arXiv subjects

Yelena Mejova

Publications and source records attributed to Yelena Mejova.

At least 19 recordsLinked to original sources

arXiv2026

Verifiable Disaster Storylines and Causal Knowledge Graphs: A Citation-Grounded Pipeline from Heterogeneous Humanitarian Sources

Effective humanitarian response depends on the rapid synthesis of heterogeneous, high-volume information sources - a task that routinely exceeds human analytical capacity in the critical early hours of a crisis. We present a pipeline that combines structured disaster records from EM-DAT with unstructured documents from ReliefWeb and the European Media Monitor (EMM) to produce source-grounded disaster storylines and causal knowledge graphs supporting situational awareness for responders and analysts. Using Retrieval-Augmented Generation, the pipeline extracts structured storylines - tabular event profiles covering 17 fields, from severity and key drivers to child-sensitive impact indicators - and constructs causal knowledge graphs where each node and edge is enriched with citation-grounded explanatory narratives, enabling full traceability back to primary sources. We evaluate the system on three diverse crisis use cases through a human evaluation involving 9 domain expert and 9 non-expert evaluators. Results confirm high retrieval precision, strong faithfulness of extracted causal relations, and a clear expert preference for citation-grounded components over ungrounded alternatives. The pipeline is designed to scale to the full EM-DAT catalogue, with the goal of publicly releasing a new collection of disaster stories complementing the EM-DAT database.

cs.AI↗

arXiv2026

Conditional Publics: Shared Events and Divergent Meanings in the European Twitter Debate on the Ukraine War

How do European publics debate a geopolitical crisis on social media, and do they inhabit a shared informational reality? We analyze over 38 million geolocated tweets from 20 European countries during the first eight months of the Russian invasion of Ukraine. Using retweet community detection and stance annotation across six issues, we identify 'hawkish' and 'doveish' opinion clusters present within almost every country studied. We find that structural polarization is driven not by radicalization, but by the exit of casual users. Crucially, whether opposing sides orient to the same events depends on the issue. On pragmatist issues, both sides react to the same high-profile events, forming an agonistic public sphere. Instead, on interpretive issues, they operate as affective publics and counterpublics constructing divergent meanings. We propose conditional publics to describe formations whose relational structure, sharing or fracturing a referential frame, depends on the epistemic character of the debated issue.

cs.CY↗

arXiv2025

A Large-Language-Model Framework for Automated Humanitarian Situation Reporting

Timely and accurate situational reports are essential for humanitarian decision-making, yet current workflows remain largely manual, resource intensive, and inconsistent. We present a fully automated framework that uses large language models (LLMs) to transform heterogeneous humanitarian documents into structured and evidence-grounded reports. The system integrates semantic text clustering, automatic question generation, retrieval augmented answer extraction with citations, multi-level summarization, and executive summary generation, supported by internal evaluation metrics that emulate expert reasoning. We evaluated the framework across 13 humanitarian events, including natural disasters and conflicts, using more than 1,100 documents from verified sources such as ReliefWeb. The generated questions achieved 84.7 percent relevance, 84.0 percent importance, and 76.4 percent urgency. The extracted answers reached 86.3 percent relevance, with citation precision and recall both exceeding 76 percent. Agreement between human and LLM based evaluations surpassed an F1 score of 0.80. Comparative analysis shows that the proposed framework produces reports that are more structured, interpretable, and actionable than existing baselines. By combining LLM reasoning with transparent citation linking and multi-level evaluation, this study demonstrates that generative AI can autonomously produce accurate, verifiable, and operationally useful humanitarian situation reports.

