1 Introduction

Analysis of social media (Facebook, Twitter, Weibo, and so on) data has proven to be an efficient method for evaluating disasters and lowering disaster risk (Ogie et al. 2019). Millions of people turn to social media during both natural hazard and human-induced disasters (Mihunov et al. 2022). Humanitarian organizations, government agencies, and public health authorities are progressively utilizing real-time data from social media to assess disaster risks, save lives, and provide assistance to those in distress (Vieweg 2012). This becomes particularly crucial when traditional information sources such as television or radio are unavailable (Castillo 2016). During disasters, social media can be used to study the extent of the disaster-related damage (Ogie et al. 2019; Mihunov et al. 2022), disseminate information (Ragini et al. 2018; Karami et al. 2020), coordinate the distribution of aid (Fan et al. 2020), examine the mental health of affected individuals (Ahmed and Sinnappan 2013; Kim and Madison 2020; Palen et al. 2020; Willson et al. 2021), raise money for reconstruction (Ahmed and Sinnappan 2013; Takahashi et al. 2015), and more.

Despite the fact that social media offers opportunities for enhanced disaster study, there is a risk that social media attention will be influenced by uneven representation of regions affected by disasters (Robertson and Feick 2016). Previous studies have used the number of social media postings to measure disaster risk directly without accounting for regional and group vulnerability differences (Kelman 2015; Castillo 2016; Ogie et al. 2019). According to the United Nations Office for Disaster Risk Reduction (UNDRR), disasters result from the interplay of hazardous events, exposure conditions, vulnerability, and lack of capacity (Ainuddin et al. 2013). Social vulnerability, lower network popularity among low-income groups, and weaker post-disaster resilience in areas with inadequate infrastructure make it challenging for these regions to gain attention and dissemination on social media (Dargin et al. 2021). An analysis of Twitter usage in 76 counties in Texas and Louisiana during Hurricane Harvey in 2017 in the United States showed that areas with better socioeconomic conditions gain more attention on social media (Dargin et al. 2021).

Under the same impact of disasters, the economically developed areas attract relatively more social media attention, resulting in the relatively insufficient attention of the public and social disaster relief resources to the economically backward areas. This phenomenon reveals the inequality of social media attention in disaster caused by differences in economic development. Inequality of social media attention can lead to inaccurate estimates regarding affected populations and the actual damage that has occurred in the disaster-hit area (Checker 2007; Cutter et al. 2014; Xiao et al. 2015; Dargin et al. 2021). As big data gains prominence, an increasing number of social disaster relief initiatives are influenced by the attention generated on social media (Alam et al. 2020). Furthermore, allocating relief supplies and volunteer support based on unbalanced social media data can lead to disaster victims in vulnerable areas receiving inadequate assistance, and result in a long-term development gap (Fan et al. 2020; Dahal et al. 2021). Low-income areas experience higher economic losses as a percentage of GDP during disasters, but receive less social media attention (Field et al. 2012). Social media attention inequality exacerbates social vulnerability to disasters in susceptible regions.

Disasters, examined through the lens of social production, underscore profound contradictions and inequalities inherent in social and economic structures (Tierney 2020). The evolution of disaster consequences is notably shaped by the messaging and responses orchestrated by governments and relief organizations (Sun and Faas 2018). Prior research has highlighted the role of official media in shaping social media’s focus during disasters, serving as a crucial means for disaster-affected individuals to garner public attention and spontaneous social funding (White and Fu 2012). The significant difference between social media and official media lies in the authority of the information publishers (White and Fu 2012; Christensen and Lægreid 2020). Social media plays a crucial role in disaster response and is primarily utilized by the general public (Tang et al. 2022). While anyone can post comments about disasters on social media, the challenge lies in the limited valuable and credible information contributed (Ogie et al. 2019). In economically developed regions, individuals tend to post numerous comments even for minor disasters due to their widespread Internet access and inclination for posting (Fan et al. 2020). Unfortunately, this surge in comments from affluent areas can overshadow the voices of disaster-hit regions with fewer resources. In contrast, official media sources provide more dependable information and serve a crucial role in reporting on disaster in vulnerable areas (Fang et al. 2019).

