How Policy Announcements Shape Online Political Discourse: A Quasi-Experimental Analysis of Technical Language and Engagement on Reddit

Abstract

This study examines the effects of policy announcements on the sophistication and engagement levels of online political discourse. While existing research has underpinned the idea that major events attract significant attention online, there is a lack of literature examining how the specific type of event affects both the quality of discourse and the technical language users employ, particularly in trade contexts. This paper seeks to fill in this gap by implementing a quasi-experimental pre-post comparison design that collected 3,545 Reddit posts from 21 politically and economically diverse subreddits spanning five major tariff announcements in 2025. Altogether, this study found that there were no overall significant changes in technical language and engagement before and after the events. However, event-specific analysis proved to be significant, with an increase in technical language associated with Trump’s Tariff Threats. Notably, while the volume of Reddit posts roughly doubled following the announcements, engagement per post simultaneously significantly decreased, suggesting that announcements may facilitate democratizing participation but also fragment attention across a greater number of posts. These patterns provide a more nuanced depiction of how policy announcements actually shape online discourse, beyond the prevalent notion that all focusing events uniformly increase attention and engagement across communities.

Keywords: tariff, Reddit, trade policy, online discourse, political communication

Introduction

Social media has become a fundamental aspect of modern day politics, helping mold our understanding and opinions of political developments. Many platforms such as Reddit allow users to consume information while simultaneously reacting to and debating with other users, influencing the lens that issues become framed through. This new form of political communication makes examining the discourse patterns behind online political discussions more important than ever to allow us to understand how information disseminates online: what types of political activity attract certain types of users and what types of discourse gather the highest levels of engagement. Research currently often assumes that all policy announcements create uniform surges in attention online. However, this assumption is often inaccurate, given that online discussions vary greatly in engagement and sophistication. While some political announcements create technical debates, others only amount to surface-level discussions. Such discrepancies indicate that the course of public reaction online may actually depend on the specific nature of the event, rather than solely on the fact that an event occurred.

Therefore, this study addresses existing literature’s limitations through comparing Reddit discussions before and after major tariff announcements in the U.S. Dialogue prior to a major announcement may reflect hesitancy or anticipation, whereas dialogue following an announcement might consist of more well-informed assessments. Despite this difference, most existing studies only examine overall shifts in discourse, without distinguishing between pre- and post- announcement discourse. Aside from timing of discourse in relation to the event, the actual content of the announcement may also shift discussion patterns. Escalatory announcements (ones that increase pressure and conflict) may generate more intense discussions, whereas de-escalatory announcements (ones that reduce pressure and conflict) could lead to less engagement where users feel less urgency to analyze new policies.

Oftentimes, the sophistication and engagement levels of online discourse are formed by a combination of the type of announcement as well as the timing of the discourse, rather than there being uniform effects. As a result, this study provides a more nuanced account of users’ discourse online, placing them in their proper temporal and policy context to answer the question: how do U.S. tariff announcements affect the technical sophistication and engagement of political discourse on Reddit?

Literature Review

Focusing events are defined as sudden or rare events that may present highly visible harm to policymakers and the public¹. Building on this concept, long periods of policy stability tend to be punctuated by such events that generate new policy dynamics, especially given that changes in policy occur most frequently when focusing events converge with problem recognition and political opportunities^2,3. The agenda-setting theory bolsters this dynamic, as it explains that the media amplifies focusing events to reshape how the issues are prioritized by the public⁴. General reactions to focusing events are also often layered. Attention may span from simply supporting a general cause to advocating for detailed policy proposals⁵. Nonetheless, attention does not remain concentrated forever. Although interest spikes immediately after, it slowly declines as novelty wears off⁶. Furthermore, as media coverage increases information supply, attention also increasingly fragments⁷. Yet, existing research largely only focuses on analyzing patterns in aggregate shifts in attention, without accounting for the quality or depth of that discourse. This then leaves open the question of how policy announcements shape what people say and how they engage with content.

Social media has become increasingly embedded into political communication, functioning alongside traditional media to shape how the public processes information⁸. A meta-analysis of 36 studies revealed that social media platforms positively correlated with increased civic participation⁹. Additionally, online platforms have also been central to allowing for more political interactions. For instance, in the 2016 U.S. presidential election, r/The_Donald allowed populists to convene and collectively challenge mainstream media narratives¹⁰. Interestingly, the echo chamber likely did not hold during that election, as there were large amounts of interaction between political groups, sometimes more than amongst themselves. As an example, Clinton supporters communicated to Trump supporters more often than they did internally¹¹. When examining conservative discussions on Reddit and Truth Social, research has also revealed that both platforms included similar activity levels, but Reddit conversations were more centered around policy debate, whereas Truth Social’s conversations centered around airing grievances, suggesting that Reddit is generally more well-suited for policy-focused discussions¹². However, the studies referenced above only examine elections and social media sites from a broad perspective, without looking at the impacts of specific policy announcements or policy announcement types on discussions.