cs.CL↗

arXiv2025

Language-Agnostic Modeling of Source Reliability on Wikipedia

Over the last few years, verifying the credibility of information sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of web domains as sources in references across multiple language editions of Wikipedia. Utilizing editing activity data, the model evaluates domain reliability within different articles of varying controversiality, such as Climate Change, COVID-19, History, Media, and Biology topics. Crafting features that express domain usage across articles, the model effectively predicts domain reliability, achieving an F1 Macro score of approximately 0.80 for English and other high-resource languages. For mid-resource languages, we achieve 0.65, while the performance of low-resource languages varies. In all cases, the time the domain remains present in the articles (which we dub as permanence) is one of the most predictive features. We highlight the challenge of maintaining consistent model performance across languages of varying resource levels and demonstrate that adapting models from higher-resource languages can improve performance. We believe these findings can assist Wikipedia editors in their ongoing efforts to verify citations and may offer useful insights for other user-generated content communities.

cs.SI↗

arXiv2025

Vaccine Hesitancy on YouTube: a Competition between Health and Politics

YouTube has rapidly emerged as a predominant platform for content consumption, effectively displacing conventional media such as television and news outlets. A part of the enormous video stream uploaded to this platform includes health-related content, both from official public health organizations, and from any individual or group that can make an account. The quality of information available on YouTube is a critical point of public health safety, especially when concerning major interventions, such as vaccination. This study differentiates itself from previous efforts of auditing YouTube videos on this topic by conducting a systematic daily collection of posted videos mentioning vaccination for the duration of 3 months. We show that the competition for the public's attention is between public health messaging by institutions and individual educators on one side, and commentators on society and politics on the other, the latest contributing the most to the videos expressing stances against vaccination. Videos opposing vaccination are more likely to mention politicians and publication media such as podcasts, reports, and news analysis, on the other hand, videos in favor are more likely to mention specific diseases or health-related topics. Finally, we find that, at the time of analysis, only 2.7% of the videos have been taken down (by the platform or the channel), despite 20.8% of the collected videos having a vaccination hesitant stance, pointing to a lack of moderation activity for hesitant content. The availability of high-quality information is essential to improve awareness and compliance with public health interventions. Our findings help characterize the public discourse around vaccination on one of the largest media platforms, disentangling the role of the different creators and their stances, and as such, they provide important insights for public health communication policy.

cs.CY↗

arXiv2025

Google Search Advertising after Dobbs v. Jackson

Search engines have become the gateway to information, products, and services, including those concerning healthcare. Access to reproductive health has been especially complicated in the wake of the 2022 Dobbs v. Jackson decision by the Supreme Court of the United States, splintering abortion regulations among the states. In this study, we performed an audit of the advertisements shown to Google Search users seeking information about abortion across the United States during the year following the Dobbs decision. We found that Crisis Pregnancy Centers (CPCs) -- organizations that target women with unexpected or "crisis" pregnancies, but do not provide abortions -- accounted for 47% of advertisements, whereas abortion clinics -- for 30%. Advertisements from CPCs were often returned for queries concerning information and safety. The type of advertisements returned, however, varied widely within each state, with Arizona returning the most advertisements from abortion clinics and other pro-choice organizations, and Minnesota the least. The proportion of pro-choice vs. anti-choice advertisements returned also varied over time, but estimates from Staggered Augmented Synthetic Control Methods did not indicate that changes in advertisement results were attributable to changes in state abortion laws. Our findings raise questions about the access to accurate medical information across the U.S. and point to a need for further examination of search engine advertisement policies and geographical bias.

cs.CY↗

arXiv2025

Too Little, Too Late: Moderation of Misinformation around the Russo-Ukrainian Conflict

In this study, we examine the role of Twitter as a first line of defense against misinformation by tracking the public engagement with, and the platforms response to, 500 tweets concerning the RussoUkrainian conflict which were identified as misinformation. Using a realtime sample of 543 475 of their retweets, we find that users who geolocate themselves in the U.S. both produce and consume the largest portion of misinformation, however accounts claiming to be in Ukraine are the second largest source. At the time of writing, 84% of these tweets were still available on the platform, especially those having an anti-Russia narrative. For those that did receive some sanctions, the retweeting rate has already stabilized, pointing to ineffectiveness of the measures to stem their spread. These findings point to the need for a change in the existing anti-misinformation system ecosystem. We propose several design and research guidelines for its possible improvement.

