Generating Media Background Checks for Automated Source Critical Reasoning (2024)

Abstract: Not everything on the internet is true. This unfortunate fact requires both humans and models to perform complex reasoning about credibility when working with retrieved information. In NLP, this problem has seen little attention. Indeed, retrieval-augmented models are not typically expected to distrust retrieved documents. Human experts overcome the challenge by gathering signals about the context, reliability, and tendency of source documents - that is, they perform *source criticism*. We propose a novel NLP task focused on finding and summarising such signals. We introduce a new dataset of 6,709 “media background checks” derived from Media Bias / Fact Check, a volunteer-run website documenting media bias. We test open-source and closed-source LLM baselines with and without retrieval on this dataset, finding that retrieval greatly improves performance. We furthermore carry out human evaluation, demonstrating that 1) media background checks are helpful for humans, and 2) media background checks are helpful for retrieval-augmented models.

## introduction when humans perform knowledge-intensive reasoning, we are rarely able to rely on a single, authoritative source. instead, we forage for multiple sources, evaluate their trustworthiness, and synthesize answers @xcite . the basic task is to choose reliable sources, to read them reliably, and to combine them into reliable narratives @xcite . best practice for epistemic experts, such as journalists and historians, is to rely on multiple sources, to present evidence of source tendency and reliability, and to explain source disagreements to readers @xcite . search engines, acting as surrogate experts @xcite , similarly enrich their results with knowledge-contexts that help users reason about tendency and trust @xcite . no, that is disinformation created to justify sanctions. has the syrian government used chemical weapons? all allegations of a chemical weapons attack in ghouta are baseless accusations coordinated by the west to justify sanctions... ## task definition we seek to automatically find and summarise indicators of trustworthiness and tendency in order to facilitate source-critical reasoning by humans and nlp models. we refer to such summaries as media background checks (mbc). a fact is considered a relevant indicator to a media background check for source x if it can shift the relative trust assigned to source x for some consumer of content. such facts include, but are not limited to, the founding, leadership, and organization of the source, the funding model of the source, the remit and target audience of the source, public stances taken by the source (e.g., endorsing a political campaign), discussion in other media about the source, reliance by other media on the source, and the factual history of the source (as indicated e.g. by fact-checks undergone by reputable fact-checking organisations). we identify 42 common patterns of relevant facts, which we also use for evaluation -see appendix c. however, these do not fully cover the facts seen empirically in background checks from mb/fc. real-world indicators also include rarer patterns, such as multi-hop facts (e.g. other sources owned by the same company), and topic-specific indicators, for example evidence that a source cannot be trusted specifically on issues related to healthcare, québécois politics, or news about a particular advertiser. to understand common patterns, we further analyse 20 randomly chosen articles from mb/fc. the most common facts include known biases, funding model, remit, fact-checking history, and ownership (see appendix d). in order to build an mbc, a model must first recall or retrieve pertinent details for the source. these range from simple facts (e.g., whether the source is funded by a specific government) to more complex (e.g., whether a poor track record for factuality is evidenced). necessary retrieval steps may differ depending on the source type -websites have different indicators of credibility than print sources, for example @xcite . once the appropriate information has been retrieved, the model must then summarise this information. ## dataset media bias / fact check foot_1 (mb/fc) is an independent, volunteer-run website that promotes awareness of media bias. while past datasets have used bias ratings from the site @xcite , the textual "detailed reports" are an untapped resource. these are well-sources documents that summarise the factual history, bias, tendency, and potential conflicts of interest for media sources -i.e., background checks. we introduce a novel dataset based on these. we collected all reports listed through their search tool. this provided a total of 6,709 background checks. we cleaned the reports, and removed the bias and credibility ratings. statistics for the dataset can be seen in while mb/fc is an extensive resource, covering almost 7,000 media organisations, it is far from complete. in our human evaluation in sections 4.4, out of 40 sources used, 29 are covered by mb/fc reports. however, of these 29, 9 are incomplete or lack crucial information. we do not intend to replace human-written background checks -rather, we suggest that generated background checks can supplement mb/fc in these cases. ## experiments 4.1 models for a simple baseline, we prompt a large language model to generate an mbc given the name of a source. our prompt can be seen in appendix b.1. we experiment with two models, one closed-source and one open-source: gpt-3.5-turbo-0125 3 and llama 3 8b instruct 4 , the instruction-tuned version of llama 3. the latter is small enough to reasonably run on a single consumer-grade gpu. for these initial models, we rely solely on the information stored in their