Official media platforms vary by country, with centralized countries like China relying on traditional channels such as newspapers, television, government websites, and social media platforms such as Weibo and Twitter for government-driven disaster information dissemination (Shao et al. 2022; Wu and Pan 2022). In federal or less centralized countries with weaker central government authority, emergency agencies and active political figures on the Internet offer credible sources of disaster warnings (Han et al. 2022; Yudarwati et al. 2022). Their timely information release significantly aids in information support, reconstruction, and infrastructure services. After a disaster, publicly released disaster information typically sparks extensive public discussions and sharing, making it more likely to reach a wider audience (Graham et al. 2015). Social media serves as a vital bridge for information exchange between the government and the public in such scenarios (Graham et al. 2015). This can accelerate the dissemination of disaster-related information and improve the efficiency of associated information-dissemination networks (Ma et al. 2020; Rajput et al. 2020; Yudarwati et al. 2022). As a result, more Internet users may pay attention to the hardest-hit areas and may take appropriate responsive actions (White and Fu 2012).

Official media exhibit thematic selectivity when disseminating disaster information, and different thematic contents demonstrate varying abilities in disaster response mobilization (Roitman et al. 2020; Ogie et al. 2022). Whether originating from the government or emergency agencies, official media often provide information support with urgent disaster warnings, which are typically taken seriously by social media users and widely shared. However, the extent to which these messages can effectively enhance social mobilization remains uncertain. Some studies have questioned the transparency of official media (Han et al. 2022). Thus, examining the mechanism through which official media’s information release diminishes social media attention inequality represents a critical research gap. Understanding the effectiveness of different mobilization information themes is integral to this investigation.

Existing studies suggested that the use of social media during disasters may overlook vulnerable groups or regions, but few studies have empirically measured inequalities in social media attention during disasters. Furthermore, the literature has yet to fully explore how official media can be leveraged to address inequalities in social media attention during disasters. To address this gap, our study examined social media attention inequality in rainstorm disasters in China from 2018 to 2021. We investigated whether varying regions received equal attention on social media relative to the extent of the disaster. We focused on the causal relationship between regional development disparities and social media attention inequality. Additionally, we investigated whether official media can guide public attention towards reducing such inequality and examine its influence as a mechanism in conjunction with topic analysis.

By employing panel data and fixed-effects regression models, this study aimed to answer the following research questions (RQ).

  • RQ1: Are there inequalities in social media attention across regions with different levels of development during disasters?

  • RQ2: To what extent can official media reduce online attention inequality?

  • RQ3: What are the mechanisms by which official media reduces inequality in social media attention?

2 Methods

Section 2 outlines the methodology, including steps to measure social media attention inequalities during disasters, data sources, and model specifications for analysis.

2.1 Empirical Strategy

To explore the existence of social media attention inequality and the role of official media during disasters, our research comprised three steps. The first step is to measure inequalities in social media attention during disasters, and we conducted a set of regressions with fixed effects using city-year panel data. Next, we quantified the impact of official media statements on inequalities in social media attention by employing a moderating effect model. Finally, we explored the underlying mechanism by which official statements reduce inequality in social media attention. The methodology and empirical strategy of this research are shown in Fig. 1.

Fig. 1
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Research framework of this study

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2.2 Data Processing

The panel data used in this study covered 333 prefecture-level cities in China for the period of 2018 to 2021, and were obtained from a variety of sources, including rainfall records from meteorological stations, statistical yearbooks, Weibo, and People’s Daily Online (2019–2020). We grouped all these data into panel data by city and month. Table 1 presents a description of the variables and summary statistics.

