LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media
Authors: Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Chengkai Li
Accepted at: acl25

RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims

Zhengyuan Zhu, Zeyu Zhang, Haiqi Zhang, Chengkai Li
University of Texas at Arlington
{zhengyuan.zhu, haiqi.zhang}@mavs.uta.edu
{zeyu.zhang, cli}@uta.edu

Abstract

Social media provides a valuable lens for assessing public perceptions and opinions. This paper focuses on the concept of truthfulness stance, which evaluates whether a textual utterance affirms, disputes, or remains neutral or indifferent toward a factual claim. Our systematic analysis fills a gap in the existing literature by offering the first in-depth conceptual framework encompassing various definitions of stance. We introduce RATSD (Retrieval Augmented Truthfulness Stance Detection), a novel method that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims. RATSD is evaluated on TSD-CT, our newly developed dataset containing 3,105 claim-tweet pairs, along with existing benchmark datasets. Our experiment results demonstrate that RATSD outperforms state-of-the-art methods, achieving a significant increase in Macro-F1 score on TSD-CT. Our contributions establish a foundation for advancing research in misinformation analysis and provide valuable tools for understanding public perceptions in digital discourse.

1 Introduction

Online information provides a valuable lens through which we can gauge people’s perceptions and opinions, offering insights into societal trends, beliefs, and behaviors that shape human society (Sobkowicz et al., 2012; Zhang et al., 2018; Willaert et al., 2020). This paper focuses on the concept of truthfulness stance which, given a factual claim, assesses whether a textual utterance affirms its truth, disputes it as false, or expresses a neutral or indeterminate position. Specifically, the study examines social media posts, focusing on tweets from Twitter (now rebranded as X) as the primary form of textual utterance. Figure 1 presents examples of tweets that express positive, neutral, negative, or no stance regarding the truthfulness of the same factual claim.

Truthfulness stance has the potential to be a useful tool in discerning how misinformation spreads (Ecker et al., 2022) and shapes decision-making in political discourse (Ognyanova et al., 2020) and health-related contexts (Suarez-Lledo and Alvarez-Galvez, 2021). Such insights can help social scientists assess the impact of misinformation and develop effective interventions (Watts et al., 2021). Additionally, health organizations can utilize this information to gauge public opinion and identify communities in specific geographic regions that may be more susceptible to health-related misinformation (Loomba et al., 2021; Zhu et al., 2021). Truthfulness stance can also be a valuable tool for marketers and media strategists in evaluating the effectiveness of their campaigns (Dwivedi et al., 2021) and tracking shifts in public perception regarding a product or political figure (Dimitrova and Matthes, 2018).

Definitions of stance across various studies share a common conceptual framework, wherein a de-

![Four tweets illustrating different truthfulness stances toward the same factual claim.](page_1012_468_377_312.png)

Figure 1: Four tweets illustrating different truthfulness stances toward the same factual claim.
clared stance comprises four components: a textual utterance expressing the stance (e.g., a news article or a social media post), a target that receives the stance (e.g., an entity, a topic, an event, or a factual claim), the orientation of the stance (e.g., positive, neutral, or negative), and the type of stance, which specifies what the stance is about (e.g., favorability toward the target entity, the likelihood of an event, or the target claim’s truthfulness). Section 2 presents this conceptual framework of stance definitions in greater detail. While prior studies (Küçük and Can, 2020; Hardalov et al., 2022; Alturayef et al., 2023) have addressed various aspects of it, our systematic articulation of the conceptual framework represents a significant contribution to the field, as such a nuanced and fine-grained analysis has been largely absent from the literature. Regarding the specific definition of stance examined in this paper, ours is the first to focus on the stance of social media posts toward the truthfulness of general claims, as further explained in Section 2.

Section 3 proposes novel methods for truthfulness stance detection. Specifically, we present RATSD (retrieval augmented truthfulness stance detection), a large language model (LLM)-powered framework, as illustrated in Figure 2. The framework leverages LLMs, including open-source models such as Zephyr (Tunstall et al., 2023) and proprietary models such as GPT-3.5 (Achiam et al., 2023), for three purposes. First, RATSD generates contextual knowledge related to factual claims and tweets using the approach of retrieval augmented generation (RAG) (Lewis et al., 2020). Incorporating contextual knowledge enables the framework to access relevant, up-to-date information, thereby enhancing stance detection models’ accuracy and contextual awareness. Second, RATSD produces stance analyses — narratives that describe tweets’ stance toward claims — by prompting LLMs with the generated contextual knowledge. This leverages LLMs’ ability to analyze stance while integrating contextual information into the learning process. Additionally, it helps mitigate the informal tone often present in social media content. Third, RATSD includes a classifier based on a fine-tuned language model that takes as input a claim, the tweet analysis, and the contextual knowledge. It then outputs a classification label representing the orientation of the tweet’s truthfulness stance toward the claim. To our knowledge, this work pioneers the application of RAG to stance detection, demonstrating the utility of contextual knowledge for the task.

Section 4 introduces our new benchmark dataset TSD-CT which consists of 3,105 claim-tweet pairs (hence the “CT” in its name). The dataset captures tweets’ stances on the truthfulness of factual claims sourced from PolitiFact, labeled using our in-house annotation tool. This tool includes multiple quality control mechanisms to ensure the accuracy of the annotated dataset.

Section 5 discusses our experiments with RATSD on TSD-CT and three existing datasets — SemEval-2019 (Gorrell et al., 2019), WT-WT (Conforti et al., 2020) and COVIDLies (Hossain et al., 2020) — across various experimental settings and choices of LLMs. The results show that RATSD with GPT-3.5 outperforms state-of-the-art models (Reddy et al., 2022; Arakelyan et al., 2023), achieving a 6.38-

![The RATSD framework diagram showing the flow of data from factual claims, fact check corpus, tweet corpus, and tweets through filtering, embedding, candidate chunks, prompting LLM, contextual knowledge synthesis, fine-tuning LLM, and truthfulness stance detection model.](page_120_120_1208_670.png)

Figure 2: The RATSD framework.
point increase in Macro-F1 score on TSD-CT. Our ablation study revealed that both contextual knowledge and stance analyses play crucial roles in enhancing the model’s performance.

In summary, this paper’s contributions are as follows:
• We developed a novel conceptual framework for defining stance and introduced a unique task formulation for truthfulness stance detection.
• We created a new benchmark dataset, TSD-CT, which has the potential to be a valuable resource for research in this field and computational social science more broadly.
• We designed RATSD, a method that integrates RAG for generating contextual knowledge and LLMs for stance analysis. In our experiments, RATSD, based on GPT-3.5, achieves the highest Macro F1 score compared to other models.
• The TSD-CT dataset and RATSD’s codebase are available at https://github.com/idirlab/RATSD to promote research reproducibility and facilitate further studies.

2 Conceptual Framework and Task Definition

Given a factual claim \( c \) and a tweet \( t \), the task of truthfulness stance detection is to return one of three distinct classification labels — positive (\( \oplus \)), negative (\( \ominus \)), or neutral/no stance (\( \odot \)). A positive stance applies when \( t \) conveys the belief that \( c \) is true. A negative stance indicates that \( t \) believes \( c \) is false. A neutral/no stance signifies that \( t \) either expresses uncertainty about the truthfulness of \( c \) (neutral) or does not explicitly take a position on \( c \)’s truthfulness, even though both \( t \) and \( c \) discuss the same topic (no stance). As summarized in Section 1, the conceptual framework of stance consists of four components: the utterance, target, orientation, and type of the stance. This section examines each component in greater detail and discusses how our work both aligns with and diverges from existing definitions of stance in the literature.

Orientation of Stance. Figure 3 illustrates the relationship among all stance orientation labels. Note that our truthfulness stance detection model does not consider unrelated pairs, because detecting the relevance between \( c \) and \( t \) falls within the scope of fields such as textual semantic similarity (Wang and Dong, 2020; Gomaa et al., 2013), which is beyond the focus of this work. We did annotate unrelated pairs while creating TSD-CT, though, in order to exclude such pairs in training and evaluating detection models.

Conceptually, we recognize the difference between a neutral stance and no stance. A tweet holds a neutral stance if it expresses a mixed verdict or uncertainty about a claim’s truthfulness. On the other hand, a tweet has no stance if, while being related to the claim in terms of topic, it does not express an intentional stance reflecting beliefs or desires (Dretske, 1988) regarding the claim’s truthfulness. This distinction is similarly recognized in some existing studies, such as SemEval-19 and (Grimminger and Klinger, 2021), though they use different terminology for stance labels.

