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The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.

Tighe PJ, Goldsmith RC, Gravenstein M, Bernard HR, Fillingim RB - J. Med. Internet Res. (2015)

Bottom Line: The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama.Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters.Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, United States. ptighe@anest.ufl.edu.

ABSTRACT

Background: Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.

Objective: The aim was to examine the type, context, and dissemination of pain-related tweets.

Methods: We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.

Results: The most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).

Conclusions: Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

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Number of nodes (blue) and modularity communities (red) per retweet network.
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figure7: Number of nodes (blue) and modularity communities (red) per retweet network.

Mentions: Similar to the results for weakly connected network components, the number of modularity communities in proportion to node count was greater for emotional terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Obama (0.25), and Manchester United (0.14) (Figure 7). Maximum in-degree centrality scores were greater than out-degree centrality for all terms, although the median numbers for all centrality scores remained between 0 and 1 for all terms (Multimedia Appendix 7). Maximum in-degree centrality scores were greater for objective terms in comparison with emotional terms. In particular, there were statistically significant differences between “apple” and “pain” (mean score difference=−65, P=.003, effect size=0.10), “excitement” and “pain” (mean score difference=−70, P=.001, effect size=0.10), “Manchester United” and “pain” (mean score difference=−167, P<.001, effect size=0.23), and “fear” and “pain” (mean score difference=−175, P<.001, effect size=0.23) for in-degree centrality. For out-degree centrality, there were statistically significant differences between “Manchester United” and “pain” (mean score difference=182, P<.0001, effect size=0.25), “fear” and “pain” (mean score difference=163, P<.001, effect size=0.21), “Obama” and “pain” (mean score difference=79, P=<.001 effect size=0.10), and “apple” and “pain” (mean score difference=65, P=.002, effect size=0.10). For total degree centrality, there were only statistically significant differences between “Obama” and “pain” (mean score difference=79, P<.001, effect size=0.13), and tired and pain (mean score difference=−37, P=.002, effect size=0.10) (Multimedia Appendix 8).


The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.

Tighe PJ, Goldsmith RC, Gravenstein M, Bernard HR, Fillingim RB - J. Med. Internet Res. (2015)

Number of nodes (blue) and modularity communities (red) per retweet network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4400316&req=5

figure7: Number of nodes (blue) and modularity communities (red) per retweet network.
Mentions: Similar to the results for weakly connected network components, the number of modularity communities in proportion to node count was greater for emotional terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Obama (0.25), and Manchester United (0.14) (Figure 7). Maximum in-degree centrality scores were greater than out-degree centrality for all terms, although the median numbers for all centrality scores remained between 0 and 1 for all terms (Multimedia Appendix 7). Maximum in-degree centrality scores were greater for objective terms in comparison with emotional terms. In particular, there were statistically significant differences between “apple” and “pain” (mean score difference=−65, P=.003, effect size=0.10), “excitement” and “pain” (mean score difference=−70, P=.001, effect size=0.10), “Manchester United” and “pain” (mean score difference=−167, P<.001, effect size=0.23), and “fear” and “pain” (mean score difference=−175, P<.001, effect size=0.23) for in-degree centrality. For out-degree centrality, there were statistically significant differences between “Manchester United” and “pain” (mean score difference=182, P<.0001, effect size=0.25), “fear” and “pain” (mean score difference=163, P<.001, effect size=0.21), “Obama” and “pain” (mean score difference=79, P=<.001 effect size=0.10), and “apple” and “pain” (mean score difference=65, P=.002, effect size=0.10). For total degree centrality, there were only statistically significant differences between “Obama” and “pain” (mean score difference=79, P<.001, effect size=0.13), and tired and pain (mean score difference=−37, P=.002, effect size=0.10) (Multimedia Appendix 8).

Bottom Line: The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama.Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters.Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, United States. ptighe@anest.ufl.edu.

ABSTRACT

Background: Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.

Objective: The aim was to examine the type, context, and dissemination of pain-related tweets.

Methods: We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.

Results: The most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).

Conclusions: Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

Show MeSH
Related in: MedlinePlus