Vaccine hesitancy, as defined by the World Health Organization’s (WHO) Strategic Advisory Group of Experts, refers to the reluctance or refusal to vaccinate despite the availability of vaccinations. While straightforward in definition, vaccine hesitancy encompasses a spectrum of attitudes [1]. This spectrum arises from complex interactions involving health attitudes, decision-making processes, cultural contexts, and health infrastructure. These factors contribute to varying degrees of vaccine confidence, vaccine complacency, and accessibility to vaccination services [2]. Theories borrowed from the health behavior literature underscore the complexity of vaccination intentions. For instance, the Health Belief Model posits that individuals assess the perceived severity of a disease (perceived susceptibility and severity) alongside the perceived benefits and barriers of treatment before making a decision [3]. These theories emphasize that the decision to vaccinate is influenced by a multifaceted interplay of factors. The implications of vaccine hesitancy are significant, as evidenced by the resurgence of diseases such as measles, with studies showing that even a small drop in vaccination rates can lead to substantial increases in disease outbreaks and cost health care systems millions [4,5]. Consequently, the WHO identified vaccine hesitancy as one of the top 10 global health threats in 2019 [6].

Implicit in the definition of vaccine hesitancy is its dynamic nature across a spectrum, indicating that attitudes toward vaccination can evolve over time. Monitoring this phenomenon necessitates a flexible, dynamic approach to complement traditional epidemiological methods. Social media has become increasingly utilized for this purpose due to its widespread adoption and the real-time nature of the data it offers. This enables prompt detection of changes in public sentiment. Furthermore, social media plays an increasingly influential role in shaping public perceptions of vaccination. This influence was particularly pronounced during the COVID-19 pandemic, where misinformation proliferated alongside scientific communication, complicating public health responses [7,8]. The pandemic brought renewed focus on vaccine hesitancy studies, especially with the development and scrutiny of COVID-19 vaccines. This prompted the WHO to increase investment in infodemiology research.

The WHO outlined a comprehensive research agenda for managing infodemics, which represents a situation in which an excess of information, including false or misleading content, spreads in both digital and physical spaces during outbreaks [9,10]. This framework is organized into five 5 streams, each targeting various aspects of infodemic management. The first stream focuses on measuring and monitoring the evolution of infodemics, utilizing various metrics and tools to track information flow. A subtheme within this stream involves triangulating data from multiple sources and standardizing taxonomies and classifications. The second stream aims to detect the origin, evolution, and spread of information across various platforms, with a specific emphasis on the influence of key actors in disseminating this information. The third and fourth streams focus on deploying and evaluating interventions that mitigate the effects of infodemics, thereby enhancing community resilience. The overarching goal of the research agenda, encapsulated in the final stream, is to integrate these tools into broader epidemic management strategies, promoting a paradigm shift in epidemic management that incorporates infodemic control.

In the vaccine hesitancy infodemic space, the majority of studies have primarily concentrated on the first and second streams. A review by Yin [11] provides a summary of the most commonly used big data methods and key research topics in vaccine hesitancy. However, several gaps remain in this area. One significant gap is the need for more comprehensive tracking of vaccine sentiment. While many studies focus on identifying either pro- or anti-vaccination sentiment [11,12], there is a notable lack of attention given to the gray area of vaccine “hesitancy,” which represents a potentially vast spectrum. Another gap is the predominance of studies focusing on English, possibly influenced by its global linguistic dominance as a lingua franca and the availability of tools for English-language analysis. Analyzing diverse contexts and languages enriches the vaccine hesitancy discourse by triangulating global trends with context-specific insights. A third gap lies in the exploration of thematic areas that remain underexplored. Many studies use sentiment analysis and topic modeling as relatively straightforward tools for tracking vaccine sentiment. However, these methods have limitations in identifying how sentiments cluster and the key actors responsible for such clustering. Another frequently neglected thematic area is the temporal aspect of sentiment change. Given that vaccine hesitancy is a dynamic state, tracking these changes over time is crucial for comprehensive understanding. For these reasons, this study focuses on vaccine hesitancy in Taiwan during the COVID-19 pandemic using social network analysis. The choice of methods, the case study of Taiwan, and the emphasis on influencers aim to contribute to the WHO’s infodemiology efforts by proposing new surveillance methods, providing insights from a distinct context for triangulating broader trends, and exploring the understudied area of vaccine hesitancy (influencing). The following sections delve into the literature on echo chambers and influential users in vaccine hesitancy, offering conceptual clarifications, a task aligned with the WHO’s infodemic research agenda.