cs.SI↗

arXiv2025

The Role of Science in the Climate Change Discussions on Reddit

Collective and individual action necessary to address climate change hinges on the public's understanding of the relevant scientific findings. In this study, we examine the use of scientific sources in the course of 14 years of public deliberation around climate change on one of the largest social media platforms, Reddit. We find that only 4.0% of the links in the Reddit posts, and 6.5% in the comments, point to domains of scientific sources, although these rates have been increasing in the past decades. These links are dwarfed, however, by the citations of mass media, newspapers, and social media, the latter of which peaked especially during 2019-2020. Further, scientific sources are more likely to be posted by users who also post links to sources having central-left political leaning, and less so by those posting more polarized sources. Unfortunately, scientific sources are not often used in response to links to unreliable sources.

cs.CY↗

arXiv2025

Narratives of War: Ukrainian Memetic Warfare on Twitter

The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. Wartime communication via memes has been a successful strategy used not only by independent accounts such as @uamemesforces, but also-for the first time in a full-scale interstate war-by official Ukrainian government accounts such as @Ukraine and @DefenceU. We study this prominent example of memetic warfare through the lens of its narratives, and find them to be a key component of success: tweets with a 'victim' narrative garner twice as many retweets. However, malevolent narratives focusing on the enemy resonate more than those about heroism or victims with countries providing more assistance to Ukraine. Our findings present a nuanced examination of Ukraine's influence operations and of the worldwide response to it, thus contributing new insights into the evolution of socio-technical systems in times of war.

cs.CY↗

arXiv2024

Quantitative Information Extraction from Humanitarian Documents

Humanitarian action is accompanied by a mass of reports, summaries, news, and other documents. To guide its activities, important information must be quickly extracted from such free-text resources. Quantities, such as the number of people affected, amount of aid distributed, or the extent of infrastructure damage, are central to emergency response and anticipatory action. In this work, we contribute an annotated dataset for the humanitarian domain for the extraction of such quantitative information, along side its important context, including units it refers to, any modifiers, and the relevant event. Further, we develop a custom Natural Language Processing pipeline to extract the quantities alongside their units, and evaluate it in comparison to baseline and recent literature. The proposed model achieves a consistent improvement in the performance, especially in the documents pertaining to the Dominican Republic and select African countries. We make the dataset and code available to the research community to continue the improvement of NLP tools for the humanitarian domain.

cs.CL↗

arXiv2024

Political Context of the European Vaccine Debate on Twitter

At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users' endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.

cs.SI↗

arXiv2023

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Geographical location is a crucial element of humanitarian response, outlining vulnerable populations, ongoing events, and available resources. Latest developments in Natural Language Processing may help in extracting vital information from the deluge of reports and documents produced by the humanitarian sector. However, the performance and biases of existing state-of-the-art information extraction tools are unknown. In this work, we develop annotated resources to fine-tune the popular Named Entity Recognition (NER) tools Spacy and roBERTa to perform geotagging of humanitarian texts. We then propose a geocoding method FeatureRank which links the candidate locations to the GeoNames database. We find that not only does the humanitarian-domain data improves the performance of the classifiers (up to F1 = 0.92), but it also alleviates some of the bias of the existing tools, which erroneously favor locations in the Western countries. Thus, we conclude that more resources from non-Western documents are necessary to ensure that off-the-shelf NER systems are suitable for the deployment in the humanitarian sector.

cs.CL↗

arXiv2023

Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter

Unprecedented lockdowns at the start of the COVID-19 pandemic have drastically changed the routines of millions of people, potentially impacting important health-related behaviors. In this study, we use YouTube videos embedded in tweets about diet, exercise and fitness posted before and during COVID-19 to investigate the influence of the pandemic lockdowns on diet and nutrition. In particular, we examine the nutritional profile of the foods mentioned in the transcript, description and title of each video in terms of six macronutrients (protein, energy, fat, sodium, sugar, and saturated fat). These macronutrient values were further linked to demographics to assess if there are specific effects on those potentially having insufficient access to healthy sources of food. Interrupted time series analysis revealed a considerable shift in the aggregated macronutrient scores before and during COVID-19. In particular, whereas areas with lower incomes showed decrease in energy, fat, and saturated fat, those with higher percentage of African Americans showed an elevation in sodium. Word2Vec word similarities and odds ratio analysis suggested a shift from popular diets and lifestyle bloggers before the lockdowns to the interest in a variety of healthy foods, communal sharing of quick and easy recipes, as well as a new emphasis on comfort foods. To the best of our knowledge, this work is novel in terms of linking attention signals in tweets, content of videos, their nutrients profile, and aggregate demographics of the users. The insights made possible by this combination of resources are important for monitoring the secondary health effects of social distancing, and informing social programs designed to alleviate these effects.

