UNASSIGNED: Little is known about how best to reach people with social marketing messages promoting use of clinical HIV and sexually transmitted infection (STI) services.
UNASSIGNED: We evaluated a multiplatform, digital social marketing campaign intended to increase use of HIV/STI testing, treatment, and prevention services among gay, bisexual, and other men who have sex with men (MSM) at an LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and/or questioning) community health center.
UNASSIGNED: We evaluated engagement with a social marketing campaign launched by Open Door Health, the only LGBTQ+ community health center in Rhode Island, during the first 8 months of implementation (April to November 2021). Three types of advertisements encouraging use of HIV/STI services were developed and implemented on Google Search, Google Display, Grindr, and Facebook. Platforms tracked the number of times that an advertisement was displayed to a user (impressions), that a user clicked through to a landing page that facilitated scheduling (clicks), and that a user requested a call to schedule an appointment from the landing page (conversions). We calculated the click-through rate (clicks per impression), conversion rate (conversions per click), and the dollar amount spent per 1000 impressions and per click and conversion.
UNASSIGNED: Overall, Google Search yielded the highest click-through rate (7.1%) and conversion rate (7.0%) compared to Google Display, Grindr, and Facebook (click-through rates=0.4%-3.3%; conversion rates=0%-0.03%). Although the spend per 1000 impressions and per click was higher for Google Search compared to other platforms, the spend per conversion-which measures the number of people intending to attend the clinic for services-was substantially lower for Google Search (US $48.19 vs US $3120.42-US $3436.03).
UNASSIGNED: Campaigns using the Google Search platform may yield the greatest return on investment for engaging MSM in HIV/STI services at community health clinics. Future studies are needed to measure clinical outcomes among those who present to the clinic for services after viewing campaign advertisements and to compare the return on investment with use of social marketing campaigns relative to other approaches.

The COVID-19 has caused not just an unprecedented sanitary crisis but a social crisis, which has affected, among many other fields, the local journalism, which had to adapt to meet the public\'s information needs about coronavirus. In this study we analyzed the evolution of local news about COVID-19 in Spain throughout the pandemic by examining local news articles in social media. Using a unique dataset of over 230k Facebook posts published by Spanish local media organizations during 2020 and 2021, we found evidence that the interest of local media in COVID-19, measured as the proportion of the news related to COVID-19, changed as the pandemic evolved. Our results also show that the interest that local media readers had in COVID-19, measured as the proportion Facebook interactions related to COVID-19, was even higher, and also evolved during the pandemic. Although the interest in COVID-19 of local media and their readers essentially progressed in parallel, we also identified some periods in which they behaved differently. While a fatigue with COVID-19 related news would be expected after 2 years of pandemic, a clear decrease of interest was not observed neither in journalists nor in readers.

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

The world is moving fast toward digital transformation as we live in the artificial intelligence (AI) era. The COVID-19 pandemic accelerates this movement. Chatbots were used successfully to help researchers collect data for research purposes.
To implement a chatbot on the Facebook platform to establish connections with health care professionals who had subscribed to the chatbot, provide medical and pharmaceutical educational content, and collect data for online pharmacy research projects. Facebook was chosen because it has billions of daily active users, which offers a massive potential audience for research projects.
The chatbot was successfully implemented on the Facebook platform following 3 consecutive steps. Firstly, the ChatPion script was installed on the Pharmind website to establish the chatbot system. Secondly, the PharmindBot application was developed on Facebook. Finally, the PharmindBot app was integrated with the chatbot system.
The chatbot responds automatically to public comments and sends subscribers private responses using AI. The chatbot collected quantitative and qualitative data with minimal costs.
The chatbot\'s auto-reply function was tested using a post published on a specific page on Facebook. Testers were asked to leave predefined keywords to test its functionality. The chatbot\'s ability to collect and save data was tested by asking testers to fill out an online survey within Facebook Messenger for quantitative data and answer predefined questions for qualitative data.
The chatbot was tested on 1000 subscribers who interacted with it. Almost all testers (n = 990, 99%) obtained a successful private reply from the chatbot after sending a predefined keyword. Also, the chatbot replied privately to almost all public comments (n = 985, 98.5%) which helped to increase the organic reach and to establish a connection with the chatbot subscribers. No missing data were found when the chatbot was used to collect quantitative and qualitative data.
The chatbot reached thousands of health care professionals and provided them with automated responses. At a low cost, the chatbot was able to gather both qualitative and quantitative data without relying on Facebook ads to reach the intended audience. The data collection was efficient and effective. Using chatbots by pharmacy and medical researchers will help do more feasible online studies using AI to advance health care research.

