Exploring the Digital Identity of Agrifood Products:
My ATRIUM TNA Placement at the GATE Group
By Tenia Panagiotou
As a linguist and postdoctoral researcher at the Consumer and Sensory Lab of the Department of Food Science and Nutrition, University of the Aegean, my work sits at the intersection of language, food, and digital communication. Over the past year, my research has focused on the digital identity of agrifood products – how olive oil, wine, honey, cheese, herbs, and other regional products are portrayed online, how consumers talk about them, and how professionals frame them through branding, marketing narratives, and cultural references. Understanding this “digital identity” requires robust, scalable data extraction and analysis pipelines – something that text mining and AI tools can powerfully enhance.
Through the Transnational Access scheme of the ATRIUM, I had the opportunity to return to the School of Computer Science at the University of Sheffield for a two-week placement (17–28 November 2025). My goal this time was more targeted than during my previous visit. Whereas my earlier placement focused on exploring tools, this one focused on building and evaluating an operational pipeline for analysing agrifood discourse across social media and web sources.
School of Computer Science, University of Sheffield
Developing and Validating a Multi-Layer Analytical Pipeline
During this visit, I worked closely with the GATE/CLARIN-UK team (special thanks go to Xingyi Song and Ian Roberts) to refine the multi-step data acquisition and analysis pipeline I presented at the beginning of my stay. This placement concentrated on building and evaluating an operational pipeline for analysing agrifood discourse across social media and web sources. This work forms a foundational part of our broader effort to understand how food products acquire and project their digital identity in consumer-driven environments.
Members of the GATE team
As the first analytical layer, I assessed food relatedness, distinguishing posts that genuinely concern food products from those that only mention them peripherally. This was followed by sentiment analysis (positive, negative, neutral) and a more granular classification of emotions using the food-elicited emotion lexicon developed by our team. This dual approach allowed us to capture both surface-level sentiment and more nuanced affective responses associated with agrifood products.
This visit has once again demonstrated the value of interdisciplinary cooperation in addressing complex, data-intensive challenges in food communication and consumer behaviour. I look forward to extending our partnership with the GATE group as we continue building tools and methodologies for understanding online food discourse – an emerging area at the intersection of linguistics, AI, and food science.
Screenshot of pipeline output regarding emotion
A significant part of the workflow involved classifying posts against the 17 United Nations Sustainable Development Goals, allowing us to investigate how sustainability narratives intersect with agricultural food product promotion and consumer expression. In parallel, I applied structured evaluations of health claims (healthy / unhealthy / neither), and identified mentions of nutritional content using recognised nutritional-claim terminology from the literature. Posts were also categorised by diet style, based on the most common diet-related expressions appearing in Greek online food discourse.
The pipeline further incorporated several agrifood-specific layers. These included the identification of sponsored versus non-sponsored posts; extraction of sensory attributes using the ISO sensory-analysis vocabulary; and topic classification using a controlled vocabulary derived from the LanguaL™ food-description thesaurus, an established international standard for structured food categorisation. Additional layers captured time expressions, Protected Designation of Origin/Protected Geographical Indication references, the 13 official Greek prefectures for locality insights, and standard olive oil types, given the prominence of olive-oil content in our dataset.
Together, these components form a comprehensive analytical framework that supports direct comparison between human classification, and AI-driven open-response versus closed-set classification. This comparison will allow us to quantify alignment, divergence, and the specific areas where Large Language Model (LLM)-based classification performs strongly or reveals fragility when applied to Greek agrifood discourse. This will allow us to choose the best route considering the pros and cons of each method. The placement offered the environment and technical guidance required to validate the pipeline, evaluate model behaviour, and refine the overall architecture for future tool development and collaborative research.
Pic.5: Screenshot of pipeline output regarding sensory attributes
Technical Discussions and Next Steps
Beyond the core analysis, the visit provided valuable opportunities for in-depth discussions with GATE researchers about potential tool development and methodological extensions. We reviewed infrastructure constraints, data-format interoperability issues, and strategies for orchestrating multi-step processing within the GATE ecosystem. These conversations will directly shape the next phase of collaboration, guiding both immediate refinements and the longer-term agenda for joint work.
Throughout my stay, the GATE team was extremely supportive, offering expert advice on evaluation frameworks, debugging strategies, and LLM behaviour in multi-label tasks. The environment was collaborative, constructive, and intellectually stimulating. These two weeks enabled me not only to strengthen the methodological aspects of the project but also to strategically plan our next steps, including joint publications, shared datasets, and the creation of specialised agrifood-oriented NLP tools.
I am especially grateful to the ATRIUM programme for providing this opportunity; to Diana Maynard for accepting me for a second visit and for her guidance and support; to Jo Wright for managing all logistics and securing the coziest accommodation for my stay.
Christmassy Sheffield
This visit has once again demonstrated the value of interdisciplinary cooperation in addressing complex, data-intensive challenges in food communication and consumer behaviour. I look forward to extending our partnership with the GATE group as we continue building tools and methodologies for understanding online food discourse – an emerging area at the intersection of linguistics, AI, and food science.
