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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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Percentage of terms contained within 161 modularity communities.
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figure2: Percentage of terms contained within 161 modularity communities.

Mentions: The average degree centrality of the reduced pain tween corpus graph was 60.7, with total degree centrality counts for individual terms ranging from 0 to 5652 with a median of 18 (Figure 1). Terms with the highest total degree centrality included “feel” (degree centrality=5652), “don’t” (degree centrality=3375), “love” (degree centrality=3274), “ass” (degree centrality=3049), and “can’t” (degree centrality=2983) (Multimedia Appendix 3). The most common associations between terms, as a function of edge weights, included “laugh” and “watching” (edge weight=566), “don’t” and “feel” (edge weight=395), and “uploaded” and “video” (edge weight=361) (Table 1). A total of 161 modulus-based communities were detected using Louvain’s algorithm (Figure 2). The 10 most common modulus communities accounted for 77% of all terms.


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)

Percentage of terms contained within 161 modularity communities.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure2: Percentage of terms contained within 161 modularity communities.
Mentions: The average degree centrality of the reduced pain tween corpus graph was 60.7, with total degree centrality counts for individual terms ranging from 0 to 5652 with a median of 18 (Figure 1). Terms with the highest total degree centrality included “feel” (degree centrality=5652), “don’t” (degree centrality=3375), “love” (degree centrality=3274), “ass” (degree centrality=3049), and “can’t” (degree centrality=2983) (Multimedia Appendix 3). The most common associations between terms, as a function of edge weights, included “laugh” and “watching” (edge weight=566), “don’t” and “feel” (edge weight=395), and “uploaded” and “video” (edge weight=361) (Table 1). A total of 161 modulus-based communities were detected using Louvain’s algorithm (Figure 2). The 10 most common modulus communities accounted for 77% of all terms.

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