Public Application Programming Interface Research Articles

How APIs Create Growth by Inverting the Firm

Traditional asset management strategy has emphasized building barriers to entry or closely guarding unique assets to maintain a firm’s comparative advantage. A new “inverted firm” paradigm, however, has emerged. Under this strategy, firms share data seeking to become platforms by opening digital services to third parties and capturing part of their external surplus. This contrasts with a “pipeline” strategy where the firm itself creates value. This paper quantitatively estimates the effect of adopting an inverted firm strategy through the lens of application programming interfaces (APIs), a key enabling technology. Using both public data and those of a private API development firm, we document rapid growth of the API network and connecting apps since 2005. We then perform difference-in-difference and synthetic control analyses and find that public firms adopting public APIs grew an additional 38.7% over 16 years relative to similar nonadopters. We find no significant effect from the use of APIs purely for internal productivity: the pipeline strategy. Within the subset of firms that adopt public APIs, those that attract more third-party complementors and those that become more central to the network see faster growth. Using variation in network centrality caused by API degradation, an instrumental variables analysis confirms a causal role for APIs in firm market value. Finally, we document an important downside of public APIs: increased risk of data breach. Overall, these facts lead us to conclude that APIs have a large and positive impact on economic growth and do so primarily by enabling an inverted firm strategy. This paper was accepted by D. J. Wu, information systems. Funding: This work was partially supported by Mulesoft and Apigee Corporation. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4968 .

Pediatric injuries classification using openAI: new opportunities for epidemiological surveillance

Abstract Background The burden of unintentional pediatric injuries remains a significant concern for healthcare systems worldwide. Timely and accurate surveillance is critical for injury prevention and resource allocation. However, the diagnosis reported in the emergency department records is often not coded, and the manual coding of these records is labor-intensive, time-consuming, and prone to errors. To implement an automatic coding system using GPT-based models to extract and classify injury data in the Italian language from pediatric emergency department records. Methods The study included 283,468 admission records to the pediatric Emergency Department of Padova University Hospital from 2007 to 2018. Each access is mandatorily registered in an electronic data collection system. For each emergency department access, both coded (administrative and demographic data) and free-text (diagnosis) information are reported. A random subset of 40,030 records underwent classification of free-text diagnosis (as injury or not) by an expert clinician (gold standard). OpenAI was used for the classification task. OpenAI is an extensive language model based on the GPT architecture. Specifically, the GPT-3.5 variant was employed for the present work. openAI was accessed through a public application programming interface (API) using R software. Results Preliminary results showed a classification accuracy of 96.2%. The tool's ability to correctly classify the injuries (sensitivity) was 95%, while the specificity was 96.5%. Conclusions The performance of the classification task was excellent. The present results demonstrate the feasibility of GPT-based models for processing unstructured free text information from medical records. Key messages • OpenAI would represent a promising opportunity to improve pediatric injuries surveillance • OpenAI would improve the classification of unstructured information from medical records

Information model of the essential goods purchase duration

The task of reducing the time for the purchase of essential goods is especially relevant in cases of shortage of free time of buyers. To do this, it is necessary to predict and estimate the time required to purchase goods. Traditional approaches based on cartographic systems do not provide estimates and forecasts, but only allow you to build a route to the right place based on an assessment of the traffic situation. For this reason, the problem of developing a more modern model is relevant, taking into account such factors as the infrastructural location of the store, user evaluation, and the workload of the store. The paper proposes an information model that includes such time costs of the buyer as the search for goods, the route to the place of sale of goods, the purchase of goods. The time spent on the purchase of goods is described using elements of queuing theory. Statistical and direct methods for assessing the workload and queues in the store are highlighted. The developed generalized model contains the parameters necessary to estimate the required time using statistical methods which include traffic forecasting based on user ratings and reviews, analysis of the infrastructure location and public video surveillance cameras, public Application Programming Interface of stores, and Internet services. Correction coefficients have been introduced to adjust the estimation of model parameters depending on the infrastructure location of the store and user ratings. A new information model has been formulated that allows taking into account the dependence of the time required to purchase emergency goods on the workload of the store, its infrastructure location, ratings and user reviews. The simulation model is developed in the AnyLogic environment. An example of using the model to estimate the average time spent on the purchase of emergency goods is demonstrated. The simulation results are consistent with the conducted experiment in which purchases of emergency goods were made in various stores in Saint Petersburg. The developed model can be used when searching for the optimal route to the place of sale of essential goods when planning the construction of stores as well as in the areas of marketing and delivery of goods.

Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources.

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.

The surveillance of infectious diseases: the experience of Open Data COVID

to describe Open Data Covid, an online application dedicated to the pandemic and the health of the population of the province of L'Aquila (Abruzzo Region, Southern Italy), created following the health emergency in Italy and worldwide. Open Data Covid is the result of a multidisciplinary study group including University of L'Aquila, Local Health Unit 1 Abruzzo, and Gran Sasso Science Institute. In the first phase, the information to be shown was identified and made available based on the pandemic national reports to obtain comparable results. The health databases containing the information needed to power the application were selected. This information was then evaluated, cleaned, and integrated. data derived from administrative data flow of the Local Health Unit. the final application collects individual data from confirmed laboratory cases of SARS-CoV-2 infection and collects information about their residence, laboratory diagnosis, hospitalization, clinical status, risk factors and outcome. the application was structured in three sections. The first section shows data about the COVID-19 pandemic; the second contains information about the assisted population; the third contains documentation and access to public Application Programming Interfaces (APIs) for accessing source data. Consulting the application data is simple and intuitive: through graphs and infographics, the temporal and geographical course of the pandemic can be known. the Open Data Covid application is a response to the information gaps highlighted by the COVID-19 pandemic. Its creation demonstrated that is possible to build an online application useful both for the population and for public health professionals.

Mock galaxy surveys for HST and JWST from the IllustrisTNG simulations

ABSTRACT We present and analyse a series of synthetic galaxy survey fields based on the IllustrisTNG simulation suite. With the IllustrisTNG public data release and JupyterLab service, we generated a set of 12 light-cone catalogues covering areas from 5 to 365 square arcmin, similar to several JWST Cycle 1 programs, including JADES, CEERS, PRIMER, and NGDEEP. From these catalogues, we queried the public application programming interface to generate simple mock images in a series of broad-band filters used by JWST-NIRCam and the Hubble Space Telescope cameras. This procedure generates wide-area simulated mosaic images that can support investigating the predicted evolution of galaxies alongside real data. Using these mocks, we demonstrate a few simple science cases, including morphological evolution and close pair selection. We publicly release the catalogues and mock images through MAST, along with the code used to generate these projects, so that the astrophysics community can use these products in their scientific analyses of JWST deep field observations.

Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9.

The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

BackgroundBiomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API).ResultsTo create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink.ConclusionRTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

Research on the Construction of Malware Variant Datasets and Their Detection Method

Malware detection is of great significance for maintaining the security of information systems. Malware obfuscation techniques and malware variants are increasingly emerging, but their samples and API (application programming interface) sequences are difficult to obtain. This poses difficulties for the development of malware variant detection models. To address this issue in this paper, we first generated a malware variant dataset using the obfuscation technique based on the disassembly and decompilation of malware. Then, an API call dataset of these malware variants was constructed through sandboxing. Compared to similar work, the malware variants and their obfuscated API call sequences generated in this paper were all runnable. After that, taking a public API call sequence dataset of obfuscation-free malware as input, a BERT (bidirectional encoder representation from transformers) pretrained model for malware detection was constructed. To enhance the ability of this pretrained model to handle obfuscation and variants, in this paper, we used adversarial training to improve the robustness and generalization of the detection model under obfuscation. As the experimental results show, the proposed scheme can improve the classification performance of malware variants under obfuscation. The accuracy of the malware variant classification was close to that of the unobfuscated case.

