Digital ethnographic analysis of prostate cancer discussions on social media.
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ABSTRACT:
SUBMITTER: Eleswarapu SV
PROVIDER: S-EPMC8988692 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
Digital ethnographic analysis of prostate cancer discussions on social media.
Eleswarapu Sriram V SV Jiang Tommy T Mills Jesse N JN Osadchiy Vadim V
BJUI compass 20201231 2
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Project description:BackgroundSentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods.ObjectivePopular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. Sentiment analysis methods were also examined regarding ease of programming, monetary cost, and other practical considerations.MethodsEvaluation methods included descriptive statistics, receiver operating characteristic (ROC) curve analysis, confusion matrices, Cohen κ, accuracy, specificity, precision, sensitivity (recall), F1-score harmonic mean, and the Matthews correlation coefficient. An inductive, iterative approach to content analysis of the data was used to obtain manual sentiment codes.ResultsA subset of comments were analyzed by a second coder, producing good agreement between the 2 coders' judgments (κ=0.734). YouTube social media about the opioid crisis had many more negative comments (4286/4871, 88%) than positive comments (79/662, 12%), making it possible to evaluate the performance of sentiment analysis models in an unbalanced dataset. The tone summary measure from LIWC-22 performed better than other tools for estimating the prevalence of negative versus positive sentiment. According to the ROC curve analysis, VADER was best at classifying manually coded negative comments. A comparison of Cohen κ values indicated that NLP tools (VADER, followed by LIWC's tone and T2D) showed only fair agreement with manual coding. In contrast, ChatGPT 4.0 had poor agreement and failed to generate binary sentiment scores in 2 out of 3 attempts. Variations in accuracy, specificity, precision, sensitivity, F1-score, and MCC did not reveal a single superior model. F1-score harmonic means were 0.34-0.38 (SD 0.02) for NLP tools and very low (0.13) for ChatGPT 4.0. None of the MCCs reached a strong correlation level.ConclusionsResearchers studying negative emotions, public worries, or dissatisfaction with social media face unique challenges in selecting models suitable for unbalanced datasets. We recommend VADER, the only cost-free tool we evaluated, due to its excellent discrimination, which can be further improved when the comments are at least 100 characters long. If estimating the prevalence of negative comments in an unbalanced dataset is important, we recommend the tone summary measure from LIWC-22. Researchers using T2D must know that it may only score some data and, compared with other methods, be more time-consuming and cost-prohibitive. A general-purpose large language model, ChatGPT 4.0, has yet to surpass the performance of NLP models, at least for unbalanced datasets with highly prevalent (7:1) negative comments.
| S-EPMC11784633 | biostudies-literature
Project description:Previous studies in the literature have shown that Digital Marketing (DM) can be a factor influencing competitiveness and the performance of marketing actions so that a company can correctly reach the target audience. From this literary assumption, it is imperative to evaluate tools that can help marketing managers in decision-making to select the most social media platform to use the DM. In this context, this article aims to analyze the performance of social media platforms for using the strategies DM in organizations. from the point of view of experts from different organizations that use DM for marketing actions. To provide empirical evidence, the selection of criteria was based on the eight dimensions of the DM mix to the lens of multi-criteria decision-making based on the inter-criteria correlation method (CRITIC). This method adhered to the order of preference by similarity technique to find an ideal solution with the help of the Fuzzy TOPSIS environment. The results discovered corroborate the previous literature and stand out, mainly by showing that the main criterion with greater weight among the eight analyzed dimensions was promotion (Cr5). Based on this finding, these results indicate that the best performing alternative for DM actions for the specific case was the A1-Facebook platform. This article brings reflections, and presents a robust tool of quantitative analysis in addition to bringing theoretical and managerial contributions that can direct DM strategies in the consumer's journey through social media platforms. Supplementary Information The online version contains supplementary material available at 10.1057/s41270-023-00211-z.
| S-EPMC9936493 | biostudies-literature
Project description:ObjectivesGiven the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.Materials and methodsWe collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.ResultsTwo types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events.DiscussionAlthough ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.ConclusionSpikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.
| S-EPMC7651954 | biostudies-literature
Project description:Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
| S-EPMC4912099 | biostudies-literature
Project description:With the proliferation of social media networks, online discussions can serve as a microcosm of the greater public opinion about key issues that affect society as a whole. Online discussions have been catalyzed by the COVID-19 pandemic and have magnified challenges experienced by older adults, health care professionals, and caregivers of long-term care (LTC) residents. Our main goal was to examine how online discussions and public perceptions about LTC practices have been impacted by the COVID-19 pandemic. We conducted a content analysis of Twitter posts about LTC to understand the nature of social media discussions regarding LTC practices prior to (March to June 2019) and following the declaration of the COVID-19 pandemic (March to June 2020). We found that a much greater number of Twitter posts about LTC was shared during the COVID-19 period than in the year prior. Multiple themes emerged from the data including highlighting concerns about LTC, providing information about LTC, and interventions and ideas for improving LTC conditions. The proportion of posts linked to several of these themes changed as a function of the pandemic. Unsurprisingly, one major new issue that emerged in 2020 is that users began discussing the shortcomings of infection control during the pandemic. Our findings suggest that increased public concern offers momentum for embarking on necessary changes to improve conditions in LTC.