Table 1 Variable description and summary statistics
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2.2.1 Rainstorm Data

In this study, the definition of a rainstorm disaster was an incident when more than 50 mm of rain fell in a location within six hours, which is the criterion for a yellow rainstorm warning in China.Footnote 1 The blue and orange alert for rainstorms will be used for robustness checks. Previous studies have shown that rainfall data can be used to accurately measure the process and extent of disasters (Samaddar et al. 2014; Cheng et al. 2019). We computed damage as the monthly average of rainfall exceeding the storm warning standard in a given area. Rainfall data for China are available from The National Climatic Data Center.Footnote 2 The distribution of rainstorms in China is shown in Fig. 2, and Fig. 3 illustrates that regions with lower economic development levels, such as the central areas, experience more significant economic losses from rainstorms compared to economically developed eastern coastal regions. Due to inequalities in social media attention, these heavily impacted areas might not receive commensurate focus from social relief efforts, rendering them more susceptible to challenges in post-disaster recovery.

Fig. 2
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Average monthly rainfall in China in 2018

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Fig. 3
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Rainstorm damage in China: Economic losses associated with rainstorm disasters in 2018 (a), 2019 (b), 2020 (c), and 2021 (d)

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According to the China Flood and Drought Disaster Prevention Bulletin (Ministry of Water Resources of the People’s Republic of China 2020), China experiences its heaviest precipitation between May and September, and disasters triggered by rainstorms in October have caused significant losses in recent years.Footnote 3 Therefore, our research focused on May–October.

2.2.2 Weibo Data

Social media attention was measured by analyzing Weibo postings.Footnote 4 Through applying data-crawling algorithms, we obtained 12 million Weibo postings related to rainstorm disasters for the period 2018–2021. Specifically, we first scanned Weibo postings for the keyword “rainstorm” and, for matching Weibo postings, recorded the user names, posting content, posting times, numbers of comments received, numbers of likes received, and geo-tags. We only considered postings with additional comments; postings that reposted the original text directly were not considered. We employed the monthly aggregate of Weibo postings, pertaining to rainstorm-related topics, as the measure of local social media attention in each region.

The three basic methods for identifying locations in postings are using the geo-tags included in the postings, examining the users’ profiles, and analyzing the postings’ content, respectively (Zou et al. 2019). The insufficient presence of geo-tags in less than 1% of postings hampers the complete identification of geographically related social media information during disasters, constraining a more comprehensive understanding of the events (Granell and Ostermann 2016). To increase the dataset and minimize research bias, this study extracted location information from both the text content of user-posted postings and their incorporated geo-tags. For instance, in the case of a posting, “It is said that Nankai Joy City has no thighs. Driving through the waves, hiding. For fear that the car will stall and lie in the water,” we determined the geographic location by identifying the reference to “Nankai.” This allowed us to analyze that the author was mentioning a rainstorm in Nankai District of Tianjin. The distribution of rainstorm-related postings across China’s provinces from 2018 to 2020 is shown in Fig. 4. The map shows that big cities occupy the core position in information-dissemination networks.

Fig. 4
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Distribution of rainstorm-related postings in China

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2.2.3 Official Media Data

Our data on official media came from People’s Daily Online.Footnote 5 This source aggregates information published by authoritative agencies in China’s central and local governments. The information release and dissemination platforms of official media include traditional media such as newspapers and television, and social media such as Weibo and Twitter. We quantified official media attention by calculating the monthly total of news articles related to rainstorms on each region’s section of the People’s Daily Online. We obtained 16,068 news articles regarding rainstorms for the period 2019–2020. Its distribution is shown in Fig. 5.

Fig. 5
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Distribution of rainstorm-related official media news in China

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The situational crisis communication theory (SCCT) was used to explain the official media’s response to the crises (Coombs 2007; Holladay 2010; Li et al. 2022). The existing literature indicates that the official media mainly adopts three types of strategies in crises such as natural hazards and disasters: providing instructing information, providing adjusting information, and bolstering (Liu et al. 2018). Instructing information focuses on reporting circumstances related to crises. This strategy typically includes communication such as disaster warnings, travel alerts, updates of disaster information, and alerts about means of assistance (Coombs 1995; Houston et al. 2015). The adjusting information strategy, meanwhile, is frequently combined with instructing information to provide for the needs of the public during crises and maintain public faith in the region’s ability to recovery from disasters (Kim and Niederdeppe 2013; Olsson 2014; Rajput et al. 2020). Bolstering strategy primarily refers to praising relief efforts and expressing sympathy for the coordinators (Coombs 2007).

