I am a PhD candidate in Applied Linguistics at the Universitat Politècnica de València. My research examines conflict in its more hostile forms, including aggression, incivility, gendered delegitimisation and multimodal hostility, as well as the relations of power expressed through digital discourse.
I also study the use and methodological validation of AI in linguistic research, the effects of algorithms and AI-mediated content on social-media discourse, and the integration of generative AI into language learning and university teaching.
I am an applied linguist, discourse researcher and university educator based in Valencia, Spain. I am completing a PhD in Applied Linguistics at the Universitat Politècnica de València. My dissertation has been approved for defence, scheduled for late October 2026.
My research centres on conflict in digital discourse, particularly its more hostile forms: aggression, incivility, gendered delegitimisation and multimodal hostility. I am interested not only in what people say, but also in how linguistic choices, images, interactional patterns and platform conditions make particular forms of judgement and exclusion possible.
I came to applied linguistics through more than twenty years of language teaching, assessment and course development. During that time, I worked with learners at different levels and in different educational contexts, including examination preparation, academic communication and university teaching.
Teaching made me increasingly interested in the gap between what speakers intend, what language communicates and how meaning is interpreted by others. It also drew my attention to evaluation: how people express approval and disapproval, assign responsibility, construct authority and turn disagreement into personal judgement.
What began as an interest in verbal interaction expanded into the study of gender, multimodality, visual hostility and the wider conditions under which aggressive discourse circulates online.
My work examines how conflict is organised through discourse rather than treating hostility as a collection of isolated offensive words.
I study recurring patterns of evaluation, impoliteness and delegitimisation in social-media discourse, particularly in communication directed at women in public and political life. This includes the ways in which public figures are represented as incompetent, dishonest, abnormal, illegitimate or out of place.
My research also considers multimodal communication. Images, memes and visual framing do not simply illustrate verbal messages; they can intensify judgement, introduce meanings that are not stated directly and construct forms of symbolic punishment or exclusion.
Critical Discourse Analysis and Feminist Critical Discourse Analysis provide an important part of this perspective. They allow me to connect individual linguistic and visual choices with broader questions of power, ideology, gender and social legitimacy.
My research is primarily corpus-based. I work with purpose-built collections of social-media posts, replies, comments and multimodal material, using both qualitative discourse analysis and quantitative methods.
Depending on the project, my work may involve:
I am particularly interested in methodological transparency. Categories used in discourse research often involve interpretation, and interpretation should not be disguised as mechanical certainty. For this reason, I pay close attention to annotation guidelines, disagreement between coders, reliability measures and the evidence required to justify analytical claims.
Artificial intelligence has become a second major strand of my work.
One part concerns the use and methodological validation of AI in linguistic research. Large language models can assist with corpus design, classification and exploratory analysis, but their outputs cannot be treated as automatically valid. I am interested in how human expertise and AI-assisted procedures can be combined, how model performance should be evaluated and where automated analysis introduces bias, inconsistency or false confidence.
A second part concerns algorithmic and AI-mediated social-media discourse. Generative content, recommender systems, automated moderation and synthetic participation increasingly affect what is produced, what becomes visible and which forms of discourse are amplified or suppressed.
I also study generative AI in language education and university teaching. Students will use these systems regardless of whether institutions attempt to prohibit them. The more useful question is therefore how teaching, feedback and assessment should be redesigned so that AI supports learning without replacing critical judgement, responsibility or independent intellectual work.
My teaching is based on clear task design, transparent expectations and feedback that students can act on.
I favour communicative and task-based learning, but I do not treat communication as an alternative to accuracy, structure or explicit instruction. Students work more effectively when complex tasks are broken into manageable stages, criteria are made visible in advance and feedback identifies a concrete next step.
My experience as a Cambridge speaking examiner has also influenced how I approach assessment. It has reinforced the importance of distinguishing evidence-based evaluation from general impressions and of making performance criteria understandable to students before they are assessed.
A fuller account of my teaching approach is available on the Teaching page. Teaching experience and statement →
Across my work, I return to the ways public scrutiny develops on social media and to what happens when attention shifts from what a public actor has done to who that person is understood to be. The cases differ—politicians, members of royal institutions, athletes, media figures and people involved in institutional controversies—but they reveal related pressures surrounding authority, visibility, identity and belonging.
My research approaches these questions at different scales. Some studies follow the detail of a single controversy; others compare larger collections of posts and comments across languages, countries and platforms. Words are considered alongside images, humour, memes and platform conventions. Taken together, the work combines close qualitative interpretation with corpus-based and statistical analysis, allowing recurring patterns to be tested without losing the social and cultural contexts in which they acquire meaning.
I draw on critical discourse analysis and systemic functional linguistics, combining qualitative interpretation with corpus-based, multimodal and statistical analysis.
My doctoral thesis by publication examines how hostile evaluation is used to contest political and symbolic authority on X. It treats conflict talk as the broader discourse phenomenon and hostile political evaluation as the mechanism through which public figures are assessed in relation to morality, truthfulness, competence, resolve, normality, authenticity and legitimacy.
Across three published studies, the project moves from an analysis of Spanish hostile replies to Isabel Díaz Ayuso, to a multimodal study of Queen Letizia and King Felipe VI during the 2024 DANA crisis, and then to a Spanish–Italian comparison of Ayuso and Giorgia Meloni.