In practice, though, discerning no stance is highly challenging. Example (3) in Figure 7 in the Appendix demonstrates one such challenging case. Although the tweet is highly pertinent to the claim, as it mentions Paul Ryan, gun laws, and “action,” it does not indicate whether Paul Ryan has blocked such actions or not. Its stance is not neutral; rather, it does not express any stance on the claim’s truthfulness. Neutral stance and no stance often exhibit strong similarities. This difficulty is evident in our preparation of the TSD-CT dataset where, among all pairs of stance labels, the (neutral stance, no stance) pair received the lowest inter-annotator agreement among expert annotators.

Given this intrinsic challenge, we chose to merge neutral stance and no stance into a single class \( \odot \) for both dataset creation and detection model development. A similar approach was used in sentiment analysis, where Koppel and Schler (2006) categorized documents’ neutrality sentiment into two types. The first type of neutrality sentiment (analogous to no stance in our framework) applies to documents that present objective information without expressing a clear sentiment. The second type (akin to neutral stance) applies to documents that convey a mix of positive and negative sentiment.

![A claim c and a tweet t may be related or unrelated. Related claim-tweet pairs are partitioned into four cases of stance.](page_1042_180_377_246.png)

Figure 3: A claim \( c \) and a tweet \( t \) may be related or unrelated. Related claim-tweet pairs are partitioned into four cases of stance.
<table>
  <tr>
    <th rowspan="2">Type of stance</th>
    <th colspan="4">Target of stance</th>
  </tr>
  <tr>
    <th>Entities or Topics</th>
    <th>Events or Rumors</th>
    <th>Fact Triples</th>
    <th>Factual Claims</th>
  </tr>
  <tr>
    <td>Favorability</td>
    <td>SemEval-2016 (Mohammad et al., 2016); VAST (Allaway and McKown, 2020); P-Stance (Li et al., 2021); (Grimminger and Klinger, 2021); (Aleksandric et al., 2024)</td>
    <td>MGTAB (Shi et al., 2023)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Likelihood</td>
    <td>WT-WT (Conforti et al., 2020)</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Truthfulness</td>
    <td>PHEME (Zubiaga et al., 2016); SemEval-2017 (Derczynski et al., 2017); SemEval-2019 (Gorrell et al., 2019)</td>
    <td>NewsClaims (Reddy et al., 2022); FactBank (Sauri and Pustejovsky, 2009); (Diab et al., 2009)</td>
    <td>Emergent (Ferreira and Vlachos, 2016); FNC-1 (Pomerleau and Rau, 2017); COVIDLies (Hossain et al., 2020); This work (TSD-CT)</td>
    <td></td>
  </tr>
</table>

Table 1: Various definitions of stance differ in the type, utterance, and target of stance.

Utterance of Stance. Table 1 compares the definitions of stance across existing datasets, listing dataset names (if available) and their corresponding references. Our ensuing discussion refers to these names whenever applicable. Researchers have developed various methods and models for these datasets and their respective stance detection tasks. Such models are referenced throughout this paper but not necessarily in Table 1.

To distinguish between utterances in existing definitions and our own, Table 1 uses two colors — brown for news articles and blue for social media posts (primarily tweets, though SemEval-2019 includes Reddit posts and VAST considers comments on news websites). In stance detection, the prevalence of informal language traits, such as slang, abbreviations and misspellings, poses greater challenges (Al Qundus et al., 2020; Smirnov, 2017) compared to news articles, which predominantly adhere to formal language conventions.

Target of Stance. Table 1 identifies four primary types of stance targets in prior studies: 1) entities (e.g., Hillary Clinton) and topics (e.g., “legalization of abortion”) in SemEval-2016, VAST, P-Stance, (Grimminger and Klinger, 2021), and (Aleksandric et al., 2024); 2) events (e.g., mergers and acquisitions of companies in WT-WT and Japan’s nuclear wastewater release in MGTAB) and rumors — true or false eventually — in PHEME, SemEval-2017 and SemEval-2019 (e.g., the rumor about a second shooter in the 2014 Parliament Hill shootings in Ottawa); 3) factual claims (e.g., news claims in Emergent, news headlines in FNC-1, and COVID-19 related misconceptions in COVIDLies); and 4) fact triples (i.e., subject-predicate-object triples) extracted from the utterance itself. For example, in NewsClaims and FactBank (Sauri and Pustejovsky, 2009), the stance is about whether an utterance affirms or refutes a particular fact triple, e.g., (Vitamin C, cure, COVID-19 virus). Similarly, Diab et al. (2009) explored committed belief by evaluating whether a writer conveys belief in the truth of a fact triple, such as (GM, layoff, workers), within their utterance.

Note that the datasets also vary in the number of targets, ranging from fewer than ten to several thousand. Some datasets have a small number of targets, such as MGTAB (1 target), P-Stance and (Grimminger and Klinger, 2021) (3 targets), WT-WT (5 targets), and SemEval-2016 (6 targets). Others contain dozens to hundreds of targets, including COVIDLies (86 misconceptions), Emergent (300 news claims), NewsClaims (889 fact triples), and PHEME, SemEval-2017 and SemEval-201 (several hundred latent rumors as their source tweets, each mentioning a rumor, are on such a scale). Finally, some datasets feature thousands of targets, such as our TSD-CT (1,520 factual claims), FNC-1 (2,542 news headlines), FactBank (4,801 fact triples), and VAST (5,634 topics).

Type of Stance. The type of stance in various existing definitions falls into three main categories: 1) likelihood of target events occurring (e.g., WT-WT); 2) favorability — determining whether the stance expressed in an utterance is in favor of or against a given target (e.g., SemEval-2016, VAST, P-Stance, (Grimminger and Klinger, 2021), (Aleksandric et al., 2024), and MGTAB); 3) the truthfulness of a rumor (PHEME, SemEval-2017 and SemEval-2019), a news headline (FNC-1), a fact triple (NewsClaims, FactBank and (Diab et al., 2009)), or a claim (Emergent and COVIDLies). These stance types are not equivalent and therefore require distinct detection models. This is clearly illustrated by the upper-left example in Figure 1 — the tweet conveys a negative favorability stance but a positive truthfulness stance toward the claim.

Among the aforementioned datasets and
definitions of stance, TSD-CT most closely resembles COVIDLies, as both focus on tweets’ stance toward the truthfulness of factual claims. One key distinction is that COVIDLies (https://ucinlp.github.io/covid19/misinformation/) exclusively focuses on COVID-19-related misconception claims, which were manually examined and rephrased and tend to be simple and short. On the contrary, the claims in TSD-CT, sourced from PolitiFact, are more complex both syntactically and semantically, covering a broader range of topics relevant to fact-checkers. It is also worth noting that COVIDLies only includes false claims as targets, whereas TSD-CT contains a mix of true and false claims. The expression of truthfulness stance may differ depending on whether the claim is true or false.

3 Methodology

The design of the RATSD framework hinges on two key challenges associated with the data: 1) Both claim \( c \) and tweet \( t \) are standalone sentences that often lack sufficient context, making it difficult for a classification model to make an informed decision. 2) Tweets frequently contain acronyms, hashtags, and slang, which pose challenges for the classification model to interpret accurately.

RATSD counters these challenges with two innovative data augmentation ideas, both leveraging LLMs’ abilities. One is to employ RAG (retrieval augmented generation) to retrieve relevant contextual information from external knowledge corpora to compensate for the inherent lack of context. The other is to synthesize an analysis of the tweet \( t \) based on the retrieved context. The tweet analysis directly incorporates LLM’s perspective on \( t \)’s truthfulness stance toward \( c \). Additionally, it helps mitigate the challenges posed by the aforementioned informal language. Recent advancements have demonstrated the effectiveness of RAG in knowledge retrieval (Lewis et al., 2020; Wang et al., 2023) and LLMs’ success in text analysis (Tang et al., 2024).

Reflecting this design, the RATSD framework as depicted in Figure 2 comprises three main components: the construction of external knowledge corpora (dashed blue box), the LLM-enabled data augmentation which includes RAG and tweet analysis generation (dotted orange box), and the fine-tuning of truthfulness stance classification model (dash-dotted yellow box). The rest of this section discusses these components in detail.

3.1 Knowledge Corpora Construction

Two knowledge corpora were constructed to provide contextual knowledge for other components in RATSD, one for claims and the other for tweets.