We divided official media news into three strategies using unsupervised learning. Applying the SCCT framework, we then identified the corresponding crisis communication strategies for each news. Algorithmically, these procedures were conducted by employing the latent Dirichlet allocation (LDA) topic-modeling method in a deep learning approach. The LDA is a popular method for topic modeling that uses unsupervised machine learning to partition large texts into a predetermined number of topics (Blei et al. 2003).

According to the qualitative analysis, official media prefer to employ the strategies of providing instructing and adjusting information. Among the instructing information released by official media, keywords such as warning, weather station, forecast, rainfall, geological hazard, impact, and disaster were most common. These words reflect how official media inform the public and major decision-making bodies regarding the disaster situation and provide advance disaster warnings. The terms rescue, government, command, and dispatch describe the approaches used in the adjusting information employed in response to the emergencies. Finally, the words central, party, loss, officers, soldiers, and society were used in bolstering to attempt to positively affect the emotional state of the population and boost their spirits (Li et al. 2021). The topic recognition in the original text is shown in Fig. 6.

Fig. 6
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Official media news topic identification

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2.3 Model Specifications

In the baseline model, a panel regression explored disaster intensity and economic impact on social media attention. An interaction term, representing the mathematical product of economic outcomes and disaster intensity, is used to investigate inequality. The significance level of the interaction term highlighted how economic development levels influence social media attention under equivalent disaster severity. Additionally, the impact of official media was assessed through a triple interaction term involving the number of official news, disaster intensity, and economic outcomes. Mechanism analysis further dissected official media responses by categorizing them into three themes. Each theme was analyzed using a separate triple interaction term regression to measure the effectiveness of various government communication strategies in effectively reducing inequalities in social media attention.

2.3.1 Baseline Model

First, we constructed a panel regression model with two-way fixed effects to quantify the impacts of disaster intensity and economic outcomes on social media attention.

$$Weibo_{{it}} = \beta _{0} + \beta_{1} Rain_{{it}} + \beta_{2} PGDP_{{it}} + \beta_{3} Z_{{it}} + \mu_{i} + \gamma_{t} + \varepsilon_{{it}}$$
(1)

where \({Weibo}_{it}\) represents social media attention, measured by the number of “rainstorm”-related postings on Weibo in city i during month t; \({Rain}_{it}\) represents the intensity of the rainstorm in month t in city i, and is the monthly average rainfall at each weather station by the scale of the rainstorm (yellow alert for a rainstorm: ≥ 50 mm within 6 h). Further, \({PGDP}_{it}\) represents the economic outcome per capita in city i for month t, and shows the level of regional economic development. The vector Zit represents time-varying city-level characteristics, such as population, historical rainfall, and finance expenditure.\({\mu }_{i}\) represents the city fixed effect that absorbs unobservable time-invariant variables, and \({\gamma }_{t}\) represents the month fixed effect that absorbs the time-varying variables for all cities. The term \({\varepsilon }_{it}\) represents a random error.

To investigate whether there was inequality in social media attention during rainstorm disasters, we added an interaction term of rainstorm intensity and economic outcome per capita in Eq. 2.

$${Weibo}_{it}={{\beta }_{0}+\beta }_{1}{Rain}_{it}+{\beta }_{2}{PGDP}_{it}+{\beta }_{3}{Rain}_{it}\times {PGDP}_{it}+{{\beta }_{4}Z}_{it}+{\mu }_{i}+{\gamma }_{t}+{\varepsilon }_{it}$$
(2)

where \({Rain}_{it}\times {PGDP}_{it}\) is the cross-sectional term for measuring inequality of social media attention. Specifically, this term evaluates whether the marginal effect of rainstorm intensity on social media attention is affected by local economic development.