The project addresses a persistent analytical problem: hostility is often identified through explicit insults or isolated linguistic features, although attacks on public authority frequently emerge through combinations of evaluation, intensification, identity positioning, imagery and other semiotic resources. To account for these combinations, the thesis develops the concept of configurational hostile evaluation. This approach makes less explicit forms of delegitimation analytically visible and helps explain how digital disagreement can develop into attacks on credibility, legitimacy and the right to exercise public authority.
My current work extends this programme through multilingual and cross-platform comparison, recognition and institutional belonging, visual forms of public scrutiny, transparent human–AI annotation, AI-mediated social-media discourse, and generative AI in language education.
{{ p.abstract }}
I am available for interviews and expert commentary on digital hostility, gendered political communication, multimodal discourse, corpus-based research and the use of artificial intelligence in linguistic analysis. I can contribute in English, Spanish and Russian.
Request an interviewMy teaching brings together language development, disciplinary communication and linguistic study. Across university and professional contexts, I have taught English from intermediate to advanced levels, designed courses and materials, and worked with students preparing to use the language in academic, technical and educational settings.
At university level, this work has ranged from academic and professional English for engineering students to the history, vocabulary and grammar of English for future language teachers. Although the subjects and student groups differ, the underlying concern remains the same: helping learners understand how language works and use it with greater precision, independence and awareness of context.
At the Institute of Linguistics, I taught language and linguistics courses to university students preparing to become teachers of English.
My teaching combines explicit attention to language with sustained practice in meaningful academic and professional tasks. Course design is adapted to the students' disciplinary context, while assessment is used not only to measure performance but also to guide revision and further learning.
Selected materials for academic English, professional communication, language analysis and classroom practice will be added here.
My work combines research in digital discourse with more than twenty years of language teaching, curriculum development and university education. I am currently a researcher in applied linguistics and a PhD candidate at the Universitat Politècnica de València.
My research focuses on public scrutiny in digital discourse, hostile political evaluation, multimodal communication, corpus-based analysis, and the methodological use and validation of AI-assisted approaches in linguistic research.
The downloadable CV provides a complete record of my education, research appointments, publications, book chapters, conference activity, teaching experience, funded projects and professional service.
Notes is where I write more freely about questions arising from research, teaching and academic practice. The pieces are less formal than journal articles, but they develop arguments prompted by classrooms, data, digital technologies and everyday conversations.
“The real question is not whether machines think but whether men do.”— B. F. Skinner, 1969
One of my students recently asked whether I'm afraid that AI will take people's jobs, and whether I, as a teacher, worry about becoming unnecessary. I'm not, though the reason isn't that teaching is somehow safe.
We have lived through this before. Cities once employed lamplighters to light the street lamps each evening and put them out at dawn. There were knocker-uppers who went door to door waking workers for their shifts, switchboard operators who connected every call, typists, and plenty of other trades that simply faded out. But the people didn't fade out. What disappears, when technology shifts, is a particular way of doing the work, and it never disappears in just one way. Some jobs vanish outright. Some are transformed into something different. And some simply pick up the new tools and end up faster, sharper, better at what they already did. So “will AI take our jobs?” has always struck me as the wrong question, or at least a lazy one. The more interesting question is what this technology actually is.
Here I hold a less popular view. I'm not convinced we should be calling today's large language models “artificial intelligence”. Is it artificial? Definitely yes. Is it intelligence? Nope. Look at what actually happens under the hood and you find prediction: the system estimates the next token, the next word, the most probable continuation. We have seen this before in miniature. T9 on our old phones. Then autocomplete. Then systems that stopped guessing the next word and started guessing the next sentence, the next page, the whole program. Today's models are vastly more capable. I don't say this to diminish them; they are remarkable. But strip the layers back and the core is the same: predict what comes next.
And yet that prediction is not some hidden spark. It is the effect of an architecture, a stack of algorithms doing exactly what they were built to do. The model predicts because that is what the architecture produces, not because it wants anything at all. To be fair, the result can be uncanny. Give it enough about you and a typical-enough thought, and it will guess your next one, or even offer an idea you hadn't reached yet. But it is still imitation, not anything genuinely its own. It mimics human thinking, and mimics it impressively, which is exactly why “intelligence” feels like the wrong word to me. We have built extraordinarily powerful machines that copy the intellectual qualities of people, and they run on an old human trick: fake it till you make it. A model can continue your thought. Can it ever begin one? It unfolds the question. We pose it.
Still, the more time I spend with these systems, the more I notice how much we resemble them. Don't we learn in a similar way? We absorb language, books, films, songs, school, the endless talk around us, and then reproduce other people's ideas as our own, often without realising it. Put that way, the human and the model start to look uncomfortably alike.
Which brings me back to my student. If my only value as a teacher were to store and repeat information, a book could have done my job ages ago. What I do is harder to automate. A model can produce a flawless explanation. It cannot want you to understand.
My student wanted to know whether the machine would replace her and me. I think the better question is what it enables us to do. And that, in the end, is what it is: a tool. Like the technologies before it, it speeds work up, raises its quality, and opens up new kinds of work, sometimes whole fields that didn't exist yesterday. For now, I see more that it adds than it takes away.
Originally published on LinkedIn on 19 June 2026. LinkedIn.
← Back to NotesI welcome enquiries about research collaboration, conference participation, teaching, media commentary, and projects related to digital discourse, language and AI-assisted research. Please include enough context for me to understand the request.