PTT is a terminal-based bulletin board system (BBS) in Taiwan developed by Yi-Chin Tu (杜奕瑾) and other students from National Taiwan University in 1995 [38]. It serves as a free and open forum where users can discuss a wide range of topics including politics, culture, entertainment, and current affairs. Often referred to as Taiwan’s “Reddit,” PTT is one of the most active forums in Taiwan. From July 2022 to July 2023, the average number of users per day was 56,000 [39]. The user base includes adolescents to young adults, predominantly male, and the forum can host politically charged and radical opinions, similar to Reddit.

The empirical strategy involves evaluating chambering at the network level, identifying opinion leaders based on their network connectivity, and documenting how their behavior influences chambering dynamics over time.

I argue initially that the locality and diversity—the first 2 aspects of influencers—are confined to the forum and its topics. Extracting all vaccine-related discussion boards on PTT focuses the discussion on a case study specific to Taiwan. Next, to analyze polarity, I categorize these boards into 3 sentiments—provaccination, vaccine hesitant, and antivaccination—within vaccine-related discussions. These stances form a conceptual multilayer network, where each layer represents a sentiment toward vaccination. Subsequently, various layer-level analyses are conducted both at the macro and micro levels over time, taking into account the fourth aspect, temporality, for influencers.

All data from PTT are open and publicly available. All data from PTT in its raw form include usernames.These usernames are deidentified and anonymised during the research process to ensure they cannot be traced back to individuals. To do this, a 6 digit alphanumeric temporary username is generated for each unique user. Data is available upon request.

From a total of 48,288 scraped boards, 2992 were selected for analysis after categorization: 1283 were provaccination, 1322 were vaccine hesitant, and 387 were antivaccination (Table 2). Boards classified as neutral and irrelevant (n=2826) were excluded from the analysis. The number of nodes and edges in the network correspond to users and comments, respectively. The discussions on the vaccine hesitant layer are the most active with most nodes (v=14,037) and edges (e=33,520), followed by the provaccine layer (v=11,087, e=23,504). Average degrees mirrored this trend, with the vaccine hesitant layer showing the highest degrees and the antivaccination layer the lowest.

Based on the node overlap results across layers, it appears that a significant portion of nodes engaging in provaccination topics also participated in vaccine hesitant discussions (Figure 2). This pattern persists consistently over time, indicating ongoing engagement in both topics. A notable sharp increase is observed around November 2021, reflected in the steepening slope of the overlapping curve between provaccination and vaccine hesitant discussions. This trend could be attributed to heightened discussions following the Omicron outbreak. The overlap proportion of nodes engaging in antivaccination topics appears lower for both, indicating that these discussions occur on a smaller scale and are more isolated within the overall network.

In network studies, a common analysis involves examining whether networks exhibit power-law distributions, characterized by a connection pattern where a few nodes have many connections and most nodes have few. Figure 3 shows histograms of node degree distributions on a logarithmic scale, visually illustrating a power-law distribution with a heavy tail. The sigma, an exponential parameter of the power-law distribution, was estimated for each layer. Layers that adhere to power-law networks exhibit probability distributions of degree d, p(d), following a power-law relationship p(d) d^δ, where δ≥1 is an exponential parameter (typically with a value around 2). Upon estimating sigma for the power-law parameter, it was found that all 3 layers have sigma values of around 2. For the significance test, where significant P values (ie, P≤.05) indicate a deviation from the power-law value δ≈2, all layers exhibited a power-law network (G^P: δ=2.2, P=.067; G^H: δ=2.3, P=.068; G^A: δ=2.3, P=.09). This suggests that all 3 layers are characterized by a distribution where a few nodes have many connections. A previous study on PTT also identified a similar power-law structure on the platform [42].

Table 3 presents Spearman rank correlations for the entire network. It shows that, on average, the ranking based on indegree is higher than that based on outdegree. This implies that individuals who receive many comments within 1 layer also tend to do so in other layers. Particularly notable is the strong correlation between the provaccination and hesitant layers, indicating a significant overlap in nodes between these 2 layers as a partial explanation for this trend. The hesitant-antipair also shows a comparable trend, with high rankings in indegree suggesting that similar users receive comments across these layers, despite their smaller overlap in node percentage. In terms of commenting activity, overall correlations weakened, indicating that users tend to comment on sentiment boards aligned with their stance, possibly indicating an echo chamber effect. Across all pairwise comparisons of layers, the provaccination and antivaccination layers exhibited the least correlation in indegree and outdegree rankings, suggesting polarization within the network. The low overlap in node percentage further supports this observation.

