Dataset Information

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

ABSTRACT: This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.

SUBMITTER: Subirats L

PROVIDER: S-EPMC6163744 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Publications

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Subirats Laia L Reguera Natalia N Bañón Antonio Miguel AM Gómez-Zúñiga Beni B Minguillón Julià J Armayones Manuel M

International journal of environmental research and public health 20180830 9

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextB ...[more]

PMID: 30200209

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Project description:BACKGROUND:Sudden unexpected infant death (SUID), which includes the diagnosis of sudden infant death syndrome (SIDS), is a leading cause of infant mortality in the United States. Despite prevention efforts, many parents continue to create unsafe infant sleep environments and use potentially dangerous infant sleep and monitoring devices, ultimately leading to sleep-related infant deaths. Analyzing Facebook conversations regarding SIDS may offer a unique maternal perspective to guide future research and prevention efforts. OBJECTIVE:This study aims to describe and analyze conversations among mothers engaged in discussions about SIDS on a Facebook mother's group. We were interested in understanding maternal knowledge of SIDS, identifying information sources for SIDS, describing actual infant sleep practices, exploring opinions regarding infant sleep products and monitoring devices, and discovering evidence of provider communication regarding SIDS. METHODS:We extracted and analyzed 20 posts and 912 comments from 512 mothers who participated in a specific Facebook mother's group and engaged in conversations about SIDS. There were 2 reviewers who coded the data using qualitative descriptive content analysis. Themes were induced after discussion among researchers and after the study objectives were addressed. RESULTS:The theme of social support emerged, specifically informational and emotional support. A variety of informational sources for SIDS and safe sleep were identified, as was a continuum of infant sleep practices (ranging from unsafe to safe sleep per the American Academy of Pediatrics standards). There was widespread discussion regarding infant sleep products and monitoring devices. Embedded within conversations were (1) confusion among commonly used medical terminology, (2) the practice of unsafe infant sleep, (3) inconsistency in provider communication about SIDS, and (4) maternal anxiety regarding SIDS. CONCLUSIONS:We uncovered new findings in this analysis, such as the commonality of infant sleep products and monitoring devices and widespread maternal anxiety regarding SIDS. Additionally, mothers who participated in the Facebook group provided and received informational and emotional support regarding SIDS via this social media format. Such results can guide future prevention efforts by informing health communication regarding SUID and safe sleep. Future provider and public health agency communication on the topic of SUID and safe sleep should be simple and clear, address infant sleep products and monitoring devices, address maternal anxiety regarding SIDS, and address the common practice of unsafe sleep.

Project description:BackgroundFacebook can be a suitable platform for public health interventions. Facebook users can express their reaction to the given social media content in many ways using interaction buttons. The analysis of these interactions can be advantageous in increasing reach and engagement of public health interventions.ObjectiveThis research aimed at understanding how Facebook users' interactions correlate with organic reach and engagement regarding the same smoking cessation support contents.MethodsThe study population consisted of Facebook users who were reached by a public smoking cessation support page without advertising. We included 1025 nonpaid Facebook posts (N=1025) which used smoking cessation strategies based on a motivational interviewing counseling style. The following data were collected from the "Post Details": the number of people who saw the given nonpaid content (organic reach) which consisted of fan and nonfan reach according to previous "page like" activity; each rate of "engagement indicators" (such as the symbols of "like," "love," "haha," "wow," "sad," "angry"; or other interactions: "shares," "comments," "clicks"); and the rate of negative Facebook interactions (eg, "post hides" or "unlike of page"). Overall, these data were analyzed with the Spearman correlation method.ResultsSurprisingly, we found a significant negative correlation between organic reach and the "like" reaction (rs=-0.418; P<.001). The strongest significant positive correlations of organic reach were observed with the "haha" reaction (rs=0.396; P<.001), comments (rs=0.368; P<.001), and the "love" reaction (rs=0.264; P<.001). Furthermore, nonfan reach correlated positively with "shares" (rs=0.388; P<.001) and clicks (rs=0.135; P<.001), while fan reach correlated positively with the "haha" reaction (rs=0.457; P<.001), comments (rs=0.393; P<.001), and the "love" reaction (rs=0.310; P<.001). Contrary to expectations, the "like" reaction was sharply separated by significant negative correlations from "wow" (rs=-0.077; P=.013), "sad" (rs=-0.120; P<.001), "angry" reactions (rs=-0.136; P<.001), and comments (rs=-0.130; P<.001). Additionally, a high rate of negative Facebook interactions was significantly associated with "wow" (rs=0.076; P=.016) and "sad" reactions (rs=0.091; P=.003).ConclusionsThis study has shown that it is possible to hypothesize a disadvantage of the "like" reaction and advantages of other interactions (eg, the "haha" reaction or "comments") in content algorithmic ranking on Facebook. In addition, the correlational analysis revealed a need of a further categorization to fan-specific interactions (eg, "haha" or "love" reactions) and nonfan-specific interactions (eg, "shares" and "clicks"). Regarding the direction of the correlations, these findings suggest that some interactions (eg, negative Facebook interactions, "wow," "sad," and "angry" reactions) may decrease the engagement, while other interactions ("like," "love," "haha" reactions, "shares," and "clicks") may increase the engagement during Facebook-based smoking cessation interventions. This hypothesis-generating research offers an important insight into the relationship between organic reach, engagement, and Facebook users' interactions for public health professionals who design Facebook-based interventions.

Dataset Information

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Publications

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Similar Datasets

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