cs.SI↗

arXiv2023

Authority without Care: Moral Values behind the Mask Mandate Response

Face masks are one of the cheapest and most effective non-pharmaceutical interventions available against airborne diseases such as COVID-19. Unfortunately, they have been met with resistance by a substantial fraction of the populace, especially in the U.S. In this study, we uncover the latent moral values that underpin the response to the mask mandate, and paint them against the country's political backdrop. We monitor the discussion about masks on Twitter, which involves almost 600k users in a time span of 7 months. By using a combination of graph mining, natural language processing, topic modeling, content analysis, and time series analysis, we characterize the responses to the mask mandate of both those in favor and against them. We base our analysis on the theoretical frameworks of Moral Foundation Theory and Hofstede's cultural dimensions. Our results show that, while the anti-mask stance is associated with a conservative political leaning, the moral values expressed by its adherents diverge from the ones typically used by conservatives. In particular, the expected emphasis on the values of authority and purity is accompanied by an atypical dearth of in-group loyalty. We find that after the mandate, both pro- and anti-mask sides decrease their emphasis on care about others, and increase their attention on authority and fairness, further politicizing the issue. In addition, the mask mandate reverses the expression of Individualism-Collectivism between the two sides, with an increase of individualism in the anti-mask narrative, and a decrease in the pro-mask one. We argue that monitoring the dynamics of moral positioning is crucial for designing effective public health campaigns that are sensitive to the underlying values of the target audience.

cs.SI↗

arXiv2023

The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections

Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions$\unicode{x2013}$most of which from Eurosceptic and far-right parties$\unicode{x2013}$thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a "thin ideology", that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data.

cs.SI↗

arXiv2022

Global misinformation spillovers in the online vaccination debate before and during COVID-19

Anti-vaccination views pervade online social media, fueling distrust in scientific expertise and increasing vaccine-hesitant individuals. While previous studies focused on specific countries, the COVID-19 pandemic brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Here, we leverage 316 million vaccine-related Twitter messages in 18 languages, from October 2019 to March 2021, to quantify misinformation flows between users exposed to anti-vaccination (no-vax) content. We find that, during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter anti-vaccination network. U.S. users are central in this network, while Russian users also become net exporters of misinformation during vaccination roll-out. Interestingly, we find that Twitter's content moderation efforts, and in particular the suspension of users following the January 6th U.S. Capitol attack, had a worldwide impact in reducing misinformation spread about vaccines. These findings may help public health institutions and social media platforms to mitigate the spread of health-related, low-credible information by revealing vulnerable online communities.

cs.SI↗

arXiv2022

Modeling Political Activism around Gun Debate via Social Media

The United States have some of the highest rates of gun violence among developed countries. Yet, there is a disagreement about the extent to which firearms should be regulated. In this study, we employ social media signals to examine the predictors of offline political activism, at both population and individual level. We show that it is possible to classify the stance of users on the gun issue, especially accurately when network information is available. Alongside socioeconomic variables, network information such as the relative size of the two sides of the debate is also predictive of state-level gun policy. On individual level, we build a statistical model using network, content, and psycho-linguistic features that predicts real-life political action, and explore the most predictive linguistic features. Thus, we argue that, alongside demographics and socioeconomic indicators, social media provides useful signals in the holistic modeling of political engagement around the gun debate.

cs.SI↗

arXiv2022

Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate

Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.

cs.SI↗