The first knowledge corpus, denoted \( \mathcal{D}_C \), encompasses 52,596 synthesized documents for factual claims. It is worth noting that, although the claim-tweet pairs in the TSD-CT dataset include claims from PolitiFact only, the knowledge corpus incorporates claims and corresponding fact-checks published by seven fact-checking websites from 1995 to 2023. Additionally, some claims were fact-checked by multiple websites. Given a claim \( c \), the corresponding synthesized document \( d_c \) was constructed by concatenating excerpts from fact-checks (i.e., articles) on the claim. Each excerpt includes the following information: the claim \( c \) itself, the name of the claimant and their profile description from the fact-checking website, the date and location of the claim, the publication date of the fact-check article, the summary of the fact-checking ruling provided in the article, and the main body of the article. The resulting \( d_c \) typically ranges from 10,000 to 30,000 words in length.

The second knowledge corpus, \( \mathcal{D}_T \), consists of 8,236 synthesized documents for tweets posted from 2010 to 2023. Given a tweet \( t \), the corresponding document \( d_t \) was constructed by concatenating the following information: the raw HTML content of all web pages linked in the tweet, the profile description (retrieved using Twitter API) of the account that posted the tweet, and information (name and description, from Twitter API) about the entities mentioned in \( t \).

3.2 Contextual Knowledge Generation

Using the constructed knowledge corpora \( \mathcal{D}_C \) and \( \mathcal{D}_T \), RATSD generates contextual knowledge in the form of a document \( e_c \) for \( c \) and a document \( e_t \) for \( t \), given a claim-tweet pair (\( c, t \)). Note that the set of claims from the claim-tweet pairs in TSD-CT, i.e., the set of \( c \) for which \( e_c \) was generated (let us call it \( C_1 \)), is not identical to the set of \( c \) for which \( d_c \) was constructed in forming \( \mathcal{D}_C \) (call it \( C_2 \)). Specifically, \( C_2 \) is a much larger superset of \( C_1 \), as each \( c \in C_1 \) is sourced from PolitiFact while each \( c \in C_2 \) can be from any of the seven fact-checking websites. The rationale was that useful contextual knowledge for a claim can come from not only the claim itself but also other relevant claims. Similarly, the set
of tweets from TSD-CT is the annotated subset of tweets in \( d_t \) (see Section 4.1 for the tweet collection process of \( d_t \)).

The \( e_c \) and \( e_t \) are critical for accurate truthfulness stance detection. Particularly, such contextual information is instrumental in mitigating LLM-generated hallucination (Ji et al., 2023; Yao et al., 2023; Tonmoy et al., 2024). The generation process of \( e_c \) and \( e_t \) follows four steps: 1) document preprocessing, 2) relevant document selection, 3) relevant chunk retrieval, and 4) prompting LLM.

Document Preprocessing. All the documents in both \( \mathcal{D}_C \) and \( \mathcal{D}_T \) were segmented into smaller chunks (i.e., continuous sequence of tokens), each with a token size of 512. For each chunk, we used the BAAI general embedding (BGE) model (Xiao et al., 2024) to generate its text embeddings. The BGE model, being a lightweight, pre-trained model, has demonstrated strong performance in the text embedding leaderboard (Muennighoff et al., 2023).

Relevant Document Selection. We used a keyword-based approach to select relevant documents for \( c \) from the claim knowledge corpus \( \mathcal{D}_C \). Nouns, verbs, and adjectives were extracted from \( c \). Jaccard similarity between the extracted words and each document \( d_{c_i} \in \mathcal{D}_C \) was calculated. Top 10 documents based on the similarity scores were selected as relevant documents for \( c \). The same approach was used to select the 10 most similar documents for \( t \) from the tweet knowledge corpus \( \mathcal{D}_T \). This step excludes irrelevant documents from consideration and thus reduces noise in the next step. Furthermore, it also helps reduce the computational cost of LLM retrieval by limiting it to a smaller set of documents.

Relevant Chunk Retrieval. Not all the chunks of the selected top documents are relevant to \( c \) and \( t \). Given each \( c \) and \( t \), the top 10 most relevant chunks were retrieved. For retrieving relevant chunks, we used the BGE embeddings and applied cosine similarity to measure the semantic alignment between each chunk and a text query based on \( c \) (or \( t \)). The query is essentially the same prompt instruction used in prompting the LLM, as follows.

Prompting the LLM. To generate high-quality \( e_c \) and \( e_t \), we designed a prompt. It includes both \( c \) and \( t \), along with the specific instruction to generate relevant contextual knowledge. The top portion of Figure 5 in the Appendix shows an example prompt. As described above, this prompt was used to find relevant chunks based on their vector embeddings. Once the most relevant chunks have been retrieved, they are fed into the LLM along with the same prompt to generate the contextual knowledge \( e_c \) and \( e_t \) for the factual claim \( c \) and the tweet \( t \). An example of \( e_c \) and \( e_t \) can be found in Figure 5.

3.3 Stance Analysis

Utilizing the contextual knowledge described above, RATSD generates the stance analysis for each claim-tweet pair (\( c, t \)). Specifically, an LLM is prompted using \( c, t, e_c \) and \( e_t \) as the input to generate a narrative of \( t \)'s truthfulness stance regarding \( c \). We use \( a \) to denote the generated stance analysis. The prompt instruction and an example input can be found in Figure 6.

When training the stance detection model, \( a \) will replace \( t \) in the input claim-tweet pair, as detailed in Section 3.4. This approach is helpful for producing the final stance classification model in three ways. First, it leverages the power of LLMs to analyze the tweet's stance and the analysis is directly included in training the detection model. (Section 5 reports experiment results comparing our approach with directly prompting LLMs.) Second, the analysis incorporates additional context from \( e_c \) and \( e_t \) which is not in the original \( t \). Finally, this approach helps reduce the informality in tweet content (e.g., acronyms, hashtags, slang, and nickname references of entities) which otherwise presents a challenge in training the model.

3.4 Classification Model

RATSD produces the final stance label by using a fine-tuned LLM as a classifier. Given a claim-tweet pair (\( c, t \)) as well as the corresponding \( a, e_c \) and \( e_t \) generated by other components described earlier, the LLM converts the \( i \)-th input into a vector representation \( h_i = ([CLS], a_i, [SEP], c_i, [SEP], e_{ti}, e_{ci}) \). The vector is fed into a single fully connected layer and a softmax layer to produce the probability distribution of stance orientation labels \( \{ \hat{s}_i^{\oplus}, \hat{s}_i^{\ominus}, \hat{s}_i^{\otimes} \} = softmax(W h_i + b) \) where \( W \) and \( b \) are trainable parameters. The LLM is optimized by a cross-entropy loss \( \min_{\Theta} \mathcal{L} = - \sum_i \sum_{o \in \{ \oplus, \ominus, \otimes \}} s_i^o \log(\hat{s}_i^o) + \lambda \| \Theta \|_2^2 \) where \( s_i^o \) and \( \hat{s}_i^o \) are the ground-truth probability and predicted probability for stance orientation \( o \) of the \( i \)-th input, \( \Theta \) denotes all trainable parameters of the model, and \( \lambda \) represents the coefficient of \( L_2 \)-regularization. The model parameters are fine-tuned during training and optimized using the
Adam optimizer (Kingma and Ba, 2015). The fine-tuning process involves minimizing the cross-entropy loss between the predicted stance distribution and the ground-truth stance distribution.

4 Creation of the TSD-CT Dataset

4.1 Claim-tweet Pair Collection
We chose factual claims from PolitiFact in our fact-check collection (details in Appendix A.1), excluding those that were phrased as questions. We then used spaCy to extract keywords (nouns, verbs, adjectives, pronouns, and numbers) from the claims. For each claim, we retrieved related tweets via Twitter API v2 using a conjunctive (ANDed) query formed by the extracted keywords from the claim. We filtered out tweets with fewer than 30 characters, as well as retweets, replies, and quotes, to avoid duplicates. To ensure temporal relevance between tweets and factual claims, we restricted the API search to tweets posted within one month before and up to one year after the claim’s publication. This process led to 36,154 claim-tweet pairs.