In the second stage, we incorporated official media statements into Eq. 2 to explore the impact of official media on social media attention.

$$Weib{{o}_{it}}={{\beta }_{0}}+{{\beta }_{1}}Rai{{n}_{it}}+{{\beta }_{2}}PGD{{P}_{it}}+{{\beta }_{3}}Rai{{n}_{it}}\times PGD{{P}_{it}}+{{\beta }_{4}}Officia{{l}_{it}}+{{\beta }_{5}}{{Z}_{it}}+{{\mu }_{i}}+{{\gamma }_{t}}+{{\varepsilon }_{it}}$$
(3)
$$Weib{{o}_{it}}={{\beta }_{0}}+{{\beta }_{1}}Rai{{n}_{it}}\times PGD{{P}_{it}}+{{\beta }_{2}}Rai{{n}_{it}}\times Officia{{l}_{it}}+{{\beta }_{3}}PGD{{P}_{it}}\times Officia{{l}_{it}} +{{\beta }_{4}}Rai{{n}_{it}}\times PGD{{P}_{it}}\times Officia{{l}_{it}}+{{\beta }_{5}}{{Z}_{it}}+{{\mu }_{i}}+{{\gamma }_{t}}+{{\varepsilon }_{it}}$$
(4)

where \({Official}_{it}\) represents official media attention regarding the rainstorm in city i during month t, which is measured by the number of news reports regarding the rainstorm on People’s Daily Online. The term \({Official}_{it}\) in Eq. 3 measures the impact of official media statements on social media attention. Finally, the interaction in Eq. 4, \({{Rain}_{it}\times PGDP}_{it}\times {Official}_{it}\), is used to explore the moderating effect of official media statements on inequality in social media attention.

2.3.2 Mechanism Analysis

Latent Dirichlet allocation (LDA), a method for natural language processing, was used to divide People’s Daily Online articles into the three response strategies under the SCCT framework—instructing information, adjusting information, and bolstering. Then, we measured the separate effects of the three strategies on reducing social media attention inequality, as shown in Eqs. 5–7.

$$\begin{array}{*{20}c} {Weibo_{{it}} = \beta _{0} + \beta _{1} Rain_{{it}} \times PGDP_{{it}} + \beta _{2} Rain_{{it}} \times Instruct_{{it}} } \\ { + \beta _{3} PGDP_{{it}} \times Instruct_{{it}} + \beta _{4} Rain_{{it}} \times PGDP_{{it}} \times Instruct_{{it}} + \beta _{5} Z_{{it}} + \mu _{i} + \gamma _{t} + \varepsilon _{{it}} } \\ \end{array}$$
(5)
$$\begin{array}{*{20}c} {Weibo_{{it}} = \beta _{0} + \beta _{1} Rain_{{it}} \times PGDP_{{it}} + \beta _{2} Rain_{{it}} \times Adjust_{{it}} } \\ { + \beta _{3} PGDP_{{it}} \times Adjust_{{it}} + \beta _{4} Rain_{{it}} \times PGDP_{{it}} \times Adjust_{{it}} + \beta _{5} Z_{{it}} + \mu _{i} + \gamma _{t} + \varepsilon _{{it}} } \\ \end{array}$$
(6)
$$\begin{array}{*{20}c} {Weibo_{{it}} = \beta _{0} + \beta _{1} Rain_{{it}} \times PGDP_{{it}} + \beta _{2} Rain_{{it}} \times Bolster_{{it}} } \\ { + \beta _{3} PGDP_{{it}} \times Bolster_{{it}} + \beta _{4} Rain_{{it}} \times PGDP_{{it}} \times Bolster_{{it}} + \beta _{5} Z_{{it}} + \mu _{i} + \gamma _{t} + \varepsilon _{{it}} } \\ \end{array}$$
(7)

where \({Instruct}_{it}\), \({Adjust}_{it}\), and \({Bolster}_{it}\) represent instructing information, adjusting information, and bolstering strategies, respectively, which are the responses adopted by the official media during rainstorms in month t in city i. The cubic polynomial is used to measure the effect of response strategy on reducing inequality in social media attention.

