Setting the agenda in environmental crisis: Relationships between tweets, Google search trends, and newspaper coverage during the California drought

Nuanced public responses to droughts and other chronic environmental crises reflect today’s increasingly complex communication ecosystem. At once global and infinitely customizable, this vast array of media and information channels requires existing theory to address the implications of interactions among social media, “traditional” mass media outlets, and information-seeking tools such as search engines. How do these channels intervene in public conversation? What might the agenda-setting perspective have to say? Data collected during peak years of the California drought, 2013–2015, indicate that California residents responded to worsening drought conditions Twitter first, which was the only media behavior directly stimulated by environmental stressors. Google searches stimulated newspaper coverage and Twitter activity, revealing the centrality of search behaviors in this environmental crisis. The findings suggest significant changes to the communication landscape as individual and collective users become increasingly dependent on non-mainstream media channels for information in chronic crisis situations.

Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia

In this study, tweets related to fires in Riau, Sumatra, were identified using carefully selected keywords for the 2014–2019 timeframe. The TAGGS algorithm was applied, which allows for geoparsing based on the user’s nationality and hometown and on direct referrals to specific locations such as name of province or name of city in the message itself. Online newspapers covering Riau were analyzed for the year 2019 to provide additional information about the reasons why fires occurred and other factors, such as impact on people’s health, animal mortality related to ecosystem disruption, visibility, decrease in air quality and limitations in the government firefighting response. Correlation analysis between meteorological information, Twitter activity and satellite-derived hotspots was conducted. The existing approaches that BMKG and other Indonesian agencies use to detect fire activity are reviewed and a novel approach for early fire detection is proposed based on the crowdsourcing of tweets. The policy implications of these results suggest that crowdsourced data can be included in the fire management system in Indonesia to support early fire detection and fire disaster mitigation efforts.

Stories and Sentiment in State and Local Government Finance

Budgets and financial statements convey essential information about revenues, expenditures, assets, and liabilities. But perhaps more important, they also convey positivity, negativity, fairness, uncertainty, and other social sentiments. This essay examines what we know, and what we need to know, about how state and local governments communicate financial sentiment. The main conclusion is that they do convey clear financial sentiments through traditional financial reporting methods and through new channels like social media. Moreover, those sentiments shift predictably in response to broader economic trends and policy priorities, and can shape how investors and other stakeholders view a government's finances. This raises several practical questions about how states and localities can measure financial sentiment, and many normative questions about whether and how they ought to attempt to manage it. The discussion also includes a brief demonstration of how to extract financial sentiment from state and local Twitter activity.

The Rich Get Richer and the Poor Get Poorer: Social Media and the Post-IPO Behavior of Investors in Biotechnology Firms: The Relationship with Twitter Volume

This paper analyzes stock returns for biotechnology firms after initial public offering (IPO) and explores the effect of social media—specifically, Twitter—on these returns. The results indicate positive yet insignificant cumulative average abnormal returns (CAARs) of 1.97% in the first 25 days post-IPO and a decline of tens of percentage points over the following three years. However, after dividing the sample firms into two subsamples according to size, either under or over USD 500 million in market value, the overall results change dramatically. Firms with a market value lower than USD 500 million yield negative CAARs immediately following the IPO; however, this negative CAAR becomes significant only from day 50 onward. Firms with a market value over USD 500 million yield positive CAARs immediately following the IPO, which become significant from day 50, remaining so throughout the following year. These findings can be attributed to the limited duration of investors’ attention, which increases until the end of quiet period and, with small-sized firms, diminishes during the post-IPO years. An examination of Twitter activity and share returns demonstrates a robust correlation between the two, suggesting that investors’ attention to firms may be reflected in their Twitter usage.

Short, Not-So-Sweet, and to (Some) Point

Senators want to differentiate themselves and their agenda, so they have to be strategic about both what to say and when. Senators’ Twitter activity tells us what they value as representatives, but before parsing what senators are talking about, this chapter explains how they are doing it. Senators have to decide how much time and effort they want to put into Twitter, and the chapter dispels the notion that these decisions come down to how old a senator is. The chapter details just how many tweets senators are sending and how senators’ Twitter activity shifts—or not—by demographics like age, gender, party, and seniority. The data reveal that senators’ Twitter activity is not consistently patterned by these common explanations for congressional behavior. The chapter shows that Twitter frequency is variable across different types of senators, suggesting that the important variability is in the messaging rather than just the volume of messages.

Association Between Twitter Mentions and Academic Citations in Otolaryngology Literature

Objective To evaluate the relationship between Twitter mentions and academic citations in otolaryngology literature. Study Design Retrospective cross-sectional analysis. Setting Altmetric Twitter mention and Google Scholar citation rosters. Methods Original research articles from 10 leading otolaryngology journals were assessed 26 months after print publication. Article Twitter mentions were tracked through the Altmetric Bookmarklet, and article citation data were determined through the Google Scholar search engine. Twitter mentions and citation metrics of articles were compared through 2-tailed t test analysis ( P < .05). Results Of all articles, 50.7% (152/300) had at least 1 Twitter mention. Of all article Twitter mentions, 25% (432/1758) happened within the first week of online publication dates, while 64% (1130/1758) occurred between online and print publication dates. Articles mentioned on Twitter had 1.6-fold more Google Scholar citations (8.6 ± 0.7, mean ± SD) than articles with no Twitter mentions (5.4 ± 0.4, P < .01). A total of 8% (24/300) of publications were tweeted by their authors. Articles self-tweeted by authors were associated with an 8.4-citation increase (14.8 ± 3.1) for Google Scholar when compared with articles not shared by their authors on Twitter (6.4 ± 0.4; 2.3-fold increase, P < .01). Conclusion Most otolaryngology articles are disseminated over Twitter, with greatest Twitter activity occurring before print publication date of articles. Citations within 2 years of release are positively associated with the number of mentions on Twitter. Article Twitter mentions may augment the academic influence of otolaryngology publications.

The sleep loss insult of Spring Daylight Savings in the US is observable in Twitter activity

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use “Spring Forward” events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 min later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.