippet for the claim.
snippet_src	URL from which the snippet was retrieved.
leaked_url	Boolean value indicating if the pattern-based approach considers the URL leaked (True) or not (False).
leaked_words	Boolean value indicating if the pattern-based approach considers the content leaked (True) or not (False).
leaked	Boolean value indicating if any pattern-based approach (URL or content) consider the snippet leaked (True) or not (False).
label	Verdict of the respective claim from MultiFC.
misinformation	Boolean value if the respective claim is considered misinformation based on our predefined list of misinformation verdicts.

Compute leaked evidence for misinformation

To compute the ratio of leaked evidence (Table 5 in the paper) run:

python analyze-multifc.py stats configs/multifc-analyze-config.json --misinfo

This script computes the ratio of leaked evidence based on the previously created file leaking-all-multifc.csv.

Experiments on Snopes and PolitiFact

This section describes how to reproduce the experiments on leaked and unleaked evidence snippets using claims from Snopes and PolitiFact (via MultiFC).

The settings for each trained model in our experiments are stored within the .json files in the experiments directory. Each file contains the following fields:

Field	Values	Description
method	"concat", "split-by-snippet" (default: "concat")	Defines if all evidence snippets are concatenated with the claim and form one sample ("concat"), or if the claim is concatenated with each snippet individually and forms one sample per snippet ("split-by-snippet").
name	Text	Name of the stored model.
batch-size-train	Number (default: 16)	Batch size during training.
batch-size-eval	Number (default: 16)	Batch size during evaluation.
lr	Float (default: 2e-5)	Learning rate.
epochs	Number (default: 5)	Number of epochs for training.
seed	Number (defaults: 1, 2 and 3)	Random seed.
model-name	Text (default: bert-base-uncased)	Name of the used language model to fine-tune.
lowercase	Bool (default: True)	Determines if the text will be lowercased.
max-input-len	Number (default: 512)	Number of tokens after which the input gets truncated.
task-variant	"complete", "only-evidence","only-claim"	Defines which parts of the samples are used.
evidence-variant	"title-snippet", "title", "snippet"	Defines which parts of each evidence snippet are used. (Ignored when {"task-variant": "only-claim"}).
evaluation	Dictionary	Settings for evaluating the model on different data splits.

Training

To train a model, use an existing experiment file in the experiments directory (or create a new one).

For example, the file snes-evidence_only-snippet-bert-2.json defines the experiment to train a BERT base model to predict the correct verdict only based on the evidence snippet texts, using the random seed 2. To run this experiment, run:

python train.py train snes-evidence_only-snippet-bert-2.json

The fine-tuned model will be stored in the results directory.

Prediction

Each experiment .json file defines in the field evaluation on which data the trained model gets evaluated. The format looks as follows:

{
  "...": "...",
   "evaluation": {
      "<key-1>": {
         "name": "<name-of-prediction-file.jsonl>",
         "data": "<directory-of-data> // e.g. 'snes'",  
         "file": "<file-in-directory> // e.g. 'test.jsonl'"
      },
      "<key-2>": {
         "name": "...",
         "...": "..."
      }
   }
}

To create a .jsonl file containing the predictions run the following command:

python predict.py predict <experiment-file> <key>

By default, the keys snes-all and pomt-all are used to create predictions on the test set of Snopes and PolitiFact respectively. For example:

python predict.py predict snes-evidence_only-snippet-bert-2.json snes-all

The prediction files are stored within the ./predictions directory.

Evaluation

To evaluate the predictions based on all / only leaked / only unleaked samples (Table 7) run:

python evaluate.py evaluate ./configs/multifc-analyze-config.json <key> --exp <experiment-file-1> --exp <experiment-file-2> --exp <experiment-file-3>

The script evaluates and averages the performance across all provided experiments. To separate leaked from unleaked samples this script requires the result file from the pattern-based detection of leaked evidence. For example:

python evaluate.py evaluate ./configs/multifc-analyze-config.json snes-all --exp experiments/snes-evidence_only-snippet-bert.json --exp experiments/snes-evidence_only-snippet-bert-2.json --exp experiments/snes-evidence_only-snippet-bert-3.json 

Evaluate comparison between evidence-only and complete model (Appendix)

To evaluate the direct comparison per veracity label between the model trained on the entire input (claim and evidence), and the model trained only on the evidence as input run the following commands (Tables 11 & 12 in the Appendix):

For Snopes:

python evaluate.py compare ./configs/multifc-analyze-config.json snes-all ./experiments/snes-evidence_only-bert.json ./experiments/snes-complete-bert.json

For PolitiFact:

python evaluate.py compare ./configs/multifc-analyze-config.json pomt-all ./experiments/pomt-evidence_only-bert.json ./experiments/pomt-complete-bert.json

The script will automatically evaluate the provided experiments with all three seeds (1, 2, 3) and requires the predictions from all these trained models.

Evaluate on only leaked/unleaked snippets (Appendix)

The previous experiments consider a sample leaked if it contains at least one leaked evidence snippet and unleaked otherwise. The model's prediction is based on all evidence snippets. We additionally evaluate our model on samples that only contain leaked evidence snippets or only contain unleaked evidence snippets.

To reproduce this evaluation, first create leaked and unleaked samples using the following command for snes and pomt:

python create-leaked-unleaked-splits.py ./configs/multifc-analyze-config.json snes test.jsonl
python create-leaked-unleaked-splits.py ./configs/multifc-analyze-config.json pomt test.jsonl

It will separately combine each claim with only leaked and only unleaked evidence snippets (as detected by the pattern-based approach). The resulting files are stored in the respective directories of Snopes (snes) and PolitiFact (pomt).

Create the predictions by using the same command as described above and the keys snes-leaked / snes-unleaked (Snopes) or pomt-leaked / pomt-unleaked (PolitiFact). For example:

python predict.py predict snes-evidence_only-snippet-bert-2.json snes-leaked
python predict.py predict snes-evidence_only-snippet-bert-2.json snes-unleaked

To reproduce the results in Tables 13 & 14 (Appendix) run (train & predict) the following experiments: They describe evidence-only models using three different random seeds.

Snopes	PolitiFact
snes-evidence_only-bert.json	pomt-evidence_only-bert.json
snes-evidence_only-bert-2.json	pomt-evidence_only-bert-2.json
snes-evidence_only-bert-3.json	pomt-evidence_only-bert-3.json

To evaluate on Snopes finally run:

python evaluate.py leaked-unleaked snes-leaked snes-unleaked snes --exp ./experiments/snes-evidence_only-bert.json --exp ./experiments/snes-evidence_only-bert-2.json --exp ./experiments/snes-evidence_only-bert-3.json

To evaluate on PolitiFact finally run:

python evaluate.py leaked-unleaked pomt-leaked pomt-unleaked pomt --exp ./experiments/pomt-evidence_only-bert.json --exp ./experiments/pomt-evidence_only-bert-2.json --exp ./experiments/pomt-evidence_only-bert-3.json

Manual Analysis

The results of our manual analysis can be found in data/manual-analysis.

Contact

For questions please contact Max Glockner: [lastname]@ukp.informatik.tu-darmstadt.de.

Citing the paper

Please this bibtex when citing our work:

@inproceedings{glockner-etal-2022-missing,
    title = "{M}issing {C}ounter-{E}vidence {R}enders {NLP} {F}act-{C}hecking {U}nrealistic for {M}isinformation",
    author = "Glockner, Max and Hou, Yufang and Gurevych, Iryna",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.397",
    pages = "5916--5936"
}