weights. as such, their performance is representative of the level of sourcecritical reasoning that can be expected of currentgeneration llms, without any additional evidence provided. mbcs often incorporate information available on the internet -the 6,709 background checks we analysed contained an average of 8.9 hyperlinks. to surface this information, we propose to rely on retrieval-augmented generation @xcite . in addition to providing external memory at generation time, retrieved evidence can also be shown to the user to establish why the background check should (or should not) be trusted. showing where evidence originates from to establish trust is crucial in related tasks, such as automated fact-checking @xcite . it also provides a rudimentary form of interpretability. as the necessary retrieval steps are roughly similar across background checks, we have compiled a list of seven information-seeking search queries focusing on different aspects of a background check (see the list in appendix e). in initial experiments, we found this strategy to perform better than generating queries using our llm. for each query, we gather relevant search results using the google search api 5 , selecting the top 30 retrieved documents. we exclude the media bias / fact check website and any website linking to it. to focus on the most salient retrieved information, we use a question-answering model to extract answers to a predefined question for each informationseeking query (also found in appendix e). here, we employ deberta v.3 @xcite pretrained on squad @xcite . by this process we transform a google search result into a question-answer pair. for each question-answer pair, we then expand the background check with the information contained within. this allows us to gradually incorporate information from multiple rounds of retrieval, without running into the token limit. to expand a background check with information from a specific question-answer pair, we use the prompt seen in appendix b.2 with each respective llm. 4 see https://huggingface.co/meta-llama/ meta-llama-3-8b-instruct 5 https://programmablesearchengine.google.com/ ## related work closest to our work, @xcite ; @xcite classifies the bias and factuality of sources based on data from media bias / fact check. however, they only predict the bias labels for both (as given elsewhere on mb/fc), not the detailed background checks we produce. features include sample articles from the source, its wikipedia page and twitter account, and information about the web domain. @xcite proposed further web-domain-features such as host, domain, and certificates for websites when predicting trustworthiness. knowledge conflicts in llms is an active research area; see the survey by @xcite . using their terminology, our proposal is most relevant inter-context conflicts, i.e. conflicts between two or more retrieved documents. the mentioned mitigation strategies, e.g. chen and shu (2023); vergho et al. (2024), follow the above line of reasoning: they use a trained model to classify documents into "factual" and "unfactual", and remove the "unfactual" documents. this assumes a single source of truth (the training data or evidence database for the misinformation detector), which as we have argued does not align with best practises for human knowledge experts. our proposal would instead enable an evidence-based comparison of trustworthiness for retrieved sources. a rich literature exists -referred to as factfinding -proposing probabilistic models for computing the likelihood of claims and the trustworthiness of sources @xcite @xcite @xcite . the basis is the assumption by @xcite that "a web site is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy web sites". @xcite proposed to predict the credibility of sources based on whether known misinformation spreaders share similar content. along similar lines, @xcite introduced a model for citation-worthiness where citation-worthy papers feature in citation-worthy journals, and vice versa. @xcite identified a copying problem in fact-finding, where this strategy breaks down if many seemingly independent actors copy their positions from each other; this makes majority voting an unreliable heuristic for determining whether a piece of information is trustworthy. bala and goyal (1998) identified a similar issue, where a "royal family of knowers" can cause communities to converge on false beliefs if their connectivity is much greater than the average epistemic agent. @xcite ; @xcite proposed to develop "credibility survey reports" for search results. these are topic-specific documents that map the positions of different, contradicting search results with respect to one particular search query. unlike our proposal, these do not represent the general biases, tendencies, and factual histories of media sources. @xcite later developed a summarisation algorithm for this task. ## conclusion we have introduced media background check generation, a novel nlp task wherein models summarise information about the bias, tendency, and factual record of media sources. media background checks enable source-critical reasoning by recalling the same facts that human knowledge experts use to assess trust. we have furthermore presented a dataset of 6,709 examples collected from the media bias / fact check website. we have investigated several baselines for the task, including both open-source and closed-source llms. our findings demonstrate that retrieval-augmentation can greatly improve performance, and interestingly that open-source models are very competitive on this difficult task. finally, our human evaluation gives strong evidence both that media background checks are helpful for humans when evaluating media sources, and helpful for models when generating answers based on retrieved sources.