Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We also show that these findings hold true across cultures by analyzing tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.Comment: 17 pages, 8 figure

Association of fear of COVID-19 and resilience with psychological distress among health care workers in hospitals responding to COVID-19: analysis of a cross-sectional study

BackgroundIt remains unclear how fear of COVID-19 and resilience are related to psychological distress based on occupations among healthcare workers (HCWs) in hospitals treating patients with COVID-19. We conducted a survey on the mental health of HCWs during the COVID-19 pandemic to determine the relationship between factors such as fear of COVID-19 and resilience as well as mental distress in each occupation of HCWs.MethodsWe conducted a web-based survey among HCWs at seven hospitals treating COVID-19 patients in Japan from December 24, 2020 to March 31, 2021. A total of 634 participants were analyzed, and information regarding their socio-demographic characteristics and employment status was collected. Several psychometric measures were used, including the Kessler’s Psychological Distress Scale (K6), the fear of COVID-19 Scale (FCV-19S), and the Resilience Scale (RS14). Factors related to psychological distress were identified by logistic regression analysis. The association between job title and psychological scales was examined by one-way ANOVA, and t-tests were conducted to examine the association between the FCV-19S and hospital initiatives.ResultsIt was found that nurses and clerical workers were associated with psychological distress without considering FCV-19S or RS14; in a model that included FCV-19S, FCV-19S was associated with psychological distress, but job title was not; when RS14 was considered, resilience was protective. In terms of occupation, FCV-19S was lower among physicians and higher among nurses and clerical workers, while RS14 was higher among physicians and lower among other occupations. Having access to in-hospital consultation regarding infection control as well as to psychological and emotional support was associated with lower FCV-19S.ConclusionBased on our findings, we can conclude that the level of mental distress differed by occupation and the differences in the fear of COVID-19 and resilience were important factors. In order to provide mental healthcare for HCWs during a pandemic, it is important to create consultation services that enable employees to discuss their concerns. In addition, it is important to take steps to strengthen the resilience of HCWs in preparation for future disasters

Replication data for: Concurrent Bursty Behavior of Social Sensors in Sporting Events

Using the Twitter Search API, which allows 180 queries per 15-min window, we compiled a dataset of tweets related to Japan's 2013 Nippon Professional Baseball (NPB) games, including at least one hashtag of NPB teams such as #giants (Yomiuri Giants) and #rakuteneagles (Tohoku Rakuten Golden Eagles). This hashtag-based crawling with multiple crawlers allowed us to obtain the nearly-complete data regarding these sporting events: 528,501 tweets surrounding 19 baseball games from the Climax Series (the annual playoff series) and from the Japan Series (the annual championship series) in the 2013 NPB. We also collected tweets related to Major League Baseball (MLB) games in 2015, including at least one hashtag of the MLB teams such as #Yankees and #BlueJays. We sampled 730,142 tweets from 17 games of New York Yankees from September 11 to 27, 2015

Retweet networks and their cumulative in-degree distributions (<i>P</i>_cum(<i>k</i>)) in the sixth round of the 2013 Japan Series.

<p>The retweet network (A) consists of data generated during 30 min from 19:17, in which more retweets were generated with #rakuteneagles. The retweet network (B) consists of data generated during 30 min from 20:16, in which more retweets were generated with #giants. Green lines and circles denote #giants and blue lines and circles denote #rakuteneagles.</p

Example of the correlation between tweet and retweet time series (<i>r</i>_<i>xy</i>(<i>τ</i>)) for the six round in the 2013 Japan Series (cf. Fig 2).

<p>Example of the correlation between tweet and retweet time series (<i>r</i>_<i>xy</i>(<i>τ</i>)) for the six round in the 2013 Japan Series (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144646#pone.0144646.g002" target="_blank">Fig 2</a>).</p

<i>R</i>_max values between tweet and retweet time series for the 2015 Major League Baseball (Yankees games from September 11 to 27).

<p>(A) <i>R</i>_max values by games (<i>n</i> = 17). Red letters with an underline denote the winning team and blue letters denote the losing team. Y: New York Yankees, B: Toronto Blue Jays, M: New York Mets, R: Tampa Bay Rays, W: Chicago White Sox. (B) Boxplots of <i>R</i>_max in the winning team group and the losing team group, with a significant difference between two groups.</p

Example of tweet and retweet time series (counts per minute) for the Yankees vs. Blue Jays game on September 11, 2015.

<p>Red lines denote tweets and blue dashed lines denote retweets. The upper panel shows tweets for the Yankees (#Yankees) and the lower panel for the Blue Jays (#BlueJays).</p

<i>R</i>_max between tweet and retweet time series for the 2013 Japan Series (A) and the 2013 Climax Series for the Central (B) and Pacific (C) Leagues. Red letters with an underline denote the winning team and blue letters denote the losing team. G: Yomiuri Giants, E: Tohoku Rakuten Golden Eagles, T: Hanshin Tigers, C: Hiroshima Toyo Carp, M: Chiba Lotte Marines, L: Saitama Seibu Lions.

<p><i>R</i>_max between tweet and retweet time series for the 2013 Japan Series (A) and the 2013 Climax Series for the Central (B) and Pacific (C) Leagues. Red letters with an underline denote the winning team and blue letters denote the losing team. G: Yomiuri Giants, E: Tohoku Rakuten Golden Eagles, T: Hanshin Tigers, C: Hiroshima Toyo Carp, M: Chiba Lotte Marines, L: Saitama Seibu Lions.</p