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2022 Track

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TREC Health Misinformation Track (2022)

Track Introduction

Web search engines are frequently used to help people make decisions about health-related issues. Unfortunately, the web is filled with misinformation regarding the efficacy of treatments for health issues. Search users may not be able to discern correct from incorrect information, nor credible from non-credible sources. As a result of finding misinformation deemed by the user to be useful to their decision making task, they can make incorrect decisions that waste money and put their health at risk.

The TREC Health Misinformation track fosters research on retrieval methods that promote reliable and correct information over misinformation for health-related decision making tasks.

Track communication

Track announcements are made via the #health-misinfo-2022 channel in the TREC Slack.

2022 Track Tasks

The 2022 track will have two tasks. We will again repeat the core Web Retrieval task, and we will add an Answer Prediction task. Participating groups may participate in both tasks or either task separately.

Core Task: Web Retrieval

Task Description: Participants devise search technologies that promote credible and correct information over incorrect information, with the assumption that correct information can better lead people to make correct decisions.

Each search topic represents a user who is looking for information that is useful for making a “yes” or “no” decision regarding a health-related question. Better search result rankings will place very useful documents that are credible and correct at the top of ranking, and will not return incorrect information. In this search task, incorrect information is considered harmful and should not be returned.

Each topic will be formulated as a yes/no question. For example, “Does apple cider vinegar work to treat ear infections?” For each topic’s question, we have chosen an answer, either ‘yes’ or ‘no’, based on our best understanding of current medical practice. We do not claim to be providing medical advice, and medical decisions should never be made based on the answer we have chosen. Consult a medical doctor for professional advice.

Correct documents are those considered to be supportive of the topic’s correct answer, and incorrect documents are those that are supportive of the wrong answer.

For each topic, the topic’s author will determine an evidence link for a webpage that the topic author used as the basis for the topic’s answer.

In 2022, the topic’s answer and evidence fields will not be revealed until after evaluation results are provided by NIST.

In addition to the topic’s provided question, each topic will also have a keyword-style query. The question and query fields represents two common forms of how a user might query a modern web search system. Each topic will also have a background field that will give basic background information on the topic’s question. The core retrieval task is to use the topic’s query or question and return a ranking of documents without use of the any of the other topic fields.

Runs may be either automatic runs or manual runs. Automatic runs are required to make no use of the provided topics except for final production of the run. Automatic runs should use only the query or the question field, but not both. Automatic runs may not use the background, evidence, or the answer fields.

The 2022 topics will be similar to the 2021 topics, and if needed for an automatic run, the 2021 topics should be used for tuning and not the 2022 topics.

Manual runs are any runs that are not automatic runs. A manual run typically has been tuned on the topics or had human intervention to improve performance. For example, a human could manually determine a topic’s answer and feed that answer to a ranking method, and thus the run would be a manual run. Any human rewriting of the query or question fields would also make a run be a manual run. Use of the background, evidence, and answer fields would also make a run a manual run. (The evidence and answer fields will not be available until the 2022 evaluation results are released.)

Auxiliary Task: Answer Prediction

As noted above, in 2022, we will not provide a topic’s answer until after evaluation. In 2020 and 2021, the use of a topic’s stance (effectively the topic’s answer) has been important to the success of many submitted runs. The Answer Prediction task provides a chance for participants to focus on the challenge of predicting the answer to the topic’s question.

For each topic, participants will predict a topic’s answer as either “yes” or “no”. Participants will also provide a prediction score between 0 and 1 for each topic. A score of 1 means “yes” and a score of 0 means “no”. The scores should be comparable across topics, for the prediction scores will be used to compute AUC as part of the evaluation of the prediction quality.

As with the core Web Retrieval task, Answer Prediction runs may be automatic or manual and will follow the same rules. See above for details.

Topics

Topics will be authored by the track organizers. The NIST assessors will be provided the topic’s question and background, be asked to make judgments as per the assessing guidelines. The 2022 guidelines are to be written, but they will be similar to the 2021 assessing guidelines.

The topics will be provided as XML files using the following format:

<topics>
  <topic>
    <number>12345</number>
    <question>Does apple cider vinegar work to treat ear infections?</question>
    <query>apple cider vinegar ear infection</query>
    <background>Apple cider vinegar is a common cooking ingredient that contains
    acetic acid and has antiseptic properties.  Ear infections can be caused by 
    either viruses or bacteria and cause fluid build up in the middle ear, which 
    is located behind the eardrum.</background>
    <disclaimer>We do not claim to be providing medical advice, and medical 
    decisions should never be made based on the answer we have chosen. Consult 
    a medical doctor for professional advice.</disclaimer>
  </topic>
<topic>
...
</topic>
</topics>

After evaluation results are released, the topics will be updated to include answer and evidence fields.

Document Collection

In 2022, we are reusing the collection we used in 2021. We will be using the noclean version of the C4 dataset used by Google to train their T5 model. The collection is comprised of text extracts from the April 2019 snapshot of Common Crawl. The Collection contains ~ 1B English documents.