3 Results

Through the regression of baseline models, robustness checks, and mechanism tests, we examined the inequalities in social media attention during rainstorm disasters. Our findings indicate that regions with lower economic outcomes per capita received less social media attention for disasters of the same magnitude compared to other regions. Additionally, official media can reduce this inequality by encouraging netizens to focus on overlooked, underdeveloped areas through timely, accurate, and open disclosures of disaster occurrences.

3.1 Social Media Attention Inequality

Table 2 summarizes the baseline results for the measurement of social media attention inequality. The first two columns present the pooled ordinary least squares (OLS) regression results through employing Eq. 1, which show that rainfall and per capita economic outcome are significantly correlated with social media attention in China. The results presented in columns (1) and (2) confirm that social media attention increases with rainfall and level of economic development.

Table 2 Baseline results for social media attention inequality measure
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Column (3) shows the results of employing the panel-regression results with two-way fixed effects and adding the interaction between rainfall and economic outcome per capita to measure attention inequality. This was conducted using Eq. 2. The result of the interaction was positive and statistically significant at the 1% level and, thus, confirm that social media coverage of rainstorm disasters is unequal across regions. For regions that experienced the same level of rainstorm-related damages, those with higher economic development levels were mentioned more frequently on social media.

Existing studies show that social media attention is positively correlated with the rarity of disasters (Spence et al. 2011; Bignami et al. 2018). Social media attention to areas that receive frequent rainstorm warnings was relatively low. Given the observed heterogeneity in the impact of rainfall warnings on social media attention across Chinese regions, historical rainfall was incorporated as a control variable in Eq. 2. The regression results, controlling for historical rainfall alongside other variables, are detailed in columns (4) and (5) of Table 2. These results, largely consistent with the baseline findings shown in columns (2) and (3), confirm the robustness of our analysis. In subsequent analyses, historical rainfall is included within the aggregate of control variables, rather than being individually itemized. This integration helps streamline the presentation and enables a focused examination of additional factors that influence the results.

3.2 Effect of Official Media Statements on Social Media Attention

The baseline results in Table 2 demonstrate that there are inequalities across regions regarding the level of social media attention they receive during rainstorm disasters. Next, we added the number of official media news articles and their interactions into Eq. 2, as shown in Eqs. 3 and 4, to explore the role of official statements. The estimation results are listed in Table 3.

Table 3 Baseline results for the effect of official media voices
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Column (1) in Table 3 shows a significant positive relationship between the logarithm of the number of official media news articles and social media attention. Thus, as an important node of information-dissemination networks during disasters, official media statements can direct social media attention to rainstorm-affected areas.

Column (2) shows the results of applying Eq. 4. Through cubic interaction, we explored the role of official media statements in addressing inequality in social media attention. The elasticity of official media statements with respect to social media attention inequality was approximately -1.8% at the 1% significance level.

3.3 Robustness Checks

Section 3.1 confirmed the presence of social media attention inequalities in China during rainstorm disasters, and Sect. 3.2 revealed that official media statements can relief such inequalities. However, whether the findings are affected by biases in the sample selection or measurement approach must be addressed. The robustness checks in this study were based on two strategies.

3.3.1 Changing the Rainstorm Warning Signal

In the baseline regressions, we used yellow alerts for rainstorms (≥50 mm within six hours) as the statistical criterion for storm intensity. As our first strategy for a robustness check, we replaced the yellow alerts with blue alerts (≥50 mm within 12 hours) and orange alerts (≥50 mm within three hours),Footnote 6 and re-ran the regressions by employing Eqs. 2–4. The regression results suggest that our findings are valid (Table 4).