These trends vary slightly over time (Figure 4). From the initial period to November 2021, we observe a high correlation in users receiving comments across different layers, indicating a small group of key users generating boards of interest. However, in terms of commenting activity, the correlation between provaccination and antivaccination sentiments is highest, suggesting some disruption in the observed echo chamber effect. Simultaneously, the overall percentage of overlapping nodes remains low (considering that Spearman ranks are compared only among similar nodes). As the network expands and the percentage of common nodes increases between the provaccination and hesitant sentiment networks, we also observe a sharp decline in users receiving and giving comments across the provaccination and antivaccination layers, suggesting a temporal polarization effect. This trend becomes particularly noticeable around November 2021.

When comparing across layers, the antivaccination layer exhibits the highest average indegree and outdegree diversity overall (Table 4). To elucidate this trend, it is important to note that while the Spearman rank only considers nodes appearing in multiple layers (indicating engagement across different vaccine sentiments), the layer average encompasses all nodes, whether they participate in multiple layers or only 1. Therefore, while common nodes may suggest some attenuation of chambering in receiving comments and polarization in giving comments, there are also unique nodes that span multiple layers. This trend is most pronounced for the antivaccination layer, indicating that it is the least chambered among the layers based on the activity of its constituent nodes, despite its relatively smaller size.

Temporally, these trends are generally consistent, with a few notable exceptions. Figure 5 tracks the average indegree and outdegree diversity by layer over time. Before November 2021, there is a notable peak in the average indegree for the antivaccination layer. This spike likely corresponds to a post that received significant attention, despite the antivaccination layer being relatively small at that time. Otherwise, the antivaccination layer maintains a higher average indegree consistently until December 2022. Regarding outdegree, all 3 layers show similar values until November 2021, after which the antivaccination layer exhibits a sharp increase followed by a gradual decline. This increase suggests intensified commenting behavior within the layer, potentially indicating a break in the echo chamber effect compared with the other 2 layers.

The findings in the previous section indicate a push-and-pull effect between chambering and nonchambering dynamics. Although nodes common to both layers may exhibit some chambering tendencies, the antivaccination layer as a whole includes more nodes that extend across multiple layers. This section further explores the chambering trend by examining the contributions of 2 user types, high-diversity nodes and hardliners, within the multilayer network.

Comparing indegrees and outdegrees of highly diverse nodes across each layer reveals their polarized engagement, contributing to sentiment echo chambers. The results indicate that nodes with the most diverse participation predominantly receive and give comments on provaccination topics (Table 5). This trend is particularly substantial for indegrees, where most comments are received on the provaccination layer. For outdegree, there is no difference between the provaccination and hesitant layers, indicating that diverse nodes comment similarly on both topics and potentially disrupt the chambering effect. However, considering the larger size of the vaccine hesitant layer, this could imply that diverse nodes receive more comments from the provaccination layer, reinforcing chambering. Compared with the provaccination and hesitant layers, the antivaccination layer shows the least interaction from diverse nodes in terms of both giving and receiving comments. This trend is anticipated due to the smaller size of the antivaccination layer, and the proportion of connections aligns with its relative size compared with the other layers. These findings suggest that the forum as a whole may exhibit a preference for engaging with provaccination messaging, indicating a form of chambering, albeit in a protective manner. Diverse nodes are then spanners through the network.

Temporally, these trends largely hold, with a notable exception in the outdegree for the vaccine hesitant layer (Figure 6). Initially, commenting from diverse nodes was predominantly directed at the vaccine hesitant layer, but by November 2021, there was a notable shift toward engagement with the provaccination layer. This shift may indicate a trend toward forming a “health community” facilitated by network spanners. Further reinforcing this point is that the gap between the provaccination and hesitant layers diminishes from July 2022 until the end of the period under study, indicating another shift in the chambering effect. This breaking of chambering is also evident in the indegree for the antivaccination layer, which shows a slight overall increase starting from November 2021. This suggests that the antivaccination layer is increasingly engaging with diverse nodes.