Top 25 cited articles on Covid-19 and IBD: A bibliometric analysis

ObjectivesThe use of citation analysis to identify the most cited Covid-19 and inflammatory bowel disease (IBD) manuscripts to provide an insight into the advances and knowledge accumulated regarding the pandemic in this subgroup of patients. MethodsWe've used a public application programming interface (API) U.S. National Center for Biotechnology Information (NCBI) to access the PubMed database. Data lock was performed on April 19, 2022. The API was used to retrieve all available IBD AND Covid-19 -related entries. For each retrieved entry, we've also obtained its citation count. ResultsThe top 25 manuscripts were published between 2020 and 2021. The total citation count is 2051. The citation count of articles ranged from 41 to 313. The top 25 manuscripts were published in eight journals, while 16 were published in Gastroenterology and Gut. 36% of the most cited manuscripts reported clinical characteristics and patient outcomes, and 32% dealt with patient management. The most impactful manuscripts provided evidence that IBD patients are not at increased risk for severe morbidity or mortality from Covid-19 and that it is not advisable to discontinue the anti-inflammatory treatment for IBD during the pandemic. Two basic science studies demonstrated mechanistic insights for these observations. Studies that examined the immunogenic response of IBD patients treated with biologics were also part of the top-cited list. ConclusionsImpactful scientific publications on Covid-19 in IBD patients provided reassurance and directed treatment at the time of this newly recognized severe disease.

Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study.

BackgroundSocial media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research.ObjectiveWe aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet.MethodsTweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed.ResultsOf 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (ρ=0.266; P<.001), joy (ρ=–0.234; P=.001), general risk (ρ=0.908; P<.001), and intensity of suicidal ideation (ρ=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (β=0.311; P=.001), as well as being a predictor for fear (β=–0.009; P=.01) and emotional valence (β=0.007; P=.009). The model explained 75% of the variance.ConclusionsThese findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media.

Web-based Application Programming Interface (Web APIs): Vacancies in Iranian Public Library Websites

Application Programming Interfaces (APIs) are software tools that help different programs work together. APIs can improve an organization's presence on the Web with tools that integrate various useful, popular programs. This study aimed to identify appropriate web-based APIs used by the most popular public library websites for presentation on Iranian public libraries’ websites. For this purpose, we conducted this study in two stages: In stage one, Web APIs were identified by reviewing the websites of the top public libraries in the world. Then, in stage two, using the obtained results, important Web APIs were selected utilizing experts' opinions (the heuristic method). In stage one, the 30 Web APIs in two categories were identified: 10 public Web APIs and 20 private Web APIs. Then, in stage two, 7 public APIs and 17 private Web APIs for these websites were selected, based on expert analysis. The results of this study can be used to improve the design of public library websites and enhance the communication of such websites' presence on the Web.

Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study.

BackgroundThe number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement.ObjectiveThe goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies.MethodsGeolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status.ResultsThe percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7% vs 66.4%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one’s own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities.ConclusionsOur study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one’s own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment.

APIs, the glue under the hood. Looking for the “API economy”

Purpose Some specialized consultancies have been making the case of an “API economy”. This study aims to investigate the issue, marshalling data on the economic dimension, to better understand the environments of APIs. It offers an overview of the functions and definition of application programming interfaces (APIs) in the backdrop of the history of services computing. The paper attempt assessing the economic value (size of the market) of APIs and reviews some of the available metrics. The paper also takes a look some issues and challenges ahead for the deployment of all kind of APIs. Design/methodology/approach The paper is based on desk research and a scientific and grey literature review. However, it relies mostly on specialized consultancies although from a critical viewpoint. The paper provides an historical account of the notions of APIs and API economy. Findings The paper questions the idea of an “API economy” that still stands on the “hype” side and is not clearly substantiated. It reveals that the number of firms with mature API programs remains small and that there is an uneven development across industries (traditional firms are less active than digital natives) and countries (Silicon Valley is leading). It highlights that the domination of IT companies (leaders and pioneers of APIs) raises issue of competition and at some point, may prevent rather than foster innovation. Research limitations/implications There is no robust data about the size of the API market nor about its value. Sources are highly heterogeneous and delimitations not always precise. The standard metrics or indicators are hard to find. Further research would be needed to better document this area. Practical implications The paper reviews some of the expected benefits of the use of APIs as enablers of private or public ecosystems. Social implications The paper delineates some of the economic benefits of the public APIs based on open data. It shows some positive examples of public APIs in the EU. Originality/value There is hardly any mention of the API economy in research literature. Most of the academic literature still stems from engineering department or business-management departments, not department of economics. Consultants would usually focus on the potential of business growth, on how to design an effective API strategy but not on the very economic dimension. The paper attempts providing a synthesis of the available data.

