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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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Total (blue) and giant component (red) nodes within retweet networks.
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figure6: Total (blue) and giant component (red) nodes within retweet networks.

Mentions: Of 16,500 tweets equally distributed across 11 search terms, 48.28 % (7967/16,500) were involved in retweet networks. By visual analysis of the retweet networks, those pertaining to “pain” exhibited greater sparsity and lower connectedness than did those pertaining to “apple,” “Manchester United,” and “Obama” (Figure 5). The Obama network had the greatest number of retweeting nodes (964), and the Manchester United network had the greatest number of edges (n=827) (Multimedia Appendix 6). Network diameter, average path length, and network density did not differ greatly between the compared networks. The number of weakly connected network components, in proportion to the total number of nodes, was greater for emotional terms when compared with specific objects, ranging from a minimum of 0.14 for Manchester United to between 0.37 for pain, 0.43 for happy, and 0.45 for tired. By contrast, the objective terms overall maintained the greatest percentage of their nodes within the giant component (Figure 6). Manchester United’s network maintained 47% percent of its nodes within the giant component, followed by 29% for Obama and 25% for apple. The emotional terms exhibited lower percentages at 9% for #pain, 4% for pain, 3% for sad, and 2% for happy. An important exception to this trend is the network for fear, which maintained 56% of its nodes within the giant component


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)

Total (blue) and giant component (red) nodes within retweet networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure6: Total (blue) and giant component (red) nodes within retweet networks.
Mentions: Of 16,500 tweets equally distributed across 11 search terms, 48.28 % (7967/16,500) were involved in retweet networks. By visual analysis of the retweet networks, those pertaining to “pain” exhibited greater sparsity and lower connectedness than did those pertaining to “apple,” “Manchester United,” and “Obama” (Figure 5). The Obama network had the greatest number of retweeting nodes (964), and the Manchester United network had the greatest number of edges (n=827) (Multimedia Appendix 6). Network diameter, average path length, and network density did not differ greatly between the compared networks. The number of weakly connected network components, in proportion to the total number of nodes, was greater for emotional terms when compared with specific objects, ranging from a minimum of 0.14 for Manchester United to between 0.37 for pain, 0.43 for happy, and 0.45 for tired. By contrast, the objective terms overall maintained the greatest percentage of their nodes within the giant component (Figure 6). Manchester United’s network maintained 47% percent of its nodes within the giant component, followed by 29% for Obama and 25% for apple. The emotional terms exhibited lower percentages at 9% for #pain, 4% for pain, 3% for sad, and 2% for happy. An important exception to this trend is the network for fear, which maintained 56% of its nodes within the giant component

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