4.2 Claim-tweet Pair Sanitization
A claim-tweet pair was removed from the collection if it triggers one of the following conditions: (1) the tweet closely resembles the factual claim, with a similarity score higher than 0.9; (2) the tweet was nearly identical to the tweet in another pair (the pair containing the tweet collected earlier was kept and the one later was removed), with a similarity score above 0.8; and (3) the tweet was published by a fact-checker and it fact-checks the claim. For (1) and (2), the similarity scores were calculated by removing links and hashtags from tweets and applying the longest contiguous matching subsequence (LCS) algorithm (Bergroth et al., 2000). Removing these pairs can avoid wasting efforts in annotating similar pairs and can help diversify the dataset. For (3), such pairs were identified using a heuristic rule: the tweet contains the claim and any of the following: a hyperlink to a fact-check, the name of a fact-checking website, or the claimant’s name at the beginning of the tweet. Removing these pairs can avoid wasting efforts in annotating easy cases where the tweet’s stance could be highly accurately labeled according to the fact-checker’s verdict regarding the claim. After removing 30,032 pairs in cases (1) and (2) and 329 pairs in case (3), we were left with 2,283 unique factual claims paired with 5,793 tweets from 5,227 distinct Twitter accounts.

4.3 Claim-tweet Pair Annotation
Human annotation was conducted via an in-house website with detailed instructions, progress monitoring, and compensation based on annotation quality (see Appendix A.2 for details). To identify high-quality annotators, we used 287 carefully selected screening pairs. Each pair received consistent labeling from five researchers. These pairs were mixed with the pairs that need real annotation. They were randomly chosen and presented to an annotator at an average frequency of one in every ten pairs, without the annotator’s knowledge. Annotators were scored based on how well their labels match the experts’ labels on the screening pairs. Annotations from low-quality annotators were excluded from the dataset. A pair’s annotation is considered complete when at least three high-quality annotators contribute, and the majority of their labels are in agreement.

Among all 206 annotators, 30 were deemed high-quality based on the approach mentioned above. A total of 18,584 annotations were collected, with 13,594 from these high-quality annotators. This resulted in 3,105 completed pairs, containing 1,520 unique claims. Of the completed pairs, 216 were labeled as “different topics” and 669 as “problematic.” As explained in Section 2, detecting unrelated pairs (i.e., “different topics”) is a separate task and therefore falls outside the scope of our study, as does the detection of “problematic” pairs. Therefore, while these pairs are included in the released TSD-CT dataset, they were excluded from model training and evaluation.

5 Evaluation

5.1 Experiment Datasets
As noted in Section 2, several benchmark datasets are available for stance detection and only a few of these datasets closely align with our concepts. Therefore, we selected the three most similar benchmark datasets—SemEval-2019, WT-WT, and COVIDLies—for performance comparison, along with our own TSD-CT dataset. However, the stance and class categories are defined and named differently in these datasets. Thus, we merged and renamed labels (see Appendix B) in those datasets to ensure a fair comparison of model performance. The label distributions of SemEval-2019, WT-WT, and COVIDLies are shown in Table 3.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="4">TSD-CT</th>
    <th colspan="4">SemEval-2019</th>
    <th colspan="4">WT-WT</th>
    <th colspan="4">COVIDLies</th>
  </tr>
  <tr>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>⊖</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>M</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>⊖</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>M</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>⊖</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>M</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>⊖</sub></th>
    <th>F<sub>⊕</sub></th>
    <th>F<sub>M</sub></th>
  </tr>
  <tr>
    <td>BUT-FIT</td>
    <td>83.38</td>
    <td>72.00</td>
    <td>65.11</td>
    <td>80.11</td>
    <td>49.09</td>
    <td>50.98</td>
    <td>92.01</td>
    <td>64.03</td>
    <td>81.29</td>
    <td>94.73</td>
    <td>79.29</td>
    <td>85.10</td>
    <td>47.62</td>
    <td>97.82</td>
    <td>23.53</td>
    <td>56.32</td>
  </tr>
  <tr>
    <td>BLCU_NLP</td>
    <td>85.37</td>
    <td>71.43</td>
    <td>63.29</td>
    <td>73.36</td>
    <td><b>70.15</b></td>
    <td>40.00</td>
    <td>88.12</td>
    <td>66.09</td>
    <td>81.02</td>
    <td>94.74</td>
    <td>77.09</td>
    <td>84.28</td>
    <td>52.38</td>
    <td>97.71</td>
    <td>45.46</td>
    <td>65.18</td>
  </tr>
  <tr>
    <td>BERTSCORE+NLI</td>
    <td>88.68</td>
    <td>72.53</td>
    <td>81.04</td>
    <td>80.75</td>
    <td>46.96</td>
    <td>60.67</td>
    <td>91.32</td>
    <td>66.32</td>
    <td>82.02</td>
    <td>95.06</td>
    <td>79.11</td>
    <td>85.39</td>
    <td><b>57.14</b></td>
    <td><b>98.20</b></td>
    <td><b>58.33</b></td>
    <td><b>71.22</b></td>
  </tr>
  <tr>
    <td>BART+NLI</td>
    <td>88.00</td>
    <td>73.42</td>
    <td>74.25</td>
    <td>78.56</td>
    <td>47.96</td>
    <td>51.71</td>
    <td>91.90</td>
    <td>63.86</td>
    <td>82.82</td>
    <td>95.52</td>
    <td>81.75</td>
    <td>86.70</td>
    <td>50.00</td>
    <td>98.00</td>
    <td><b>60.87</b></td>
    <td>69.62</td>
  </tr>
  <tr>
    <td>TESTED</td>
    <td>84.09</td>
    <td>72.37</td>
    <td>67.90</td>
    <td>74.75</td>
    <td>46.43</td>
    <td>58.04</td>
    <td><b>92.08</b></td>
    <td>65.52</td>
    <td>81.75</td>
    <td>94.98</td>
    <td>78.00</td>
    <td>85.91</td>
    <td>40.00</td>
    <td>97.12</td>
    <td>51.85</td>
    <td>62.99</td>
  </tr>
  <tr>
    <td>RATSD<sub>Zephyr</sub></td>
    <td>88.67</td>
    <td>77.38</td>
    <td>80.78</td>
    <td>82.10</td>
    <td>41.71</td>
    <td>55.42</td>
    <td>91.80</td>
    <td>62.97</td>
    <td><b>83.85</b></td>
    <td><b>95.72</b></td>
    <td><b>82.66</b></td>
    <td><b>87.44</b></td>
    <td>51.42</td>
    <td>97.63</td>
    <td>54.55</td>
    <td>67.87</td>
  </tr>
  <tr>
    <td>RATSD<sub>GPT3.5</sub></td>
    <td><b>93.27</b></td>
    <td><b>80.24</b></td>
    <td><b>87.90</b></td>
    <td><b>87.13</b></td>
    <td>56.12</td>
    <td>63.79</td>
    <td>83.67</td>
    <td><b>67.86</b></td>
    <td>75.78</td>
    <td>92.98</td>
    <td>75.07</td>
    <td>81.27</td>
    <td>51.16</td>
    <td>98.06</td>
    <td>52.63</td>
    <td>67.30</td>
  </tr>
</table>

Table 2: Performance comparison on datasets TSD-CT, SemEval-2019, WT-WT, and COVIDLies.

<table>
  <tr>
    <th>Dataset</th>
    <th>(⊕)</th>
    <th>(⊖)</th>
    <th>(⊖)</th>
    <th>Total</th>
  </tr>
  <tr>
    <td>SemEval-2019</td>
    <td>1,184 (13.8%)</td>
    <td>6,784 (79.1%)</td>
    <td>606 (7.1%)</td>
    <td>8,574</td>
  </tr>
  <tr>
    <td>WT-WT</td>
    <td>6,663 (21.0%)</td>
    <td>20,864 (65.7%)</td>
    <td>4,224 (13.3%)</td>
    <td>31,751</td>
  </tr>
  <tr>
    <td>COVIDLies</td>
    <td>670 (9.9%)</td>
    <td>5,748 (85.1%)</td>
    <td>340 (5.0%)</td>
    <td>6,758</td>
  </tr>
  <tr>
    <td>TSD-CT</td>
    <td>1,262 (56.9%)</td>
    <td>451 (20.3%)</td>
    <td>507 (22.8%)</td>
    <td>2,220</td>
  </tr>
</table>

Table 3: Label distribution of SemEval-2019, WT-WT, COVIDLies and TSD-CT datasets.

5.2 Implementation Details

All experiments were conducted using 1 NVIDIA A100 80GB GPU. Due to our limited GPU memory, we applied 8-bit quantization for LLM fine-tuning. Steps 3 and 4 of contextual knowledge generation were implemented using LlamaIndex (Liu, 2022). The classification model in RATSD was fine-tuned using selected hyperparameters. The learning rate was set to 5e-5, balancing convergence speed and stability. We utilized a batch size of 8 for both training and evaluation. The models were trained for three epochs. We applied a weight decay of 0.01. We used GPT-3.5, with a temperature of 0.1 and a maximum output token length of 4,096, for contextual knowledge generation and stance analysis in RATSD.

