Research Stance Labeling

• Research Stance Labeling is the systematic annotation and computational identification of an author’s stance—favor, against, or neutral—toward specific targets in text.
• Methodologies range from traditional SVMs using linguistic features to advanced transformer-based models and LLMs, achieving notable performance improvements.
• This research underpins applications in opinion mining, misinformation detection, and sociopolitical polarization analysis, while addressing challenges like ambiguity and data imbalance.

Stance labeling refers to the systematic annotation and computational identification of an author’s position—favor, against, or neutrality—on a particular target entity, issue, or claim as expressed in text. This research problem lies at the intersection of subjectivity analysis, opinion mining, and social computing, and presents distinct methodological challenges that set it apart from sentiment analysis. Stance labeling typically operates on noisy, brief, and often implicit texts (such as tweets) and is foundational to applications that range from public opinion monitoring and misinformation detection to the scalable paper of sociopolitical polarization.

1. Theoretical Foundations and Distinctions

Stance detection is precisely defined as classifying the viewpoint or attitude expressed by the author of a text toward a specific target (e.g., an issue, person, or claim). Unlike sentiment analysis, which measures general affective polarity (positive, negative, neutral), stance detection is concerned with determining the author's alignment regarding a concrete target, independent of the sentiment's sign (Mohammad et al., 2016, Burnham, 2023). For example, a tweet can employ purely negative language towards an opponent, which nevertheless implies a “favor” stance toward the author’s preferred entity.

Several variants of the task exist: stance with respect to an explicit claim, stance toward implicit or inferred topics, and document-level versus utterance-level labeling (Hardalov et al., 2021). Label inventories also vary, ranging from three classes (favor, against, neither) to more fine-grained schemes incorporating discuss, query, support/refute, or irrelevant categories (Zheng et al., 2022, Villa-Cox et al., 2020).

2. Data Resources and Annotation Protocols

2.1 Datasets

A substantial body of research has focused on compiling high-quality labeled corpora:

• Tweet–target pairs: Early datasets, such as that of the SemEval-2016 Task 6, include over 4,100 tweets, each labeled for stance toward one of five targets (e.g., Atheism, Climate Change) and sentiment expressed (Mohammad et al., 2016). Targets may or may not be explicit in the text, challenging annotators to infer stance.
• Multilingual and cross-lingual corpora: Recent advances support large-scale, balanced datasets across languages, e.g., the Stanceosaurus corpus with 28,033 tweets in English, Hindi, and Arabic labeled for five-way stance toward misinformation claims (Zheng et al., 2022), and CIC with parallel Catalan and Spanish data (Zotova et al., 2021).
• Conversational stance: Datasets such as SRQ differentiate between replies and quotes, and label stances at the conversational turn level for more than 5,200 instances (Villa-Cox et al., 2020).
• User-level stance: PolitiSky24 provides 16,044 user-target stance pairs over the entire posting history of 8,467 Bluesky users, augmented with engagement graphs and rationales, supporting a shift from post-level to user-centric analysis (Rostami et al., 9 Jun 2025).

2.2 Annotation Protocols

Annotation schemes may use three-class favor/against/none setups, or more elaborate taxonomies that account for implicit, explicit, neutral, irrelevant, or discussing stances (Zheng et al., 2022). Some protocols include secondary questions to determine if the sentiment is directed at the labeled target or another entity, addressing the ambiguity inherent in conversational and multi-entity texts (Mohammad et al., 2016). Advanced frameworks like ConStance model annotation context, allowing inference over multiple labelings from various information conditions (Joseph et al., 2017). Recently, multi-perspective approaches retain the full distribution of annotator opinions as “soft labels,” better capturing task subjectivity (Muscato et al., 1 Mar 2025).

3. Methodological Approaches

3.1 Feature Engineering and Traditional Learning

Early approaches rely on explicit linguistic features:

• SVMs with n-grams, sentiment lexicons, target presence, and surface features delivered state-of-the-art performance in the SemEval-2016 shared task with an F1 of 70.3 (Mohammad et al., 2016).
• Dependency-based and lexicon-enriched models improve performance in the presence of distant supervision, extracting nuanced opinion roles even in informal text (Misra et al., 2017).

3.2 Exploiting Unlabeled Data

• Distant supervision via stance-indicative hashtags: Automatically collects noisy but high-confidence training examples.
• Graph-based semi-supervised learning: Label propagation and label spreading algorithms leverage graph connectivity among messages to efficiently scale to large rumor datasets, outperforming Gaussian Process baselines and enabling near real-time inference (Giasemidis et al., 2019).
• Network-based user label propagation: Two-stage heuristics first propagate labels across user–hashtag graphs, then refine using user–user interaction graphs with GNNs, enriching text-based embeddings and outperforming LLM zero-shot baselines in stance detection of divisive issues (Melton et al., 16 Apr 2024).

3.3 Deep and Representation Learning

• Transformer-based models (e.g., BERTweet, RoBERTa) and hybrid architectures (RoBERTa + LSTM) are applied to both stance classification and span identification, with the latter enabling identification of stance-taking expressions in academic writing, achieving macro F1 ≈ 0.72 (Eguchi et al., 2023).
• Lexical connotation embeddings: Distantly supervised lexicon creation for subtle ideological signals, with multi-task learning showing statistically significant improvements in stance detection under low-resource regimes (Allaway et al., 2020).
• Joint modeling frameworks: Multi-perspective learning uses annotator distributions as soft targets, resulting in higher F1 scores and improved calibration in controversial topics (Muscato et al., 1 Mar 2025).