Hardliners (ie, those who predominantly engage within their own layer) are compared across layers to assess how they drive echo chambering within their respective layers. The results indicate that antivaccination hardliners are more active in commenting on antivaccination boards compared with the other 2 layers (Table 6). This trend remains consistent over time and shows a slight sharp increase in February 2022 during the Omicron outbreak in Taiwan (Figure 7, upper panel). However, this does not imply that the vaccine hesitant layer is entirely inactive within itself. The high indegrees suggest significant commenting within the layer, possibly indicating that a few nodes are responsible for disseminating information received by others. This trend gradually intensifies over time, with spikes observed during early vaccine procurement and the announcement of relaxed antiepidemic measures in late 2022 (Figure 7, bottom panel). Antivaccination hardliners predominantly receive information within their own group, particularly surging during the Omicron outbreak and continuing steadily thereafter. This engagement suggests that hardliners in the vaccine hesitant and antivaccination layers contribute to the entrenched ideologies within their respective groups over time.

This study examined temporal chambering at the macro-network layer level and assessed the role of influential users in this process. Overall, the layers demonstrated chambering in their commenting behaviors, particularly evident in the strong polarization between the provaccination and anti-vaccination stances. Diverse nodes showed significant cross-layer engagement, particularly in the provaccination and vaccine hesitant layers. This contrasts with hardliners, who show more activity within the vaccine hesitant and antivaccination layers. These findings suggest strategies for information management moving forward and contribute to the evolving field of infodemic management.

Despite attempts to definitively assess the existence or absence of echo chambers, it is apparent that multiple chambering and nonchambering processes likely occur. For instance, analysis of similar nodes through Spearman ranks indicates varying degrees of chambering across layers and measurement criteria. The provaccination and antivaccination layers appear to exhibit the highest polarization on both measures, while the provaccination and vaccine hesitant layers show the least polarization. These findings align with previous network studies that have identified echo chambers in social media discussions on vaccines, particularly along the provaccine and antivaccine lines [24,26,29]. Additionally, the trend for receiving comments (indegree) shows less overall polarization compared with the trend in commenting (outdegree), suggesting that users may actively engage by expressing their own sentiment preferences.

Another significant factor contributing to chambering is the activity of specific nodes. Hardliners show significantly higher activity within the antivaccination layer compared with the other 2, indicating a stronger entrenchment effect (chambering). Strategies to mitigate this effect could include flagging or tagging posts from antivaccination hardliners [44]. Flagging represents an effort to balance the protection of an open, democratic forum while countering harmful messaging, akin to Singapore’s Protection from Online Falsehoods and Manipulation Act (POFMA) [45]. Additionally, involving moderators in content management [46,47] aimed at antivaccination hardliners could mitigate their impact on chambering [48]. Highly diverse nodes also allocate fewer comments proportionally to the antivaccination layer, with their engagement primarily centered around the provaccination layer, contributing to their diversity. These provaccination users can serve as valuable resources for disseminating promotional messages, given their demonstrated willingness to engage. Using these multipronged strategies concurrently aligns with common approaches to countering antivaccination movements [49].

Conversely, certain nodes engage in activities that disrupt chambering. The antivaccination layer exhibits the highest average indegree and outdegree compared with the other layers. This indicates that, apart from the nodes common to the Spearman calculations, a significant portion of this layer actively participates in both receiving and giving comments across the entire network. These findings contribute to the broader discourse on “structural hole spanners” within networks [50]. These individuals often act as bridges across diverse topics, engaging with users who hold cross-cutting attitudes toward vaccination [51]. These findings also support earlier research indicating that the vaccine hesitant group serves as a “battleground” where pro- and antivaccination narratives compete [25]. The intermediate Spearman rank values between the hesitant layer and the other 2 layers in both indegree and outdegree reinforce this idea, suggesting that the hesitant layer exhibits closer overlap with both groups.

Temporally, there are several active periods where trends shift. In the initial period from January to June 2021, the presence of a few boards makes each layer more responsive to new threads and comments, resulting in stepped patterns and high fluctuations. Surprisingly, despite Taiwan’s slow vaccine procurement and its first “large” outbreak (approximately 300 daily cases), which would typically attract more attention, the forum remains relatively silent on COVID-19 vaccine issues. Around June 2021, as vaccines become more readily available, several trends become more pronounced. The most significant changes occur in November 2021: cross-cutting users comment more on the provaccination layer, antivaccination hardliners become more insular, and polarization intensifies between the provaccine and antivaccine layers. During this period, various booster policies, the Delta outbreak, and the impending Omicron outbreak likely contributed to increased dialog around vaccines.

There are several limitations to this study and avenues for exploration, particularly in relation to the WHO’s infodemiology research agenda.