5.3 Experiment Results

We evaluated the performance of two types of stance detection models: LM-based and LLM-based. And the evaluation was conducted in two different settings: fine-tuning the models for stance detection and applying them directly in a zero-shot setting. Consistent with previous studies, we used F1 scores for each class—denoted as \( F_{⊕} \), \( F_{⊖} \), and \( F_{⊖} \)—and the Macro F1 score (\( F_M \)) as our evaluation metrics.

Fine-tuned Model Performance. We evaluated the performance of RATSD by comparing it to several state-of-the-art stance detection models, including fine-tuned LMs such as pre-trained model (BUT-FIT (Fajcik et al., 2019)), generative pre-trained model (BLCU_NLP (Yang et al., 2019)), domain-adaptive pre-trained model (BERTSCORE+NLI (Hossain et al., 2020), BART+NLI (Reddy et al., 2022) and TESTED (Arakelyan et al., 2023)). In RATSD, we utilize two fine-tuned LLMs: the open-source model Zephyr (Tunstall et al., 2023) and the proprietary model GPT-3.5.

As shown in Table 2, RATSD demonstrates strong performance across all datasets compared to other stance detection models. On the TSD-CT dataset, RATSD<sub>GPT3.5</sub> achieves the highest scores across all metrics. For the SemEval-2019 dataset, RATSD<sub>GPT3.5</sub> surpasses other models in \( F_{⊖} \) score and achieves the highest macro F1 score. RATSD<sub>Zephyr</sub> demonstrates its strength on the WT-WT dataset, where it secures the highest performance across all metrics. While on the COVIDLies dataset, BERTSCORE+NLI and BART+NLI slightly outperform RATSD, RATSD still delivers competitive results. These results suggest that the RATSD models, especially RATSD<sub>GPT3.5</sub>, demonstrate strong performance. However, different fine-tuned LLM in RATSD may excel in specific datasets or stance categories, highlighting the importance of model selection based on the specific task and dataset characteristics.

The improved performance of RATSD can be attributed to two key factors. The first is dataset quality. TSD-CT is annotated under a rigorous quality control mechanism, and our empirical analysis suggests that its quality surpasses that of other benchmark datasets. For instance, we observed that some tweets in SemEval-2019 contain only user mentions, which should have been excluded from the dataset. The second factor is dataset design. For example, the targets in SemEval-2019 consist of rumors embedded in tweets, whereas TSD-CT contains formal factual claims from PolitiFact. Due to the informal nature of the targets in SemEval-2019, models may struggle to comprehend them, leading to challenges in stance detection.
Zero-shot Performance on TSD-CT. To assess the model’s ability to generalize its learning to unseen classes without any prior examples. We conducted zero-shot performance evaluation on the TSD-CT dataset, as shown in Table 4. In the zero-shot setting, models were not trained on any stance detection data, leading to naturally lower performance compared to fine-tuned counterparts. Among the models, RATSDZephyrzero achieves the highest overall performance, with the \( F_M \) of 36.55. This suggests that RATSDZephyrzero is a strong framework for truthfulness stance detection in the zero-shot setting. RATSDZephyrzero outperforms RATSDGPT-3.5zero across most metrics. The results suggest that Zephyr is better suited for zero-shot scenarios, potentially due to its model architecture or the nature of its fine-tuning, which might be better at generalizing to new tasks without task-specific training. Notably, GPT-3.5zero uses direct prompting, as described in Section 3.3, which demonstrates its difficulty in achieving strong performance without fine-tuning.

<table>
  <tr>
    <th>Model</th>
    <th>\( F_\oplus \)</th>
    <th>\( F_\ominus \)</th>
    <th>\( F_\otimes \)</th>
    <th>\( F_M \)</th>
  </tr>
  <tr>
    <td>BUT-FITzero</td>
    <td>12.82</td>
    <td>0.00</td>
    <td>33.88</td>
    <td>15.56</td>
  </tr>
  <tr>
    <td>BLCU NLPzero</td>
    <td>27.05</td>
    <td>0.00</td>
    <td>32.81</td>
    <td>19.95</td>
  </tr>
  <tr>
    <td>BERTSCORE+NLLzero</td>
    <td>6.82</td>
    <td><b>41.71</b></td>
    <td>17.65</td>
    <td>22.06</td>
  </tr>
  <tr>
    <td>BART+NLIzero</td>
    <td>33.55</td>
    <td>40.58</td>
    <td>3.96</td>
    <td>26.03</td>
  </tr>
  <tr>
    <td>TESTEDzero</td>
    <td><b>55.84</b></td>
    <td>38.91</td>
    <td>4.04</td>
    <td>32.93</td>
  </tr>
  <tr>
    <td>GPT-3.5zero</td>
    <td>34.04</td>
    <td>16.81</td>
    <td><b>39.74</b></td>
    <td>30.20</td>
  </tr>
  <tr>
    <td>RATSDZephyrzero</td>
    <td>49.74</td>
    <td>32.14</td>
    <td>27.78</td>
    <td><b>36.55</b></td>
  </tr>
  <tr>
    <td>RATSDGPT-3.5zero</td>
    <td>28.76</td>
    <td>29.71</td>
    <td>33.46</td>
    <td>30.64</td>
  </tr>
  <tr>
    <td rowspan="3">RATSDZephyr</td>
    <td><b>88.67</b></td>
    <td><b>77.38</b></td>
    <td>80.28</td>
    <td><b>82.10</b></td>
  </tr>
  <tr>
    <td>w/o analysis</td>
    <td>87.85</td>
    <td>74.39</td>
    <td><b>81.01</b></td>
    <td>81.08</td>
  </tr>
  <tr>
    <td>w/o context & analysis</td>
    <td>87.16</td>
    <td>75.15</td>
    <td>78.01</td>
    <td>80.11</td>
  </tr>
</table>

Table 4: The zero-shot model performance comparison and ablation study on the TSD-CT dataset.

5.4 Ablation Study
To assess the effectiveness of contextual knowledge generation and stance analysis, we conducted an ablation study with two model variations on the TSD-CT dataset: RATSDZephyr without stance analysis (w/o analysis) and RATSDZephyr without contextual knowledge generation and stance analysis (w/o context & analysis). The results in the bottom three rows of Table 4 reveal the impact of key components on the performance of RATSDZephyr. When stance analysis is removed, both the \( F_\oplus \) and \( F_\ominus \) decline, indicating that stance analysis provides useful additional context for both positive and neutral pairs, although it slightly reduces the performance for the negative class. Further removing contextual knowledge generation results in a drop in performance across all F1 categories. The decline in the \( F_\oplus \) and \( F_\ominus \) indicates that contextual knowledge generation is crucial in handling neutral or negative pairs. The decrease in \( F_\oplus \), although smaller, still highlights the contextual knowledge’s contribution to detecting positive class.

6 Related Work
The concept of truthfulness stance was first introduced by Zhu et al. (2022). We refine this definition further, providing additional details and proposing a novel conceptual framework that encompasses other types of stance definitions. While Zhang et al. (2024b) also explored truthfulness stance, their work primarily focused on applying a truthfulness stance detection model to climate change-related claims. In contrast, our study centers on creating a new dataset covering general topics and introduces a novel application of RAG in truthfulness stance detection.

Methodologically, recent studies show that LLM such as GPT-3.5 can achieve impressive results in stance detection (Zhang et al., 2022, 2023). Researchers have explored the incorporation of contextual knowledge to enhance stance detection model performance. For example, Li et al. (2023) developed a topic-based heuristic algorithm to retrieve relevant Wikipedia documents for input instances. However, their approach does not utilize RAG or stance analysis. Zhang et al. (2024a) and Singal et al. (2024) proposed methods to prompt LLMs to generate contextual knowledge, but their approaches do not leverage external knowledge corpora. Additionally, the stance considered in these works (Li et al., 2023; Zhang et al., 2024a; Singal et al., 2024) focuses on favorability rather than truthfulness.

7 Conclusion
This paper revisits stance detection by proposing a conceptual framework of stance definitions and focuses on the concept of truthfulness stance. It introduces a newly annotated dataset (TSD-CT) and presents RATSD, an LLM-powered framework that leverages RAG for truthfulness stance detection. RATSD outperformed state-of-the-art models on TSD-CT and existing benchmark datasets. This work provides key concepts, methods, and a dataset to advance research on public opinion analysis and misinformation mitigation.
Limitations

One major limitation of RATSD is its inability to differentiate between neutral and no-stance claim-tweet pairs. For simplicity, these two classes were combined into a single category due to their inherent similarities. As a result, RATSD loses the ability to capture this finer distinction, which may be valuable in applications where distinguishing between neutral stance and no stance is important.