3.4 Leveraging LLMs

• Prompting and few-shot inference: Open-sourced LLMs (e.g., T5, Falcon, Llama-2) can compete with in-domain supervised models on benchmark Twitter datasets, but their performance is prompt- and format-dependent, with no single configuration dominating (Cruickshank et al., 2023). Chain-of-thought and multi-persona prompts sometimes boost accuracy, but LLMs require careful handling of output format and may not outperform smaller supervised models.
• LLM-aided annotation: LLMs are used to produce automated stance labels with supporting rationales and text spans, reaching labeling accuracy of 81% for user-level stance in PolitiSky24 (Rostami et al., 9 Jun 2025). Challenges such as instruction sensitivity, bias to label ordering, and instability across paraphrased prompts necessitate advanced techniques like two-hop multi-label instructions and adversarial multi-target sampling (Liu et al., 2023).

4. Evaluation, Analysis, and Performance Metrics

Evaluation typically uses macro-averaged F1 scores over favor and against classes (Mohammad et al., 2016), macro-F1 across all stance categories (Zheng et al., 2022), as well as precision, recall, ROC-AUC, and log-loss in various studies. For example:

• The SVM in (Mohammad et al., 2016) achieved 70.3 F1 compared to 67.8 for the top shared task competitor.
• SANDS semi-supervised architecture reported macro F1 of 0.55 (US dataset) and 0.49 (India dataset), outperforming 17 baselines on imbalanced and noisy data (Dutta et al., 2022).
• PolitiSky24’s LLM-based user-level stance classifier demonstrated 81% accuracy against human annotation (Rostami et al., 9 Jun 2025).
• Multi-perspective models boosted F1 from 57.22 to 61.90 over baseline on controversial news topics, at the expense of lower (but more calibrated) model confidence (Muscato et al., 1 Mar 2025).

Domain adaptation, cross-domain learning, and label-embedding frameworks allow models to adapt across disparate datasets with heterogeneous label sets, improving in-domain (macro-F1 ≈ 65.6) and out-of-domain performance under unsupervised settings (Hardalov et al., 2021).

5. Challenges, Limitations, and Theoretical Implications

• Ambiguity and subjectivity: Stance detection tasks are inherently subjective, with Krippendorff’s Alpha as low as 0.27–0.35 for multi-category annotation (e.g., vaccination stance), and Fleiss-Kappa of ≈0.35 in multi-perspective annotation, reflecting disagreement even among human annotators (Kunneman et al., 2019, Muscato et al., 1 Mar 2025).
• Insufficiency of sentiment features: While sentiment can inform stance, it is not sufficient—“oracle” sentiment mapping produces F1 scores as low as 53–59% compared to full-feature models (Mohammad et al., 2016).
• Label sensitivity and model bias in LLMs: Output is variably sensitive to prompt style, target description, and label order; LLMs can also propagate societal biases present in web-scale pretraining (Liu et al., 2023, Cruickshank et al., 2023).
• Data imbalance and rare class detection: Minority stances (e.g., denial, refuting) are underrepresented, hindering classifier performance, though strategies such as class-balanced loss functions, soft labels, and network-based propagation help (Zheng et al., 2022, Melton et al., 16 Apr 2024).
• Generalization under domain shift: Heterogeneous targets and shifting label semantics limit cross-domain transfer, necessitating innovations in label-embeddings and mixture-of-experts models (Hardalov et al., 2021).
• Interpretability and explainability: Advanced pipelines (e.g., PolitiSky24) provide rationales and label-supporting text spans, critical for transparency in politically charged applications (Rostami et al., 9 Jun 2025).

6. Practical Applications and Impact

Stance labeling underpins research and deployment in public opinion mining, polarization monitoring, misinformation detection, rumor verification, and automated writing evaluation. Fine-grained, multilingual, and multi-level stance datasets open research on cross-cultural and conversational phenomena, user-level tracking, and the interplay between content and social network dynamics. Model advancements aim to responsibly handle subjectivity, support real-time and scalable analysis (through GNNs, LLMs, and hybrid pipelines), and promote transparency via rationales and explainable annotations.

7. Outlook and Future Directions

The research community is moving toward:

• Embracing annotator diversity and multi-perspective modeling for subjective tasks (Muscato et al., 1 Mar 2025).
• Integrating network-level evidence and interaction patterns, particularly in emerging platforms and for real-time analysis (Melton et al., 16 Apr 2024, Rostami et al., 9 Jun 2025).
• Developing framework-agnostic, cross-lingual and cross-domain models capable of robust stance generalization (Hardalov et al., 2021, Zheng et al., 2022).
• Establishing standardized evaluation protocols for both LLM-based and traditional models, and building benchmarks specifically designed for stance detection rather than repurposed sentiment datasets (Cruickshank et al., 2023).
• Combining automated labeling (using LLMs or semi-supervised propagation) with human-in-the-loop mechanisms for quality assurance and adaptation to evolving topics (Kunneman et al., 2019, Liu et al., 2023).

Stance labeling thus remains a dynamic field, addressing both computational and social challenges with increasing sophistication, expanded data resources, and methodological innovation.