Application Programming Interface Research Articles

Analysis of Suicide-related Tweets During the COVID-19 Pandemic

Background: The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. Objective: It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Methods: Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Results: Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. Conclusion: We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.

Context-aware self-powered intelligent soil monitoring system for precise agriculture

The agricultural sector is transforming with advanced technologies such as internet of things (IoT), cloud computing, and machine learning, for increased productivity and sustainability. However, fixed sensor deployments struggle to capture the dynamic and heterogeneous soil properties with irregularities in farming operations, and negatively impacting crop performance and resource utilization. This paper presents a novel context-aware, self-powered intelligent soil monitoring system (ISMS) applied in precision agriculture. By integrating advanced sensors, energy harvesting, real-time data analytics, and context-aware decision support, ISMS provides real-time context insights into soil, energy, and weather conditions. The informed decisions are enabled and tailored to their specific agricultural environment. The system utilizes a multi-parameter soil sensor, photovoltaic (PV) panel, and intelligent context-aware analytics for a sustainable, cost-effective solution powered by solar energy and OpenWeather application program interface (API) for weather data. Field tests over two months demonstrated the system's effectiveness, together with continuous operation without grid power. This research highlights ISMS's potential in enhancing soil nutrient management and decision-making and offering significant economic and environmental benefits for modern agriculture, especially in remote areas.

Federated and reusable processing of Earth observation data

The unprecedented and continuously growing volume of Earth Observation (EO) and geospatial data has necessitated a paradigm change where compute is collocated with the data archives in public clouds. However, as no single cloud platform can host all of this data, federated processing solutions that work across multiple cloud platforms are becoming increasingly relevant. A community-based approach to federated processing has started using openEO, a common Application Programming Interface (API) and set of well-defined processes that simplifies reuse and provides a valuable level of abstraction when handling large EO data volumes. We present key concepts for federated processing and related interoperability aspects based on openEO Platform, a federated public cloud platform.

COVID-19 pediatric vaccine Hesitancy: Themes and interactions with verified twitter accounts.

Though vaccine hesitancy and misinformation has been pervasive online, via platforms such as Twitter, little is known about the characteristics of pediatric-specific vaccine hesitancy and how online users interact with verified user accounts that may hold larger influence. Identifying specific COVID-19 pediatric vaccine hesitancy themes and online user interaction and sentiment may help inform health promotion that addresses vaccine hesitancy more effectively among parents and caregivers of pediatric populations. Keywords were used to query the public streaming twitter application programming interface to collect tweets associated with COVID-19 pediatric vaccines. From this corpus of tweets, we used topic modeling to output 20 topic clusters of tweet content and examined the 10 most retweeted tweets from each cluster to classify for relevance to pediatric COVID-19 vaccine hesitancy topics. Tweets were inductively coded to identify specific themes. Publicly available user metadata were assessed to identify verified accounts and self-reporting of racial or ethnic identity, and parental status. Replies to tweets were coded for user sentiment. A chi-squared test was used to determine the proportion of users agreeing with misinformation tweets RESULTS: 863,007 tweets were collected between October 2020-October 2021. The 230 top tweets reviewed after outputting topic clusters accounted for 236,121 tweets and retweets. 84 unique tweets were identified as related to pediatric COVID-19 vaccine topics by verified users. Twenty three tweets (generating 44,509 retweets) contained misinformation-related themes. Seventy-one percent (n=742) of user replies agreed with misinformation sentiment of the parent tweet. Main themes identified included vaccine development conspiracy, vaccine is experimental, and vaccine as a control tactic discussions. This study found that users who interacted with misinformation posted by verified accounts were more likely to agree than disagree with misinformation sentiment.

Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring

Aim Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring. Innovation In this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada. Main conclusions We retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques.