Another limitation of RATSD is its inability to assess the truthfulness stance of individual sub-claims within a single factual claim. For example, in “We won and we won a lot,” which comprises two sub-claims (“We won” and “We won a lot”), RATSD treats the entire claim as a single unit. As a result, RATSD may oversimplify the semantic complexity of multi-part claims, potentially overlooking differences in stance that could exist across sub-claims.

Furthermore, while RATSD is capable of processing multi-sentence claims and even paragraphs, it was trained and evaluated on TSD-CT, which comprises exclusively single-sentence claims. Since TSD-CT was not designed to handle multi-sentence factual claims, the model’s performance on such input may be less reliable. This underscores the need for a more comprehensive dataset that includes multi-sentence claims to fully assess RATSD’s capabilities in these scenarios.

Ethics and Risks

Bias and Misinformation. One significant risk involves the potential for bias in the model’s predictions, arising from both the training data and annotations. The dataset may reflect biases from social media posts or annotators themselves. Additionally, while RATSD is designed to detect truthfulness stances, there is a risk that users might misinterpret the model’s output as a measure of truthfulness of factual claims. This could inadvertently amplify misinformation if the model’s stance detection is taken as an endorsement or validation of misinformation. Mitigating these risks requires careful curation of training data and transparency in how the model’s output should be interpreted.

Impact on Public Discourse. By automating stance detection, RATSD has the potential to influence public discourse, particularly in highly polarized contexts, such as politics, public health, or social justice. Misuse of the system could lead to the selective presentation of results, thereby reinforcing biased narratives or suppressing dissenting opinions. To prevent this, it is critical to ensure that the model’s use is transparent and its limitations are well understood, emphasizing that it is a supportive tool for human decision-making rather than a replacement.

Privacy and Data Protection. Privacy concerns are especially important when analyzing social media posts, as users often share personal opinions in semi-public spaces. The application of automated tools based on RATSD must comply with data protection and privacy regulations, ensuring responsible handling of user information. This includes respecting user consent, anonymizing data, and adhering to legal and ethical standards.

Ethical Considerations in Annotation. Ethical considerations extend to the annotation process. The content of the tweets may negatively impact annotators, exposing them to harmful or distressing material. To address this, we obtained Institutional Review Board (IRB) approval before recruiting annotators and implemented guidelines to protect their well-being.

References

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. GPT-4 technical report. arXiv preprint arXiv:2303.08774.

Jamal Al Qundus, Adrian Paschke, Shivam Gupta, Ahmad M Alzoubi, and Malik Yousef. 2020. Exploring the impact of short-text complexity and structure on its quality in social media. Journal of Enterprise Information Management, 33(6):1443–1466.

Ana Aleksandric, Henry Isaac Anderson, Anisha Dangal, Gabriela Mustata Wilson, and Shirin Nilizadeh. 2024. Analyzing the stance of facebook posts on abortion considering state-level health and social compositions. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 15–28.

Emily Allaway and Kathleen R. McKeown. 2020. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 8913–8931.

Nora Saleh Alturayef, Hamzah Luqman, and Moataz A. Ahmed. 2023. A systematic review of machine learning techniques for stance detection and its applications. Neural Comput. Appl., 35(7):5113–5144.
Erik Arakelyan, Arnav Arora, and Isabelle Augenstein. 2023. Topic-guided sampling for data-efficient multi-domain stance detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pages 13448–13464.

Lasse Bergroth, Harri Hakonen, and Timo Raita. 2000. A survey of longest common subsequence algorithms. In Proceedings Seventh International Symposium on String Processing and Information Retrieval, pages 39–48.

Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. Will-they-won’t-they: A very large dataset for stance detection on twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1715–1724.

Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017. SemEval-2017 task 8: RumourEval: Determining rumour veracity and support for rumours. In Proceedings of the 11th International Workshop on Semantic Evaluation, pages 69–76.

Mona Diab, Lori Levin, Teruko Mitamura, Owen Rambow, Vinodkumar Prabhakaran, and Weiwei Guo. 2009. Committed belief annotation and tagging. In Proceedings of the Third Linguistic Annotation Workshop (LAW III), pages 68–73.

Daniela V Dimitrova and Jörg Matthes. 2018. Social media in political campaigning around the world: Theoretical and methodological challenges.

Fred Dretske. 1988. The stance stance. Behavioral and Brain Sciences, 11(3):511–512.

Yogesh K Dwivedi, Elvira Ismagilova, D Laurie Hughes, Jamie Carlson, Raffaele Filieri, Jenna Jacobson, Varsha Jain, Heikki Karjaluoto, Hajer Kefi, Anjala S Krishen, et al. 2021. Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59:102168.

Ullrich KH Ecker, Stephan Lewandowsky, John Cook, Philipp Schmid, Lisa K Fazio, Nadia Brashier, Panayiota Kendeou, Emily K Vraga, and Michelle A Amazeen. 2022. The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1):13–29.

Martin Fajcik, Pavel Smrz, and Lukás Burget. 2019. BUT-FIT at semeval-2019 task 7: Determining the rumour stance with pre-trained deep bidirectional transformers. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1097–1104.

William Ferreira and Andreas Vlachos. 2016. Emergent: a novel data-set for stance classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, pages 1163–1168.

Wael H Gomaa, Aly A Fahmy, et al. 2013. A survey of text similarity approaches. International Journal of Computer Applications, 68(13):13–18.

Genevieve Gorrell, Elena Kochkina, Maria Liakata, Ahmet Aker, Arkaitz Zubiaga, Kalina Bontcheva, and Leon Derczynski. 2019. SemEval-2019 task 7: RumourEval, determining rumour veracity and support for rumours. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 845–854.

Lara Grimminger and Roman Klinger. 2021. Hate towards the political opponent: A twitter corpus study of the 2020 US elections on the basis of offensive speech and stance detection. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 171–180.

Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2022. A survey on stance detection for mis- and disinformation identification. In Findings of the Association for Computational Linguistics, pages 1259–1277.

Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDlies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19.

Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023. Towards mitigating LLM hallucination via self reflection. In Findings of the Association for Computational Linguistics, pages 1827–1843.

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations.

Moshe Koppel and Jonathan Schler. 2006. The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2):100–109.

Dilek Küçük and Fazli Can. 2020. Stance detection: A survey. ACM Computing Surveys (CSUR), 53(1):1–37.

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.

Ang Li, Bin Liang, Jingqian Zhao, Bowen Zhang, Min Yang, and Ruifeng Xu. 2023. Stance detection on social media with background knowledge. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15703–15717.
Yingjie Li, Tiberiu Sosea, Aditya Sawant, Ajith Jayaraman Nair, Diana Inkpen, and Cornelia Caragea. 2021. P-Stance: A large dataset for stance detection in political domain. In Findings of the Association for Computational Linguistics, pages 2355–2365.

Jerry Liu. 2022. LlamaIndex.

Sahil Loomba, Alexandre De Figueiredo, Simon J Piatak, Kristen De Graaf, and Heidi J Larson. 2021. Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa. Nature human behaviour, 5(3):337–348.

Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation, pages 31–41.

Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2023. MTEB: massive text embedding benchmark. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2006–2029.

Katherine Ognyanova, David Lazer, Ronald E Robertson, and Christo Wilson. 2020. Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power. Harvard Kennedy School Misinformation Review.

Dean Pomerleau and Delip Rao. 2017. Fake News Challenge Stage 1 (FNC-1): Stance Detection. http://www.fakenewschallenge.org.

Revanth Ganti Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi R. Fung, Kathryn Conger, Ahmed Elsayed, Martha Palmer, Preslav Nakov, Eduard H. Hovy, Kevin Small, and Heng Ji. 2022. Newsclaims: A new benchmark for claim detection from news with attribute knowledge. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6002–6018.

Roser Saurf and James Pustejovsky. 2009. Factbank: a corpus annotated with event factuality. Language resources and evaluation, 43:227–268.

Shuhao Shi, Kai Qiao, Jian Chen, Shuai Yang, Jie Yang, Baojie Song, Linyuan Wang, and Bin Yan. 2023. MGTAB: A multi-relational graph-based Twitter account detection benchmark. arXiv preprint arXiv:2301.01123.