Table 4 Robustness check: Other rainstorm warning signals
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3.3.2 Using Average Monthly Rainfall as the Independent Variable

In the second test strategy, we discarded the statistical criteria for rainstorm alerts, and directly used cities’ average monthly rainfall as the measure of rainstorm intensity. As some cities rarely experience rainstorms, consideration solely of average monthly rainfall statistics should increase the rainfall-related discrepancies among cities, making this approach essential for checking the robustness of the empirical findings. Table 5 presents the regression results, and shows that there were no significant differences from the baseline results in Tables 2 and 3.

Table 5 Robustness check: average monthly rainfall
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3.4 Underlying Mechanism

This subsection focuses on identifying the official media response topics that effectively guide public opinion and efficiently reduce inequality in social media attention.

3.4.1 Effectiveness of Different Topics in the Official Media Response

We examined the impact of three crisis response topics on reducing inequality in social media attention in order to explore the underlying mechanisms by which official media influences this inequality. Columns (1), (3), and (5) in Table 6 show that all three response strategies considerably improved social media attention to the storm-affected areas. This suggests that people value information from official and government-supported sources (Boas et al. 2020).

Table 6 Regression results for effectiveness of different topics in the official media response
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Next, we added an interaction to measure the impact of the three response strategies on reducing social media attention inequality, as shown in Eqs. 5–7. This interaction was a three-time cross term between the number of news items generated by each strategy and Rain × PGDP. The regression results are presented in columns (2), (4), and (6) of Table 6.

The coefficient of the cubic interaction in column (2) was significantly negative, suggesting that official media can effectively reduce inequality in social media attention through providing instructing information. The regression results for adjusting information are reported in column (4). The coefficient of the cubic interaction was negative, but not significant. The results indicate that the government’s response to emergency rescue and disaster relief issues can reduce inequality in social media attention. However, the effect of adjusting information is not as strong as that of instructing information. The results presented in column (6) concern the performance of the bolstering strategy used by official media. The regression coefficient of cubic interaction is positive but insignificant. It is clear from these results that bolstering strategy is ineffective for addressing social media’s inequitable distribution of attention.

3.4.2 Heterogeneity Test

Geo-tags on social media are predominantly provided by users in large cities (Morstatter et al. 2013). In contrast, vulnerable groups are less likely to share their experiences online following disasters (Samuels and Taylor 2019). As a result, vulnerable groups often rely on users from other cities to bring attention to their situation. In the heterogeneity test, we used geo-tags to represent the voice of local users, and used postings featuring addresses (without geo-tags) to measure the attention of other regions to the disaster-hit areas.

Columns (1), (3), and (5) in Table 7 report, for regions obtained from geo-tags, the regression results for the three strategies adopted by official media to reduce social media attention inequality. Columns (2), (4), and (6) report the regression results for regions obtained from postings content. Overall, the results show that the adoption of instructing and adjusting information by official media can significantly help users from other regions monitor the status of the affected regions, and that the increase in postings mentioning the affected regions can effectively reduce social media attention inequality. This effect is stronger than when local users actively post their concerns after official media have spoken.

Table 7 Heterogeneity test: Effectiveness of different topics in the official media response for postings with local geo-tags and mentioned in content during disaster
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4 Discussion

This study aimed to empirically examine social media attention inequality during disasters and the potential role of official media in reducing inequality. Our findings confirmed uneven attention allocation on social media during disasters. Regions of similar devastation received varying degrees of attention. Additionally, our study highlighted the positive impact of official media in reducing inequalities of social media attention and provided communication strategies for governments to address inequalities during disasters more effectively. The results emphasized the role of official forces in disaster reduction and further enrich the empirical research on the social production of a disaster.

Our research showed that regional development gaps can lead to unequal social media attention in disasters. We also confirmed the baseline results with a panel regression model over a long time window (Xiao et al. 2015; Robertson and Feick 2016). Most of the existing literature has analyzed social media attention inequality for individual disasters (Takahashi et al. 2015; Cheng et al. 2019; Mihunov et al. 2022). However, it is difficult to explore whether social media attention inequality persists after local infrastructure and awareness of disaster relief have improved (Zou et al. 2019; Dargin et al. 2021). To address this gap, long-term rainfall data were used as a proxy variable for the severity of rainstorm in our study, and we focused on the inequality of social media attention to disaster-hit areas over a 4-year period.