628. Geographic distribution of hospitalized severe malaria cases in the United States, 2012–2018

Abstract Background Between 200–300 severe malaria cases are diagnosed annually in the U.S. Intravenous artesunate (IVAS), the FDA-approved 1st-line therapy for severe malaria since 2020, is commercially available but at high cost. Uncertainty regarding where people with severe malaria will seek medical care poses supply chain challenges. We identified hospitals where people with severe malaria were treated and mapped proximity to Level 1 trauma centers as a proxy for hospitals that provide complex clinical care, which could assist with planning for IVAS distribution. Location identification and manual adjudication process for hospitals that hospitalized people with severe malaria as reported to CDC from 2012–2018. Of the original 1,912 cases of severe malaria reported to the CDC from 2012-2018, we excluded 379 cases that were not admitted or were admitted but missing a hospital name. Of the remaining 1,533 severe malaria cases that were eligible for adjudication, 1,237 were an exact match with an existing hospital location. Of the 296 cases that were not an exact match, we manually adjudicated 229 cases by clarifying a hospital name that was abbreviated, incomplete, or had a change in name; only 49 cases could not be identified given multiple possible sites with the same name, and 18 cases with no name that matched a hospital site. Six additional cases were excluded because of occurring at a site with no Level 1 trauma center within driving distance (i.e., Alaska and US Virgin Islands). Methods Using 2012–2018 data reported to the Centers for Disease Control and Prevention (CDC), we analyzed free text of U.S. hospital names where people were treated as inpatients for severe malaria. We identified these hospitals using Google Maps Geocoding Application Programming Interface (API) followed by manual adjudication. We excluded cases when data were incomplete or ambiguous or if a Level 1 trauma center was inaccessible (e.g., Alaska or U.S. Virgin Islands). We then mapped hospitals to U.S. counties and estimated travel times between hospitals and the nearest Level 1 trauma center as per 2014–2016, using Google Maps Distance Matrix API. Average numbers of severe malaria cases at identified hospitals per US county annually as reported to the CDC during 2012–2018. Panel A shows the geographic distribution and average number of severe malaria cases at identified hospitals per year by county reported to CDC during 2012–2018; darker color shows counties with more severe malaria cases reported. Panel B shows that the distribution is highly skewed to 2 counties that reported more than 10 inpatient cases annually; 2,824 counties (pale peach) reported no cases of severe malaria admitted to an identified hospital during 2012–2018. Results After excluding 379 cases with no recorded hospital name from 1,912 severe malaria cases, we successfully adjudicated hospitals for 1,460/1,533 cases (Fig 1). Two counties reported >10 inpatient cases per year (Bronx County, NY and New York County, NY), and 2,824 counties never reported a hospitalized severe malaria case (Fig 2). Almost 35% of inpatient cases occurred at Level 1 trauma centers (508/1,460 [34.8%]) (Fig 3). Of the 952/1,460 (65.2%) inpatient cases not at Level 1 trauma centers, 891/952 (93.5%) were at hospitals located < 2h estimated driving time of a Level 1 trauma center. Only 61/1,460 (4.2%) cases were at locations >2h from a Level 1 trauma center. Limitations include that the analysis did not consider the original site of clinical presentation with malaria or locations of people with severe malaria not hospitalized. Histogram of modeled travel times between each severe malaria case at an identified hospital and the nearest Level 1 trauma center. Of severe malaria cases reported to CDC in 2012–2018 and hospitalized at an identified hospital, 34.8% were treated at Level 1 trauma centers (red bar); for severe malaria cases that were not at Level 1 trauma centers, we found that modeled transit times between treatment site and Level 1 trauma center were less than 2 hours except in 4.2% of severe malaria cases at an identified hospital (right of black dashed vertical line). Conclusion More than 95% of people admitted for severe malaria at identified hospitals reported to the CDC in 2012–2018 were at or within 2h estimated driving time of a Level 1 trauma center, which may suggest priorities for implementing IVAS distribution. Disclosures Alison Ridpath, MD, MPH, Abbvie Inc.: Stocks/Bonds (Public Company)|Amarin Corperation PLC: Stocks/Bonds (Public Company)|Amgen Inc: Stocks/Bonds (Public Company)|BioNTech: Stocks/Bonds (Public Company)|Immunic Inc: Stocks/Bonds (Public Company)|infinity Pharmaceutical Companies: Stocks/Bonds (Public Company)|IQVIA Holdings Inc: Stocks/Bonds (Public Company)|Johnson and Johnson: Stocks/Bonds (Public Company)|Merck and Co inc: Stocks/Bonds (Public Company)|Pfizer Inc.: Stocks/Bonds (Public Company)|Protagonist Therapeutics: Stocks/Bonds (Public Company)