Ronit Singal, Pransh Patwa, Parth Patwa, Aman Chadha, and Amitava Das. 2024. Evidence-backed fact checking using RAG and few-shot in-context learning with LLMs. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 91–98, Miami, Florida, USA. Association for Computational Linguistics.

Ivan Smirnov. 2017. The digital flynn effect: Complexity of posts on social media increases over time. In Social Informatics: 9th International Conference, pages 24–30. Springer.

Pawel Sobkowicz, Michael Kaschesky, and Guillaume Bouchard. 2012. Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web. Government Information Quarterly, 29(4):470–479.

Victor Suarez-Lledo and Javier Alvarez-Galvez. 2021. Prevalence of health misinformation on social media: systematic review. Journal of Medical Internet Research, 23(1):e17187.

Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, and Furu Wei. 2024. Not all metrics are guilty: Improving NLG evaluation by diversifying references. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6596–6610, Mexico City, Mexico. Association for Computational Linguistics.

SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. 2024. A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313.

Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al. 2023. Zephyr: Direct distillation of lm alignment. arXiv preprint arXiv:2310.16944.

Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, et al. 2023. Survey on factuality in large language models: Knowledge, retrieval and domain-specificity. arXiv preprint arXiv:2310.07521.

Jiapeng Wang and Yihong Dong. 2020. Measurement of text similarity: a survey. Information, 11(9):421.

Duncan J Watts, David M Rothschild, and Markus Mobius. 2021. Measuring the news and its impact on democracy. Proceedings of the National Academy of Sciences, 118(15):e1912443118.

Tom Willaert, Paul Van Eecke, Katrien Beuls, and Luc Steels. 2020. Building social media observatories for monitoring online opinion dynamics. Social Media+ Society, 6(2):2056305119898778.

Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. 2024. C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pages 641–649.
Ruoyao Yang, Wanying Xie, Chunhua Liu, and Dong Yu. 2019. Bclu_nlp at semeval-2019 task 7: An inference chain-based GPT model for rumour evaluation. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1090–1096.

Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, and Li Yuan. 2023. LLM lies: Hallucinations are not bugs, but features as adversarial examples. arXiv preprint arXiv:2310.01469.

Bowen Zhang, Daijun Ding, Zhichao Huang, Ang Li, Yangyang Li, Baqouan Zhang, and Hu Huang. 2024a. Knowledge-augmented interpretable network for zero-shot stance detection on social media. IEEE Transactions on Computational Social Systems.

Bowen Zhang, Daijun Ding, and Liwen Jing. 2022. How would stance detection techniques evolve after the launch of chatgpt? arXiv preprint arXiv:2212.14548.

Bowen Zhang, Xianghua Fu, Daijun Ding, Hu Huang, Yangyang Li, and Liwen Jing. 2023. Investigating chain-of-thought with chatgpt for stance detection on social media. arXiv preprint arXiv:2304.03087.

Daniel Yue Zhang, Jose Badilla, Yang Zhang, and Dong Wang. 2018. Towards reliable missing truth discovery in online social media sensing applications. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 143–150.

Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Jacob Devasier, and Chengkai Li. 2024b. Granular analysis of social media users’ truthfulness stances toward climate change factual claims. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 233–240, Bangkok, Thailand. Association for Computational Linguistics.

Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohammad Samiul Saeef, Paras Pathak, and Chengkai Li. 2021. A dashboard for mitigating the COVID-19 misinfodemic. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 99–105, Online. Association for Computational Linguistics.

Zhengyuan Zhu, Zeyu Zhang, Foram Patel, and Chengkai Li. 2022. "detecting stance of tweets toward truthfulness of factual claims". In "Proceedings of the 2022 Computation+Journalism Symposium".

Arkaitz Zubiaga, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie. 2016 Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS one, 11(3):e0150989.

A Creation of the TSD-CT Dataset

A.1 Fact-check Collection

We developed a tool to collect the fact-checks from seven well-known fact-checking websites, including AFP Fact Check, AP Fact Check, FactCheck.org, FullFact, Metafact, PolitiFact, and Snopes. Table 5 provides various statistics of this collection. The collected data is coded using Claim-Review’s data schema, a widely adopted standard for structuring fact-checks. The data schema includes fields such as Publisher, ClaimReviewed, Summary, Review, Verdict, Author, ClaimPublishedDate, FactcheckPublishedDate, ThumbnailURL, URL and Tags. Particularly, the Summary field provides a summary of the fact-check, while the Review field contains the main body of the fact-checking article, including background information and the evidence supporting the verdict.

The factual claims for claim-tweet pair annotation were sourced exclusively from PolitiFact, as it offers the largest and most structured fact-check collection. This choice ensured consistency in the annotation interface and helped reduce costs.

A.2 Details of Claim-tweet Pair Annotation

Annotation Website. The human annotation was conducted through our in-house website (https://idir.uta.edu/stance_annotation/), as shown in Figure 4. For each claim-tweet pair, the annotation task is to choose one of the five distinct options, as shown in the figure. These options correspond to the positive stance, neutral/no stance, negative stance, unrelated pairs (“different topics”) in Figure 3, and an additional “problematic” option which allows annotators to flag tweets that are created for sarcasm only or contain invalid links.

To help annotators better understand the task, the website includes a detailed instruction page that provides a clear definition of the task along with thorough explanation of each classification class. For each class, it provides three examples claim-tweet pairs, each accompanied by the correct class and analysis of the choices. Additionally, we developed an administrative progress monitoring page to monitor the overall progress, track annotator performance, and examine detailed annotation history of individual claim-tweet pairs.

Annotators. During annotator recruitment, we distributed flyers and emailed announcements across the campus of our university. All annotators
<table>
  <tr>
    <th>DataSource</th>
    <th>AFP Fact Check</th>
    <th>AP Fact Check</th>
    <th>FactCheck.org</th>
    <th>FullFact</th>
    <th>Metafact</th>
    <th>PolitiFact</th>
    <th>Snopes</th>
  </tr>
  <tr>
    <td>Claims</td>
    <td>0<sup>*</sup></td>
    <td>297</td>
    <td>0<sup>*</sup></td>
    <td>2,783</td>
    <td>3,428</td>
    <td>21,023</td>
    <td>18,097</td>
  </tr>
  <tr>
    <td>Review Summary</td>
    <td>4,204</td>
    <td>297</td>
    <td>3,452</td>
    <td>2,783</td>
    <td>0<sup>*</sup></td>
    <td>21,023</td>
    <td>2,638</td>
  </tr>
  <tr>
    <td>Review</td>
    <td>4,304</td>
    <td>297</td>
    <td>3,452</td>
    <td>2,783</td>
    <td>3,428</td>
    <td>21,022</td>
    <td>18,474</td>
  </tr>
  <tr>
    <td>Verdict</td>
    <td>0<sup>*</sup></td>
    <td>225</td>
    <td>0<sup>*</sup></td>
    <td>0<sup>*</sup></td>
    <td>3,428</td>
    <td>21,023</td>
    <td>13,947</td>
  </tr>
</table>

Table 5: Numbers of claims, review summaries, reviews, and verdicts in the fact-check collection. * Not all websites follow a consistent structure in their fact-checks. For instance, AFP Fact Check and FactCheck.org do not separately list the claims they fact-check. In such cases, a document \( d_c \) is generated in the knowledge corpus to represent the latent claim \( c \) in each fact-check article.

![Screenshot of the annotation interface showing a blog post about New Zealand's euthanasia for COVID patients, with options for stance annotation](page_370_624_1002_370.png)

Figure 4: The annotation interface.

were at least 18 years old and fluent in English. Compensation for their work was provided in the form of gift cards. Their earnings were determined by their annotation quality, with the potential to earn up to 20 US cents for each claim-tweet pair.

B Merging and Renaming Dataset Labels

We renamed “support” as “positive” and “deny” as “negative” for the SemEval-2019 dataset. We merged the “comment” and “query” classes into a single “neutral/no stance” class because “query” is included in the “neutral” class in our conceptual framework. For WT-WT, we renamed “support” as “positive,” “refute” as “negative,” and removed the “unrelated” category as “unrelated” falls outside the scope of our research. For the COVIDLies dataset, we renamed “agree” as “positive,” “disagree” as “negative” and “no stance” as “neutral/no stance.”

C Challenging Examples in Truthfulness Stance Detection

To highlight the nuances among our stance labels within our conceptual framework, as well as the challenges encountered during the truthfulness stance annotation process, we present six challenging examples in Figure 7.