You can download the corpus on a Debian/Ubuntu machine using the following commands (see HuggingFace for further information).

sudo apt-get install git-lfs 
git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/allenai/c4
cd c4
git lfs pull --include="en.noclean/c4-train*"

The collection is made up of the 7168 gzipped jsonl files located in the en.noclean directory. We are using only the c4-train.*.json.gz files and not the c4-validation.*.json.gz files. Each file contains ~150k documents, and has one document per line. A document is a json object with the fields text, url and timestamp. As packaged in c4.noclean, documents do not contain a document identifier. For this TREC track we will be adding our own document identifiers to the collection.

The docno spec is as follows:

For documents inside files c4/en.noclean/c4-train.?????-of-07168.json.gz, the docno will be en.noclean.c4-train.?????-of-07168.<N> where <N> is the line number of the document starting at 0. This goes for all 7168 training files in the path c4/en.noclean/.

So for example, in the file en.noclean/c4-train.01234-of-07168.json.gz the first document’s identifier will be en.noclean.c4-train.01234-of-07168.0, the second document’s identifier will be en.noclean.c4-train.01234-of-07168.1 and the last document’s identifier will be en.noclean.c4-train.01234-of-07168.148409.

One way to insert document identifiers is by using the provided python script. Another would be to name the documents as you index them.


"""
Script to add docnos to files in c4/no.clean
To process all files:
python renamer.py --path <path-to-c4-repo>
To process a subset, e.g. the first 20 files:
python renamer.py --path <path-to-c4-repo> --pattern 000[01]?
"""
import argparse
import glob
import gzip

parser = argparse.ArgumentParser(description='Add docnos to C4 collection.')
parser.add_argument('--path', type=str, help='Root of C4 git repo.', required=True)
parser.add_argument('--pattern', type=str, default="?????", help='File name patterns to process.')
args = parser.parse_args()
pattern = args.pattern
path = args.path


def new_docno(file_number, line_number):
    return f'en.noclean.c4-train.{file_number}-of-07168.{line_number}'


files = sorted(list(glob.iglob(f'{path}/en.noclean/c4-train.{pattern}-of-07168.json.gz')))

for filepath in files:
    with gzip.open(filepath) as f:
        file_number = filepath[-22:-22 + 5]
        file_name = filepath[-31:]
        print(f"adding docnos to file number {file_number} ...")
        with gzip.open(f'{path}/en.noclean.withdocnos/{file_name}', 'wb') as o:
            for line_number, line in enumerate(f.readlines()):
                line = line.decode('utf-8')
                new_line = f"{{\"docno\":\"{new_docno(file_number, line_number)}\",{line[1:]}"
                o.write(new_line.encode('utf-8'))


Evaluation

The evaluation of Web Retrieval runs will be similar to 2021 but with likely improvements by the organizers. The Answer Prediction runs will be evaluated using standard measures for evaluation of prediction tasks with AUC being the primary measure.

Runs

Participating groups will be allowed to submit as many runs as they like, but they need authorization from the Track organizers before submitting more than 10 runs per task. Not all runs are likely to be used for pooling and groups will need to specify a preference ordering for pooling purposes.

Runs may be either automatic or manual.

Automatic runs: Only the topic’s query or question field may be used for automatic runs. An automatic run should only use the query or the question field, but not both. An automatic run is made without any tuning or customization based on the topics. Best practice for an automatic run is to avoid using the topics or even looking at them until all decisions and code have been written to produce the automatic run.

Manual runs: A manual run is anything that is not an automatic run. Manual runs commonly have some human input based on the topics, e.g., hand-crafted queries or relevance feedback. All topic fields may be used for manual runs. We encourage manual runs in addition to automatic runs.

Submission format for Web Retrieval runs will follow the standard TREC run format. For each topic, please return 1,000 ranked documents. The standard TREC run format is as follows:

qid Q0 docno rank score tag

where:

The fields should be separated with a space.

«««< HEAD Evaluation: The final qrels will contain assessments with respect to the following criteria:

Answer labels will be mapped to correctness by comparing the answer in the document and the topic answer field. If the answer provided by the document is the same in the answer field of the topic, then the document will be considered correct, otherwise it will be not correct.

Submitted runs will be evaluated with respect to usefulness, correctness and credibility:

Specific details on how to compute evaluation measures can be found here.

An example run is shown below:

1 Q0 en.noclean.c4-train.04124-of-07168.69102 1 14.8928003311 myGroupNameMyMethodName
1 Q0 en.noclean.c4-train.03346-of-07168.52165 2 14.7590999603 myGroupNameMyMethodName
1 Q0 en.noclean.c4-train.03904-of-07168.54203 3 14.5707998276 myGroupNameMyMethodName
...

The submission format for the Answer Prediction task will be four columns, each separated by a space:

qid answer score runtag

where:

An example run is shown below:

151 yes 0.95234 myGroupNameMyMethodName
152 no 0.30218 myGroupNameMyMethodName
153 no 0.00396 myGroupNameMyMethodName
...

321f7e4b91c4e1492642a14d2b57ccdf523290be

Schedule

Organizers