Temporalities behind the paywall: examining patterns of data flow and temporalities within social media platform APIs

ABSTRACT Multiple temporalities emerge with the participation of technologies and digital objects in shaping how time is perceived and articulated. By employing the concepts of data mobility and data friction, this paper scrutinizes the multiple temporalities generated and shaped in the movement of social media data via Application Programming Interfaces (APIs). Based on a document analysis of Twitter/X API documentation and changelogs, this study identifies three patterns of data mobility, each of which is accompanied by frictions that represent the conflicts between different data flows, data formats, temporal logics, and protocols. Specifically, the first pattern refers to social media data moving in fragments with different levels of granularity, which shapes fragmented and asynchronous temporalities. The second pattern refers to the composition of data movement and processes of verification of digital identities, which generates a compound temporality due to the combination of data and practice flows with different temporal logics. The third pattern refers to the asynchronicity between data collection and data analysis, setting real-timeness as the platform's primary goal. The findings enrich the examination of network time in the context of social media data movement. This article also reveals that rather than moving in an uninterrupted, real-time, and frictionless way, social media data moves in fragmented, asynchronous, and compound ways maintained by the platform's API paywall. By constructing these patterns of data mobility, the platform participates in shaping how time is perceived and how multiple temporalities are assembled.

Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022

ObjectivesHealth research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of...

Enabling Seamless Interoperability of Digital Systems in Smart Cities Using API: A Systematic Literature Review

ABSTRACT Smart cities are envisioned to achieve an urban space where systems are connected to provide value added services to citizens and stakeholders, improve quality of life, and enhance institutional effectiveness. In smart cities, digital systems are deployed that comprise a wide range of service providers who collaborate to provide digital services to citizens. Hence, there is a need for accessible data that can be used to develop innovative applications and services to improve citizens’ quality of life. Unfortunately, achieving interoperability of digital systems is challenging due to isolated systems referred to as vertical silos, which cannot communicate with each other due to lack of standardization of interfaces. Thus, interoperability is seen as a barrier for the seamless transfer of data between systems deployed in smart cities. In order to facilitate interoperability of systems in smart cities, this article conducts a Systematic Literature Review (SLR) to investigate the role of Application Programming Interface (API) to enable seamless interoperability in smart cities. An SLR was employed to guide the selection of studies retrieved to adequately address the formulated research questions. Findings from this study present the importance of interoperability in smart urban environments and help in providing an overview and categorization of existing interoperability solutions to make cities smarter. The findings also identified prior interoperability approaches in smart cities. Findings present a case scenario that depicts how APIs support seamless interoperability among digital systems in a smart city environment. Lastly, the findings elaborate on available API management tools and further suggest that APIs can significantly contribute to improve interoperability between digital systems and decrease barriers in providing innovative digital services in smart cities.

CDN modelling with API integration in public information systems of Indonesia: A simulation-based approach

This study investigates the integration of Content Delivery Networks (CDNs) and Application Programming Interfaces (APIs) within Indonesia's public information systems, modelled across the country’s administrative hierarchy. The simulation measures key performance metrics such as Time-to-Live (TTL), Cache Hit Ratio (CHR), Latency, Throughput, and Bandwidth Consumption across five different scenarios. These scenarios involve varying points of origin for content delivery, from the Central Government down to individual villages, reflecting Indonesia's administrative structure. The study demonstrates that CDN integration significantly improves data delivery efficiency by reducing latency, optimizing bandwidth consumption, and increasing cache effectiveness. These results align with the Indonesian government’s goals of enhancing public information dissemination and supporting digital transformation efforts.