D Use of AI Assistants

Some of our code was developed using GitHub Copilot, and the writing was polished using ChatGPT and Grammarly.
Prompt: Consider this factual claim: (California introduces new bill that would allow mothers to kill their babies up to 7 days after birth.). Please provide the details about the claimant, enumerate entities along with their information, and provide related information regarding those entities.
Consider this tweet: (California introduces new bill that would allow mothers to kill their babies up to 7 days after birth. A newly proposed bill would legalize the murder of children up to nine months gestation and in the week(s) after birth.). Please enumerate entities along with their information and provide related details regarding those entities. Please generate the context knowledge for the factual claim and tweet separately in a structured way.

Contextual knowledge for factual claim:

Factual Claim:
"California introduces new bill that would allow mothers to kill their babies up to 7 days after birth."

Claimant:
Facebook Posts

Source: A viral Facebook post dated March 31, 2022.
Description: The post contained a headline that falsely claimed that California lawmakers introduced a bill allowing mothers to kill newborns up to seven days after birth. The post was flagged as part of Facebook’s efforts to combat misinformation.
Key Entities:
California Assembly Bill 2223 (AB 2223)

Description: Real bill introduced by Assembly Member Buffy Wicks. It does not allow for killing newborns but seeks to protect individuals from criminal prosecution or civil liability for pregnancy-related outcomes, such as stillbirth, miscarriage, or abortion. It includes language to clarify that deaths related to pregnancy should not be criminalized.
Purpose: To prevent parents from being prosecuted or investigated for pregnancy loss or newborn deaths due to pregnancy-related complications.
Assembly Member Buffy Wicks
Affiliation: Democrat, California Assembly Member, representing Oakland.
Role: Author of AB 2223, Wicks emphasizes the bill’s intent to protect reproductive rights and prevent wrongful prosecutions linked to pregnancy loss or newborn death due to pregnancy-related causes.
Facebook’s Misinformation Policy
Action: The claim was flagged by Facebook’s team as part of its broader effort to prevent the spread of false news. The post was identified as misleading.
Chelsea Becker and Adora Perez

Description: Two California women who were prosecuted after delivering stillborn babies. Their cases highlighted the legal issues surrounding pregnancy loss, which Wicks’ bill aims to address.
Additional Information:
The false claim stemmed from a misunderstanding or deliberate misrepresentation of the term “perinatal” in the bill, which refers to a period following pregnancy. The bill would not protect individuals who intentionally harm newborns.
Verdict: False
PolitiFact Rating: The claim that the bill would allow for the killing of babies is categorically false. The bill aims to prevent investigations or prosecutions for pregnancy-related outcomes, not to legalize murder.

Contextual knowledge for Tweet:

Tweet:
"California introduces new bill that would allow mothers to kill their babies up to 7 days after birth. A newly proposed bill would legalize the murder of children up to nine months gestation and in the week(s) after birth."

Tweet User Profile:
"The last non-communist in California. Bible-believing Christian. Editor: The Liberty Daily and America First Report."

Affiliation: Describes themselves as a conservative, Christian editor for The Liberty Daily and America First Report, likely reflecting a far-right political stance.
Focus: The profile suggests a conservative, possibly religious-based opposition to legislation like AB 2223, aligning with anti-abortion or anti-reproductive-rights views.
Key Entities:
The Liberty Daily

Description: A conservative online news outlet known for its far-right perspectives on political and social issues. The platform is often critical of progressive policies, particularly those related to reproductive rights.
Content Focus: Frequently publishes articles that critique liberal or progressive initiatives, including those related to abortion or reproductive health laws. America First Report

Description: Another far-right media outlet with a focus on populist and nationalist views, advocating for America-centric policies and conservative social values.
Editor: The user of the tweet serves as the editor, indicating their significant role in shaping the outlet’s editorial stance on various issues.
Additional Information:
The tweet amplifies a false claim by conflating the legal language of the bill with an inflammatory accusation about legalizing infanticide. The bill (AB 2223) does not permit infanticide; rather, it protects individuals from prosecution in cases of pregnancy-related deaths, clarifying that prosecution should not be pursued in instances like miscarriage or stillbirth due to pregnancy complications.
Verdict: False
Nature of the Tweet: The tweet is based on the same misinformation found in the Facebook post. It falsely portrays AB 2223 as a bill allowing infanticide, which is not the case.

Figure 5: A contextual knowledge generation example.
Psychic utsava let us know fauci has been arrested. Judy mikovits let us know, he's deepstate scum.

Prompt:
Please analyze the tweet to:
(1) reduce the informality of the tweet
(2) indicate whether the tweet believes the claim "Special Forces arrest Deep State Dr. Anthony Fauci." is true or false based on the contextual knowledge.

Contextual knowledge for the claim-tweet pair:
...
Description: Real bill introduced by Assembly Member Buffy Wicks. It does not allow for killing newborns but seeks to protect individuals from criminal prosecution or civil liability for pregnancy-related outcomes, such as stillbirth, miscarriage, or abortion. It includes language to clarify that deaths related to pregnancy should not be criminalized.
...

Stance Analysis

The tweet suggests belief in the claim that Dr. Anthony Fauci, labeled as part of the "Deep State," has been arrested by Special Forces. It references Psychic Utsava as a source confirming the arrest and supports the idea by citing Judy Mikovits, who also portrays Fauci as "deepstate scum." The overall tone implies trust in these sources and the narrative that Fauci's arrest is true.

Figure 6: An example of stance analysis.

Claim: "Ohio student suspended for staying in class during National Walkout Day."
Patriot attorney needed. Reach out to this family.
Ohio student suspended for staying in class during National Walkout Day trueandfit.com/ohio-student-s...
Truthfulness Stance: Positive

Claim: "Paul Ryan has blocked all action to strengthen our gun laws."
Has Paul Ryan blocked all action to strengthen gun laws? ow.ly/sQqB30ITd3B #Middleton #wipolitics
Truthfulness Stance: Neutral

Claim: "Paul Ryan has blocked all action to strengthen our gun laws."
Wisconsin students are marching 50 miles to Paul Ryan's hometown for action on gun laws news360.com/article/447269...
Truthfulness Stance: No Stance

Claim: "People who are vaccinated for the coronavirus cannot spread it to you."
While fully vaccinated people are less likely to transmit the coronavirus to unvaccinated people, it is still possible to spread the virus. You can easily look this up yourself.
Truthfulness Stance: Negative

Claim: "George Floyd's murder was filmed before COVID-19"
I just love listening to my parents say that product manufacturer being made overseas is the reason the Covid-19 and that person that filmed the murder of George Floyd should be charged too (if it's not clear I'm being sarcastic)
Truthfulness Stance: Different Topics

Claim: "If we stopped testing right now, we'd have very few cases, if any."
"If we stopped testing, we'd have very few cases." - Donald Trump
He's right 😊
Truthfulness Stance: Problematic

Figure 7: A few samples of challenging cases in truthfulness stance annotation.

Reviewer paper summaries: The paper introduces LLMTaxo, a novel approach that leverages LLMs to automatically generate hierarchical taxonomies of factual claims from social media content. The framework is designed to aid researchers, fact-checkers, and policymakers by organizing factual claims into a multi-tiered structure. LLMTaxo reduces manual input requirements through the use of few-shot learning techniques and seed taxonomies. The effectiveness of this method is demonstrated across three distinct datasets, focusing on COVID-19 vaccines, climate change, and cybersecurity.
Summary of strengths: While previous research has primarily focused on identifying and verifying misinformation, the taxonomic aspect has received less attention. This paper addresses the crucial task of the creation of taxonomies for factual claims, particularly those originating from social media, to combat the spread of misinformation.

The versatility of LLMTaxo is showcased through its application to three diverse datasets, highlighting its adaptability to different contexts. The approach of combining LLMs with few-shot in-context learning and a seed taxonomy is a well-thought and intuitive choice.

The authors have provided a substantial amount of evaluation metrics and emphasized them significantly.
Summary of weaknesses: The lack of a standardized taxonomy framework creates challenges for objective quality assessment. While human and GPT-4 evaluations are employed, their scope remains constrained, as only 50 claims per dataset are evaluated, risking incomplete performance representation.
While the authors aim to minimize human effort, creating seed taxonomies and learning examples still requires manual annotation, introducing subjectivity and scalability issues.
The proposed taxonomy lacks empirical validation of its real-world effectiveness. There are no demonstrations of improved fact-checking efficiency or policy decision-making using the taxonomy.
Comments, suggestions and typos: Add case studies demonstrating how LLMTaxo benefits fact-checking as claimed. 
Typos: line 103 (few-short)
Overall assessment scores: 3