The first concern pertains to the data set. Social media platforms often attract specific user demographics and ideological stances. Additionally, the amount of data accessible through application programming interfaces may restrict the availability of scraped data, affecting the representativeness of the general population, a key goal in epidemiological studies. These inherent biases in social media data can internally skew the results. Despite PTT’s loose moderation and nonprofit nature, it is not immune to these limitations. To better contextualize the study, it is crucial to acknowledge its limitations stemming from these factors. To mitigate this bias, several approaches can be considered. First, conducting a comparative study—either on another topic within the same forum or across different platforms—would be beneficial. For instance, conducting similar analyses on DCard, an X-style platform known for attracting a younger demographic, could provide cross-validation of findings across different generations and platform types. This approach can also be extended to more international platforms such as X or Instagram. Another method is to validate the findings by comparing them with sentiment data from epidemiological sources. Conducting such comparative studies can enhance the reliability of these findings, ensuring that observed differences are not merely artifacts of platform or network size variations. These methods align well with the infodemic research agenda, emphasizing the triangulation of diverse data types to better integrate epidemiological and infodemiology insights.

Another limitation is that the definition of “influence” is not exhaustive in this study. The study identifies influential users using indicators such as diversity and hardliners, but these metrics are not comprehensive. The choice of indicators is often constrained by the platform’s structure and available data. For instance, follower count could be a measure of influence on platforms such as X, but PTT lacks such indicators. Despite these limitations specific to PTT, a broader array of metrics for measuring influence remains possible. Indeed, measuring the percentage of positive or negative engagement based on sentiment markers of user posts, as well as their active time spent on the forum, could provide additional dimensions to assess influence. Exploring a spectrum of influence metrics across comparative studies could further enrich our understanding of user dynamics within different platforms.

To better gauge the actual impact of “influential users” on chambering and network structure, conducting network simulations could be highly beneficial. These simulations could help bridge the gap between identifying influential users based on various metrics and understanding how their behaviors contribute to the overall network dynamics and chambering phenomena. Exploring these methods would indeed advance the understanding of chambering dynamics in online discussions about vaccination. Conducting network simulations where influential nodes are systematically removed can reveal how their presence or absence affects the clustering and cohesion of different sentiment layers. Additionally, performing regressions at the user level to analyze factors influencing their engagement across provaccine, vaccine hesitant, and antivaccine stances could provide insights into the drivers of polarization and chambering within the network. These approaches align well with the goals of infodemic management during epidemics, as outlined in the WHO research agenda.

Understanding the underlying reasons behind chambering trends in online discussions about vaccination is crucial for preparing and responding to future infodemics effectively. Identifying whether these trends are triggered by outbreaks, policy changes, or other factors unrelated to outbreaks can provide insights into the temporal dynamics of chambering. This knowledge aids in tracking infodemics and vaccine sentiment on social media platforms. Integrating this narrative with analyses of clustering and network dynamics helps illustrate the cause-effect chains driving chambering and their broader impacts on public discourse and health communication strategies. This comprehensive distillation of pathways represents a critical step in the broader paradigm shift toward integrating epidemic studies with infodemic studies, rather than treating them as separate entities.

The importance of overlapping across the layers is underemphasized in this study and carries implications for construct validity. The current framework assumes that each layer encapsulates all sentiments related to a specific vaccine stance, essentially forming a “discussion sphere” or “space” around that stance. Users can comment in multiple layers because they hold diverse vaccine sentiments, thereby blurring the exclusivity of layers and indicating cross-cutting behavior, which breaks the chambering effect. A clearer illustration of how users comment across layers over time would help distinguish true chambering from typical forum behavior where users engage across multiple boards.

The final major limitation is the absence of content analysis. Although layer- and node-level metrics serve as proxies for cross-layer communication, the specific direction and nature of engagement, such as the content exchanged, are unclear without detailed content analysis. Understanding the content aspect would provide further insights into how engagement occurs between these layers and users. This also partially addresses the issue of overlapping, shifting from engagement across layers as a measure of chambering to engagement with similar vaccine sentiments. Further along the infodemic management agenda, understanding content also informs how interventions should be designed. For instance, engagement with pro- and antivaccination content could lead to either constructive or destructive discussions. Focusing on specific content types, such as misinformation, further illustrates the existence or formation of echo chambers. This approach also aligns with the WHO’s research agenda on infodemics, linking echo chambering to unhealthy behaviors and potentially serving as a focal point for public health interventions.