The influences of social support expressed from doctors and disclosed from peers on patient decision-making: an analysis from the online health community

Patients are turning to OHCs to deal with the stresses and complications of infertility. As a stigmatized disease, infertile patients may value informational support and emotional support differently, which is ignored in existing studies. Based on social support theory, this study aims to reveal the role of informational support and emotional support of doctors in infertile patient decision-making. We select Haodf.com as our data source and we collected information for all new patients of 2,989 doctors. we use Jieba word segmentation tool and TF-IDF algorithm to segment word and find keywords. Then random forest algorithm is used to classify 750 training texts and 250 test texts. We measure all the treatment experience of these new patients between September and December 2022 and then take the average value (TreatExpi). The sentiment analysis application program interface from Baidu AI-NLP is used to measure emotion state of patients and then the average value is included (EmoStai). In order to eliminate the influence of other factors, control variables including the number of patients online (#Patienti), the recommendation (Recommendationi), hospital level (Hleveli), doctor medical title (Mtitlei) and service price (Serpi). First, the effects of social support expressed from doctor and social support disclosed from peers show heterogeneity on patient decision-making. Second, the influences of informational support and emotional support also show difference. Third, the above relationships change with the characteristics of patients. Detecting different types and different sources of social support activities via text mining contributes to between understanding patient decision behavior in OHCs. This study contributes social support theory and OHC studies, and helps the management and design OHCs.

Describing ZYN-related content on TikTok: content analysis.

Nicotine pouches are the fastest-growing oral smokeless tobacco or nicotine product category in the United States, and there are concerns about their potential appeal to youth. Few studies have surveilled discussions about nicotine pouches on TikTok, an audiovisual platform popular among youth. To address this gap, this study conducted a content analysis of TikTok posts related to the leading nicotine pouch brand, ZYN. Publicly available posts (N=665) from 10 ZYN-related hashtags were collected from the TikTok Application Programming Interface between January 2023 and February 2024. The number of likes, comments, shares, and plays were collected for each post and the number of followers for each account profile was recorded. Posts were coded for themes like product marketing and use, health warnings, and demographics. The most prevalent theme was humor (n=581, 87.4%), followed by product use (n=378, 56.8%), and branded merchandise (n=378, 56.8%). Young people (i.e., persons in the video who appeared younger than 30) were identified in 48.3% (n=321) of posts, and 61.5% (n=409) of posts featured people who appeared to be male. Cessation (n=12, 1.8%) was an uncommon theme. Most posts came from non-commercial accounts (n=420, 63.1%), followed by commercial accounts (n=158, 23.8%), and content creators/influencers (n=87, 13.1%). This study showed that ZYN-related posts on TikTok often featured pro-tobacco themes, such as humor and product use, while rarely mentioning anti-tobacco themes like cessation. Future research should examine the relationship between exposure to pro-tobacco content on TikTok and tobacco-related attitudes and behaviors. This study highlighted the potential role of TikTok in normalizing tobacco and nicotine product use, and in particular ZYN product use, including among youth. While increased enforcement of online tobacco control policies can help reduce exposure to ZYN-related content in youth, prevention programs and interventions that incorporate tobacco-related digital media literacy are needed to inoculate adolescents and young adults from the pro-tobacco content found online.

DSKIPP: A Prompt Method to Enhance the Reliability in LLMs for Java API Recommendation Task