Using official media data from 2019 to 2020, our study also found that official media not only played an important role in the information transmission network (Madianou 2015; Dargin et al. 2021) but also alleviated the attention inequality of social media. Typically, the public authorities release limited information following the disaster announcement. In the context of the social production of disasters, the involvement of the political system plays a crucial role in shaping the development of disasters. A lack of information hinders nongovernmental rescue forces from effectively targeting vulnerable areas. Instead, individuals have to rely on social media opinion leaders for updates on the disaster situation, which provides excellent opportunities for the spread of rumors and panic (Zeemering 2021). To improve efficiency and curb rumors, official media should increase transparency and enhance direct communication with netizens (Ma et al. 2020). As an externality, the voice of public authorities can help reduce inequality in disaster response efforts.

The definition of official media varies depending on a country’s system. For example, in China, a centralized state, the government enjoys a high level of trust among the people (Zhong et al. 2021). In federal or less centralized countries, official media functions may be assumed by emergency agencies or politicians through online platforms (Ogie et al. 2022). When they release information, they can promptly steer public opinion on social media toward paying attention to disaster-affected areas (Carley et al. 2016). Their social media accounts have become crucial channels for disaster-affected individuals, especially those from marginalized groups, to seek public attention and assistance (Han et al. 2022).

Our research also explored the sources of postings focused on disaster-affected areas using geographic information from multiple sources (Kent and Capello Jr 2013). The results show that after the official announcement, postings related to affected areas primarily originated from locations other than the affected areas. This highlights the critical role of official media in enhancing online visibility of vulnerable regions where communication is disrupted during disasters (Morstatter et al. 2013; Samuels and Taylor 2019). These findings reinforced the importance of official media in advancing social justice.

Our research has significant academic and policy implications. In the framework of the social production of disasters, societal inequality, vulnerability, and injustice contribute to the heightened frequency and impact of disasters. The escalating role of social media in disaster relief underscores the need to address its potential for inequality. For academic researchers on disaster risk, neglecting social media attention inequality can undermine the validity of research results and perpetuate long-term inequalities in vulnerable areas (Madianou 2015). For policymakers during disasters, official media should be regarded as a crucial tool to address social media attention inequality. Simultaneously, the approach of information intervention by official media constitutes a significant aspect of the research. Emphasizing the clarification and fulfillment of needs over promoting messages is more conducive to effective disaster mobilization.

5 Conclusion

Social media data are a valuable resource for disaster risk research and relief efforts at the societal level. By analyzing China’s monthly panel data from 2018 to 2021, we found that social media attention during rainstorm disasters is unequal, and economically underdeveloped areas are often ignored, leading to potential long-term social inequality. Further analysis of the 2019–2020 official media data showed that official media played a crucial role in reducing the inequality of social media attention during disasters. Disaster information released by official media has high influence on social media platforms and will be widely disseminated and discussed.

Specifically, our study found that the government’s instructing information strategy is the most effective in reducing social media attention inequality during disasters. The timely disclosure of disaster situations through government news is critical for attracting public attention to vulnerable areas and reducing social media attention inequality. Overall, our study highlighted the potential role of social and official media in the aggravation of disasters and their consequences and provided insights into strategies for reducing social media attention inequality during disasters.

It should be noted that this study has three main limitations. First, we used rainfall data instead of disaster loss data because of the limited amount of data available. Second, the findings did not take into account the transparency challenges faced by official media, and the applicability of the conclusions in countries with different political systems has yet to be verified. Third, acknowledging the constraints imposed by the paucity of long-term data sourced from official media outlets, our study was unable to thoroughly quantify improvements in mitigation and relief efforts across extended periods following disaster events.