The COVID-19 pandemic is not only having a heavy impact on healthcare but also changing people\'s habits and the society we live in. Countries such as Italy have enforced a total lockdown lasting several months, with most of the population forced to remain at home. During this time, online social networks, more than ever, have represented an alternative solution for social life, allowing users to interact and debate with each other. Hence, it is of paramount importance to understand the changing use of social networks brought about by the pandemic. In this paper, we analyze how the interaction patterns around popular influencers in Italy changed during the first six months of 2020, within Instagram and Facebook social networks. We collected a large dataset for this group of public figures, including more than 54 million comments on over 140 thousand posts for these months. We analyze and compare engagement on the posts of these influencers and provide quantitative figures for aggregated user activity. We further show the changes in the patterns of usage before and during the lockdown, which demonstrated a growth of activity and sizable daily and weekly variations. We also analyze the user sentiment through the psycholinguistic properties of comments, and the results testified the rapid boom and disappearance of topics related to the pandemic. To support further analyses, we release the anonymized dataset.

Media portrayals of animal cruelty can shape public understanding and perception of animal welfare law. Given that animal welfare law in Australia is guided partially by \'community expectations\', the media might indirectly be influencing recent reform efforts to amend maximum penalties in Australia, through guiding and shaping public opinion. This paper reports on Australian news articles which refer to penalties for animal cruelty published between 1 June 2019 and 1 December 2019. Using the electronic database Newsbank, a total of 71 news articles were included for thematic analysis. Three contrasting themes were identified: (1) laws are not good enough; (2) laws are improving; and (3) reforms are unnecessary. We propose a penalty reform cycle to represent the relationship between themes one and two, and \'community expectations\'. The cycle is as follows: media reports on recent amendments imply that \'laws are improving\' (theme two). Due to a range of inherent factors in the criminal justice system, harsher sentences are not handed down by the courts, resulting in media report of \'lenient sentencing\' (theme one). Hence, the public become displeased with the penal system, forming the \'community expectations\', which then fuel future reform efforts. Thus, the cycle continues.

Alcohol advertising exposure is a risk factor for earlier alcohol initiation and higher alcohol consumption. Furthermore, engagement in digital alcohol marketing, such as liking or sharing an ad on social media, is associated with increased alcohol consumption and binge or hazardous drinking behavior. In light of these challenges, Lithuania has enacted a total prohibition on alcohol advertising, including social media. This study monitored the two most popular social media networks, Facebook and Instagram, to determine compliance with current legislation. In total, 64 Facebook and 51 Instagram profiles were examined. During the 60-day study period, 1442 and 749 posts on the selected Facebook and Instagram profiles, respectively, were published. There were a total of 163 distinct social media alcohol-related posts. Alcohol-related posts accounted for 5.9 percent of total Instagram posts and 8.3 percent of total Facebook posts. Alcohol advertisements accounted for 1.4 percent of all posts (infringement of the Alcohol Control Law). Influencers were responsible for nearly half (45.5 percent) of all observed alcohol-related Instagram posts. The study demonstrates high compliance with Lithuania\'s total alcohol advertising ban on social media and emphasizes the importance of adequately monitoring the growing prominence of influencers on social media.

BACKGROUND: Although the presence of medical societies on social networks (SNs) could be interesting for disseminating professional information, there is no study investigating their presence on SNs.
OBJECTIVE: The aim of this viewpoint is to describe the worldwide presence and activity of national anesthesia societies on SNs.
METHODS: This observational study assessed the active presence (≥1 post in the year preceding the collection date) of the World Federation of Societies of Anesthesiologists member societies on the SNs Twitter, Facebook, Instagram, and YouTube. We collected data concerning each anesthesia society on the World Federation of Societies of Anesthesiologists website.
RESULTS: Among the 136 societies, 66 (48.5%) had an active presence on at least one SN. The most used SN was Facebook (n=60, 44.1%), followed by Twitter (n=37, 27.2%), YouTube (n=26, 19.1%), and Instagram (n=16, 11.8%). The SN with the largest number of followers was Facebook for 52 (78.8%) societies and Twitter for 12 (18.2%) societies. The number of followers was 361 (IQR 75-1806) on Twitter, 2494 (IQR 1049-5369) on Facebook, 1400 (IQR 303-3058) on Instagram, and 214 (IQR 33-955) on YouTube. There was a strong correlation between the number of posts and the number of followers on Twitter (r=0.95, 95% CI 0.91-0.97; P<.001), Instagram (r=0.83, 95% CI 0.58-0.94; P<.001), and YouTube (r=0.69, 95% CI 0.42-0.85; P<.001). According to the density of anesthetists in the country, there was no difference between societies with and without active SN accounts.
CONCLUSIONS: Less than half of national anesthesia societies have at least one active account on SNs. Twitter and Facebook are the most used SNs.

Users considered Social media forums like Facebook, Twitter and blogs as the most prominent social networks in the present age, where users share their views quickly in words and respond to feedback from other users within no time. This study aims to: measure the impact of influencing factors on a particular community when social media forums promote it using a machine learning model. In this research work, we performed an association rule-based method to measure the impact of COVID-19 influencing factors on adolescence when they promoted it on social media. The proposed method gave a remarkable output when we compared it with the different existing approaches. It works well in all respective fields we observed. Last but not least, when compared with survey and official results, the proposed method predicts well, and the obtained results are pretty promising.