Social media can be a significant tool for transportation and transit agencies providing passengers with real-time information on traffic events. Moreover, COVID-19 and other limitations have compelled the agencies to engage with travelers online to promote public knowledge about COVID-related issues. It is, therefore, important to understand the agencies’ communication patterns. In this original study, the Twitter communication patterns of different transportation actors—types of message, communication sufficiency, consistency, and coordination—were examined using a social media data-driven approach applying text mining techniques and dynamic network analysis. A total of 850,000 tweets from 395 different transportation and transit agencies, starting in 2018 and the periods before, during and after the pandemic, were studied. Transportation agencies (federal, state, and city) were found to be less active on Twitter and mostly discussed safety measures, project management, and so forth. By contrast, the transit agencies (local bus and light, heavy, and commuter rail) were more active on Twitter and shared information about crashes, schedule information, passenger services, and so forth. Moreover, transportation agencies shared minimal pandemic safety information than transit agencies. Dynamic network analysis reveals interaction patterns among different transportation actors that are poorly connected and coordinated among themselves and with different health agencies (e.g., Centers for Disease Control and Prevention [CDC] and the Federal Emergency Management Agency [FEMA]). The outcome of this study provides understanding to improve existing communication plans, critical information dissemination efficacy, and the coordination of different transportation actors in general and during unprecedented health crises.
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19
During COVID-19, misinformation on social media affects the adoption of appropriate prevention behaviors. It is urgent to suppress the misinformation to prevent negative public health consequences. Although an array of studies has proposed misinformation suppression strategies, few have investigated the role of predominant credible information during crises. None has examined its effect quantitatively using longitudinal social media data. Therefore, this research investigates the temporal correlations between credible information and misinformation, and whether predominant credible information can suppress misinformation for two prevention measures (i.e. topics), i.e. wearing masks and social distancing using tweets collected from February 15 to June 30, 2020. We trained Support Vector Machine classifiers to retrieve relevant tweets and classify tweets containing credible information and misinformation for each topic. Based on cross-correlation analyses of credible and misinformation time series for both topics, we find that the previously predominant credible information can lead to the decrease of misinformation (i.e. suppression) with a time lag. The research findings provide empirical evidence for suppressing misinformation with credible information in complex online environments and suggest practical strategies for future information management during crises and emergencies.
more »
« less
- Award ID(s):
- 2028012
- NSF-PAR ID:
- 10287237
- Date Published:
- Journal Name:
- ArXivorg
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
null (Ed.)During COVID-19, social media has played an important role for public health agencies and government stakeholders (i.e. actors) to disseminate information regarding situations, risks, and personal protective action inhibiting disease spread. However, there have been notable insufficient, incongruent, and inconsistent communications regarding the pandemic and its risks, which was especially salient at the early stages of the outbreak. Sufficiency, congruence and consistency in health risk communication have important implications for effective health safety instruction as well as critical content interpretability and recall. It also impacts individual- and community-level responses to information. This research employs text mining techniques and dynamic network analysis to investigate the actors’ risk and crisis communication on Twitter regarding message types, communication sufficiency, timeliness, congruence, consistency and coordination. We studied 13,598 pandemic-relevant tweets posted over January to April from 67 federal and state-level agencies and stakeholders in the U.S. The study annotates 16 categories of message types, analyzes their appearances and evolutions. The research then identifies inconsistencies and incongruencies on four critical topics and examines spatial disparities, timeliness, and sufficiency across actors and message types in communicating COVID-19. The network analysis also reveals increased communication coordination over time. The findings provide unprecedented insight of Twitter COVID-19 information dissemination which may help to inform public health agencies and governmental stakeholders future risk and crisis communication strategies related to global hazards in digital environments.more » « less
-
Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.more » « less
-
Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment AccuracyMisinformation runs rampant on social media and has been tied to adverse health behaviors such as vaccine hesitancy. Crowdsourcing can be a means to detect and impede the spread of misinformation online. However, past studies have not deeply examined the individual characteristics - such as cognitive factors and biases - that predict crowdworker accuracy at identifying misinformation. In our study (n = 265), Amazon Mechanical Turk (MTurk) workers and university students assessed the truthfulness and sentiment of COVID-19 related tweets as well as answered several surveys on personal characteristics. Results support the viability of crowdsourcing for assessing misinformation and content stance (i.e., sentiment) related to ongoing and politically-charged topics like the COVID-19 pandemic, however, alignment with experts depends on who is in the crowd. Specifically, we find that respondents with high Cognitive Reflection Test (CRT) scores, conscientiousness, and trust in medical scientists are more aligned with experts while respondents with high Need for Cognitive Closure (NFCC) and those who lean politically conservative are less aligned with experts. We see differences between recruitment platforms as well, as our data shows university students are on average more aligned with experts than MTurk workers, most likely due to overall differences in participant characteristics on each platform. Results offer transparency into how crowd composition affects misinformation and stance assessment and have implications on future crowd recruitment and filtering practices.more » « less
-
more » « less
Abstract Social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. Unfortunately, due to the unmoderated nature of social media platforms such as Twitter, rumors and misinformation are able to propagate widely. Given this, a surfeit of research has studied false rumor diffusion on Twitter, especially during natural disasters. Within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. A prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. Such studies would offer organizations and agencies new tools and ideologies to monitor misinformation on platforms such as Twitter, and make informed decisions on whether or not to use their resources in order to debunk. In this work, we fill the research gap by developing a machine learning framework to predict the veracity of tweets that are spread during crisis events. The tweets are tracked based on the veracity of their content as either true, false, or neutral. We conduct four separate studies, and the results suggest that our framework is capable of tracking multiple cases of misinformation simultaneously, with scores exceeding 87%. In the case of tracking a single case of misinformation, our framework reaches an score of 83%. We collect and drive the algorithms with 15,952 misinformation‐related tweets from the Boston Marathon bombing (2013), Manchester Arena bombing (2017), Hurricane Harvey (2017), Hurricane Irma (2017), and the Hawaii ballistic missile false alert (2018). This article provides novel insights on how to efficiently monitor misinformation that is spread during disasters.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