| S-EPMC9580405 | biostudies-literature
Project description:A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia.
| S-EPMC9643343 | biostudies-literature
Project description:BackgroundStigma associated with substance use can have severe negative consequences for physical and mental health and serve as a barrier to treatment. Yet, research on stigma processes and stigma reduction interventions is limited.AimWe use a social media dataset to examine: 1) the nature of stigma-related experience related to substance use; and 2) salient affective and temporal factors in the use of three substances: alcohol, cannabis, and opioids.MethodsWe harvested several years of data pertaining to three substances - alcohol, cannabis, and opioids - from Reddit, a popular social networking platform. For Part I, we selected posts based on stigma-related keywords, performed content analysis, and rendered word clouds to examine the nature of stigma associated with these substances. In Part II, we employed natural language processing in conjunction with hierarchical clustering and visualization to explore temporal and affective factors.ResultsIn Part I, internalized stigma was most commonly exhibited. Anticipated and enacted stigma were less common in posts relating to cannabis compared to the other two substances. Work, home, and school were important contexts in which stigma was observed. Part II showed that temporal markers were prominent; post authors shared stories of substance use journeys, and timelines of their experience with quitting and withdrawals. Shame, sadness, anxiety, and fear were common, with shame being more prominent in alcohol-related posts.ConclusionOur findings highlight the importance of contextual factors in substance use recovery and stigma reduction and offer directions for future interventions.
| S-EPMC9948814 | biostudies-literature
Project description:BackgroundGenetic testing, particularly for BRCA1/2, is increasingly important in prostate cancer (PCa) care, with impact on PCa management and hereditary cancer risk. However, the extent of public awareness and online discourse on social media is unknown, and presents opportunities to identify gaps and enhance population awareness and uptake of advances in PCa precision medicine.ObjectiveThe objective of this study was to characterize activity and engagement across multiple social media platforms (Twitter, Facebook, and YouTube) regarding BRCA and genetic testing for PCa compared with breast cancer, which has a long history of public awareness, advocacy, and prominent social media presence.MethodsThe Symplur Signals online analytics platform was used to obtain metrics for tweets about (1) #BRCA and #breastcancer, (2) #BRCA and #prostatecancer, (3) #genetictesting and #breastcancer, and (4) #genetictesting and #prostatecancer from 2016 to 2020. We examined the total number of tweets, users, and reach for each hashtag, and performed content analysis for a subset of tweets. Facebook and YouTube were queried using analogous search terms, and engagement metrics were calculated.ResultsDuring a 5-year period, there were 10,005 tweets for #BRCA and #breastcancer, versus 1008 tweets about #BRCA and #prostatecancer. There were also more tweets about #genetictesting and #breastcancer (n=1748), compared with #genetic testing and #prostatecancer (n=328). Tweets about genetic testing (12,921,954) and BRCA (75,724,795) in breast cancer also had substantially greater reach than those about PCa (1,463,777 and 4,849,905, respectively). Facebook groups and pages regarding PCa and BRCA/genetic testing had fewer average members, new members, and new posts, as well as fewer likes and followers, compared with breast cancer. Facebook videos had more engagement than YouTube videos across both PCa and breast cancer content.ConclusionsThere is substantially less social media engagement about BRCA and genetic testing in PCa compared with breast cancer. This landscape analysis provides insights into strategies for leveraging social media platforms to increase public awareness about PCa germline testing, including use of Facebook to share video content and Twitter for discussions with health professionals.
| S-EPMC8550715 | biostudies-literature
Project description:ImportanceHuman papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies.ObjectiveTo develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media.Design, setting, and participantsThis retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018.Main outcomes and measuresThe prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine.ResultsA total of 1 431 463 English-language posts from 486 116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P < .001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P < .001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P < .001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11 531 of 17 106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17 197 [56.1%] and 1080 of 1793 [60.2%]).Conclusions and relevanceThis cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.
| S-EPMC7666426 | biostudies-literature
Project description:Sharing experiences with racism (racial discrimination disclosure) has the power to raise awareness of discrimination and spur meaningful conversations about race. Sharing these experiences with racism on social media may prompt a range of responses among users. While previous work investigates how disclosure impacts disclosers and listeners, we extend this research to explore the impact of observing discussions about racial discrimination online-what we call vicarious race talk. In a series of experiments using real social media posts, we show that the initial response to racial discrimination disclosure-whether the response denies or validates the poster's perspective-influences observers' own perceptions and attitudes. Despite observers identifying denial as less supportive than validation, those who observed a denial response showed less responsive attitudes toward the poster/target (Studies 1-3) and less support for discussions about discrimination on social media in general (Studies 2-3). Exploratory findings revealed that those who viewed denial comments also judged the transgressor as less racist, and expressed less support and more denial in their own comments. This suggests that even as observers negatively judge denial, their perceptions of the poster are nonetheless negatively influenced, and this impact extends to devaluing the topic of discrimination broadly. We highlight the context of social media, where racial discrimination disclosure-and how people respond to it-may be particularly consequential.
| S-EPMC11303685 | biostudies-literature