A Case Study of Publishing Internal APIs to External Users

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

Livestock data – Is it there and is it FAIR? A systematic review of livestock farming datasets in Australia

The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts.

Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach.

BackgroundTwitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans.ObjectiveWe aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public.MethodsUsing Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts.ResultsWe identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public.ConclusionsTwitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

Fostering Value Creation with Digital Platforms: A Unified Theory of the Application Programming Interface Design

ABSTRACTWhile many firms in recent years have started to offer public Application Programming Interfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and the demands of external developers. We address the lack of theory that explains the performance impacts of three API archetypes (professional, mediation, and open asset services). We couple survey data from 152 API product managers with manually coded API design classifications. With this data, we conduct cluster and regression analyses that reveal moderating effects of two value creation strategies (economies of scope in production and innovation) on the relationships between API archetype similarity and two API performance outcomes: return on investment and diffusion. We contribute to IS literature by developing a unifying theory that consolidates different theoretical perspectives on API design, by extending current knowledge on the performance effects of API design, and by empirically studying the distinct circumstances under which digital platforms facilitate economies of scope in production or in innovation. Our results provide practical implications on how API providers can align API archetype choice with the value creation strategy and the API’s business objective.

Search Engine Get Application Programming Interface

Application Programming Interface (API) is a function concept that can be called by other programs. The API works as a link that unites various applications of various types of platforms, commonly known as API public names. The public API has been widely spread, while its users, programmers who want to search for public APIs, must browse through various methods such as general search engines, repository documentation or directly in web articles. The user does not yet have a system specifically for collecting public-public APIs, so that users have difficulty in performing API public link searches. The solution to these problems can be solved by building a web framework with a search engine interface that provides specific public-public searches for the API, so that users can search the API public more easily. Web Service is an API that is made to support the interaction between two or more different applications through a network. Representational State Transfer (ReST) is one of the rules.

Twitter conversations about food poverty: an analysis supplemented with Google Trends analysis

BackgroundAlthough the UK has not routinely collected national data on household food insecurity, the United Nations estimates that 8·4 million people in the UK experience food insecurity, a point emphasised by UN Special Rapporteur Philip Alston. With the use of novel and mixed methods we explored current topics, communications, and characteristics of social media networks on food poverty posted on Twitter. Our research questions were: what discussions were taking place on Twitter related to food poverty within the specified time? What hashtags, words, and websites were the most mentioned related to food poverty? What word-pair groups emerged by social analysis? MethodsWe used NodeXL Pro software to collect tweets sent between Nov 28 and Nov 30, 2018, using the phrase food poverty. This temporal range controlled for variables associated with food poverty, notably school-aged children and holiday periods, as we used Twitter's free public application programming interface (API) limit of 18 000 tweets and search range of 9 days. We analysed and visualised our data using social and semantic analysis tools such as overall graph metrics, vertex degree, vertex betweenness and closeness centralities, words and word pairs, and Network top items graph metrics. We also explored the spatiotemporal trends associated with this phrase using Google Trends. FindingsWe identified 4843 Twitter users whose recent tweets contained the term food poverty. There was 6039 tweets and connections in our network including 376 unique tweets, 3576 retweets, 1625 mentions, and 462 replies. The users shared information related to the existence of food poverty in the UK. Conversations included the growing numbers of foodbanks, welfare reforms, and Universal Credit as the cause of this problem. Google Trends indicated the highest degree of interest for food poverty search queries was related to New Zealand, followed by the UK and USA. Philip Alston was also the highest search term trend in the UK during this time. InterpretationThis research provides a snapshot of conversations about food poverty on Twitter. The themes we obtained indicate a lack of acknowledgment of food poverty in terms of both social policy and public health in the UK. Capturing and analysing social communications using this approach has highlighted increasing public awareness of food poverty. FundingTeesside University.