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

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.

Keywords: Twitter messaging; emotions; social networks; text mining.

Figure 1

Graph of reduced pain-related tweet corpus. Each term contained within corpus is represented by a point; point size corresponds to the total degree centrality of the associated term. The color of each point indicates membership to a modularity community. Whenever a term is associated with another term within a given tweet, the 2 points are connected by a line, or edge; edge width corresponds to the frequency of association between the 2 connected terms.

Figure 2

Percentage of terms contained within 161 modularity communities.

Figure 3

Percentage of pain-related tweets with positive sentiment in selected North American cities. Larger diameter circles indicate higher proportions of positive sentiment in tweets containing the term “pain.”.

Figure 4

Percentage of pain-related tweets that contained date and time stamps with positive sentiment over a 24-hour period. Times were adjusted from UTC to local times according to geographic location.

Figure 5

Panel of retweet networks for (A) pain, (B) #pain, (C) happy, (D) excitement, (E) sad, (F) fear, (G) tired, (H) anguish, (I) apple, (J) Manchester United, and (K) Obama. Each circle indicates a node, or Twitter user, and each line connecting the circles represents an edge, or a mention of 1 user in the tweet of another. Each edge is directional in that it “points” from the originating Twitter user to the recipient Twitter user. Node size reflects the degree centrality of the node, line thickness reflects the number of connections between nodes, and color reflects the connectedness community of a node.

Figure 6

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

Figure 7

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