ABSTRACTIn the realm of software development, selecting the appropriate Java application programming interfaces (APIs) from a vast pool remains a significant challenge for developers. This research addresses this complexity by tackling the limitations of current API recommendation methods, which often struggle to align API suggestions with the specific queries and development contexts. In this paper, we introduce a novel prompt method named DSKIPP (Development Scenario, key Knowledge and Intention's Progressive Prompt), designed to enhance the efficiency of large language models (LLMs) in Java API recommendations. Firstly, we devise an overview of DSKIPP which conducts LLMs through a sequential process: first, inferring the package level, followed by the class level, and ultimately the method level as an API comprises three distinct components at varying levels—package, class and method. Secondly, at each level, DSKIPP assists LLMs in deducing the development scenario associated with a query and the essential key knowledge relevant to that scenario. This approach enables LLMs to gain a more profound contextual understanding of the query's intention. Moreover, during the inference process at the class and method level, we implement a self‐check mechanism enabling LLMs to validate the results and ensure a more reasoned and reliable outcome. To validate the efficiency of DSKIPP, comparison and ablation experiments are both conducted within Java programming environment. The comparison results affirm that our method outperforms the current state‐of‐the‐art technologies in API recommendation tasks, while the ablation results shed light on why DSKIPP can enhance the reliability of API recommendations in LLMs. This research contributes to the field by offering a more reliable and context‐sensitive solution for API recommendation in software development.

The Weather On-Demand Framework

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.

Can large language models fully automate or partially assist paper selection in systematic reviews?

Background/aimsLarge language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building,...

Individual DOI minting for Open Repository: a script for creating a DOI on demand for a DSpace repository

Digital Object Identifiers (DOIs) are a key persistent identifier in the publishing landscape to ensure the discoverability and citation of research products. Minting DOIs can be a time-consuming task for repository librarians. This process can be automated since the metadata for DOIs is already in the repository record and DataCite, a DOI minting organization, and Open Repository, a DSpace repository platform, both have application programming interfaces (APIs). Existing software enables bulk DOI minting. However, the institutional repository at UMass Chan Medical School contains a mixture of original materials that need DOIs (dissertations, reports, data, etc.) and previously published materials that already have DOIs such as journal articles. An institutional repository librarian and her librarian colleague with Python experience embarked on a paired programming project to create a script to mint DOIs on demand in DataCite for individual items in the institution’s Open Repository instance. The pair met for one hour each week to develop and test the script using combined skills in institutional repositories, metadata, DOI minting, coding in Python, APIs, and data cleaning. The project was a great learning opportunity for both librarians to improve their Python coding skills. The new script makes the DOI minting process more efficient, enhances metadata in DataCite, and improves accuracy. Future script enhancements such as automatically updating repository metadata with the new DOI are planned after the repository upgrade to DSpace 7.

Exploring Digital-Only Banking in Malaysia: Technological Advancements, Regulatory Landscape, and Business Implications

Digital banking in Malaysia is undergoing rapid transformation, driven by technological advancements, regulatory frameworks, and evolving consumer preferences. This shift is marked by the adoption of critical technologies such as Application Programming Interfaces (APIs), low-code technology, cloud computing and artificial intelligence (AI) which are redefining how financial services are delivered. These innovations enhance operational efficiency, enable seamless integrations, and offer personalized customer experiences, positioning digital banking as a cornerstone of Malaysia’s financial ecosystem. Concurrently, regulatory measures introduced by Bank Negara Malaysia (BNM), including digital banking licenses and consumer protection policies, have facilitated a conducive environment for growth while ensuring financial inclusion and stability. Despite these advancements, the transition to digital-only banking is not without challenges. Cybersecurity threats, compliance complexities and technological dependency pose significant risks to both financial institutions and consumers. Addressing these issues requires a robust cybersecurity framework, continuous innovation and collaborative efforts among policymakers, industry players and technology developers. This paper reviews critical technologies shaping the industry, explores the regulatory landscape, evaluates security concerns, and examines the business implications and opportunities. The findings provide insights into the future of digital banking in Malaysia, focusing on innovations, challenges and potential growth areas.

Unipept in 2024: Expanding Metaproteomics Analysis with Support for Missed Cleavages and Semitryptic and Nontryptic Peptides.

Unipept, a pioneering software tool in metaproteomics, has significantly advanced the analysis of complex ecosystems by facilitating both taxonomic and functional insights from environmental samples. From the onset, Unipept's capabilities focused on tryptic peptides, utilizing the predictability and consistency of trypsin digestion to efficiently construct a protein reference database. However, the evolving landscape of proteomics and emerging fields like immunopeptidomics necessitate a more versatile approach that extends beyond the analysis of tryptic peptides. In this article, we present a significant update to the underlying index structure of Unipept, which is now powered by a Sparse Suffix Array index. This advancement enables the analysis of semitryptic peptides, peptides with missed cleavages, and nontryptic peptides such as those encountered in other research fields such as immunopeptidomics (e.g., MHC- and HLA-peptides). This new index benefits all tools in the Unipept ecosystem such as the web application, desktop tool, application programming interface (API), and command line interface. A benchmark study highlights significantly improved performance in handling missed cleavages, preserving the same level of accuracy.

The pragmatics of hashtags in French tweets

Abstract Even though France is not a bilingual country, French Twitter users are using various hashtags in English. The hashtag, which was originally created on Twitter, is a popular symbol widely used in the virtual world as well as in the real world. The goal of the hashtag was to organize topics of discussion relative to common themes. Even though this remains its primary usage, other functions for hashtags have emerged since its creation. In a Tag and Commentary hashtag analysis, this study investigates the pragmatic functions and positions of 755 English hashtags on Twitter from all users geo-tagged in France. The data was collected in all the regions of France through Twitter’s API (Application program interface) using the software RStudio. The results show that English hashtags are predominantly used for advertising purposes at the end of a tweet. However, instances of Commentary hashtags are also being used to add an evaluative role regarding the main body of tweets. Through this analysis, the study contributes to a deeper comprehension of online linguistic practices among French users, shedding light on the complex dynamics between language, technology, and communication in today’s digital era.

US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study.

Twitter (subsequently rebranded as X) is acknowledged by US health agencies, including the US Centers for Disease Control and Prevention (CDC), as an important public health communication tool. However, there is a lack of data describing its use by state health agencies over time. This knowledge is important amid a changing social media landscape in the wake of the COVID-19 pandemic. The study aimed to describe US state health agencies' use of Twitter from 2012 through 2022. Furthermore, we organized our data collection and analysis around the theoretical framework of the networked public to contribute to the broader literature on health communication beyond a single platform. We used Twitter application programming interface data as indicators of state health agencies' engagement with the 4 key qualities of communication in a networked public: scalability, persistence, replicability, and searchability. To assess scalability, we calculated tweet volume and audience engagement metrics per tweet. To assess persistence, we calculated the portion of tweets that were manual retweets or included an account mention. To assess replicability, we calculated the portion of tweets that were retweets or quote tweets. To assess searchability, we calculated the portion of tweets using at least 1 hashtag. We observed a COVID-19 pandemic-era shift in state health agency engagement with scalability. The overall volume of tweets increased suddenly from less than 50,000 tweets in 2019 to over 94,000 in 2020, resulting in an average of 5.3 per day. Though mean tweets per day fell in 2021 and 2022, this COVID-19 pandemic-era low was still higher than the pre-COVID-19 pandemic peak. We also observed a more fragmented approach to searchability aligning with the start of the COVID-19 pandemic. More state-specific hashtags were among the top 10 during the COVID-19 pandemic, compared with more general hashtags related to disease outbreaks and natural disasters in years before. We did not observe such a clear COVID-19 pandemic-era shift in engagement with replicability. The portion of tweets mentioning a CDC account gradually rose and fell around a peak of 7.0% in 2018. Similarly, the rate of retweets of a CDC account rose and fell gradually around a peak of 5.4% in 2018. We did not observe a clear COVID-19 pandemic-era shift in persistence. The portion of tweets mentioning any account reached a maximum of 21% in 2013. It oscillated for much of the study period before dropping off in 2021 and reaching a minimum of 10% in 2022. Before 2018, the top 10 mentioned accounts included at least 2 non-CDC or corporate accounts. From 2018 onward, state agencies were much more prominent. Overall, we observed a more fragmented approach to state health agency communication on Twitter during the pandemic, prioritizing volume over searchability, formally replicating existing messages, and leaving traces of interactions with other accounts.