Trump’s social media posts have also had extensive effects beyond communication. Uncertainty surrounding his online announcements has correlated with less investment and economic output due to heightened uncertainty regarding potential new policies¹³. Sudden changes in the stock market have also been a symptom of Trump’s posts related to trade or foreign policy¹⁴. His use of X has been most notable, where one of his posts in 2018 about a new aluminum and steel tariff caused a spike in online trade discussion, although that activity subsided within around ten days, reflecting social media’s quick-paced attention span¹⁵. Trade policy research has found that views of the general economy shape people’s opinions about trade, which may explain why some announcements lead to higher levels of engagement compared to others¹⁶. Nevertheless, all of these studies only recognize that Trump’s trade communication affects markets and online activity, without understanding how the content of discussions shift.

In terms of the linguistics of social media content, current literature indicates that over time, social media posts have decreased in length, vocabulary, and complexity¹⁷. Processing fluency research demonstrates how simpler language is much easier for people to digest, which results in them responding more favorably to less complex content¹⁸. Therefore, content algorithms designed to drive engagement end up promoting content with simpler language, which in turn has potentially also reduced linguistic diversity online. In addition, the easier the text is to process, the more likely people perceive it as accurate, even when in reality it is not¹⁹. One study found that companies that used less jargon were associated with being more trustworthy, as easy-to-process text made the companies feel more approachable²⁰. In contrast, companies employing higher levels of jargon were perceived as less moral and even correlated with committing more ethics infractions²¹. Yet, existing linguistic and engagement literature has consistently failed to examine whether the technical sophistication of political content increases or decreases surrounding policy events, nor has research considered how that variation affects engagement. A study analyzing over 60 million Reddit comments reported that as big events approach, discussion becomes increasingly repetitive, which is a sign that aspects beyond the mere occurrence of an event shape discourse characteristics²². Looking towards engagement metrics in political Reddit posts, researchers also discovered that posts that were fact-checked, and especially those validated as true, received greater engagement²³. Together, these findings suggest that users respond differently depending on various factors of policy announcements or the specific online content. However, this research examines broad political discourse, without distinguishing between regularly occurring political discourse and political discourse generated as a result of specific policy announcements.

Methods

This study’s data was collected on Reddit, as it allows for users to not only comment, but also debate amongst each other and amplify others’ content through upvotes. With 52 million users daily and over 138,000 active subreddits, it is also one of the most prominent social media websites²⁴. Furthermore, Reddit can feature much longer posts than on platforms such as X, encouraging more in-depth, technical discussions, and it is much more accessible to the general public than academic forums. These characteristics make Reddit an ideal platform to analyze how users’ online discourse is shaped by policy announcements, both from an engagement and sophistication level.

To collect data, Reddit’s PRAW API scraped 21 subreddits that featured a steady stream of activity. The chosen subreddits also consisted of varying ideologies (ex. r/Conservative, r/democrats, r/politics) and varying levels of economic expertise (r/AskEconomics, r/Economics, r/stocks). A complete list of subreddits is provided in Appendix A.

The event windows this study examined consisted of five major U.S. tariff announcements in 2025 ranging from March 4, 2025 to August 7, 2025. The specific events included Fentanyl Tariffs (March 4, 2025), Liberation Day (April 2, 2025), Reduced Tariffs (May 12, 2025), Tariff Threats (July 12, 2025), and Liberation Day Reactivated (August 7, 2025). Reddit posts were retrieved in 10-day windows before and after each event to balance capturing both immediate and slightly delayed online responses a shorter window would not be able to include, while avoiding overlap with unrelated events that could be captured in a longer window. The sample was collected by compiling all posts containing trade-related keywords (n=17) including the following: tariff, trade war, and section 232. The full list of keywords is recorded in Appendix B. The data was sorted by recency and filtered into corresponding event windows, and to avoid overrepresentation, posts were limited to 50 per keyword in each subreddit. Each post was then labeled as pre-event or post-event for data analysis. In total, 3,545 Reddit posts were collected. Because there tended to be fewer anticipatory posts prior to an announcement, compared to more reactionary content after an announcement, 1,155 of the retrieved posts occurred before the announcement and 2,390 occurred subsequent to the announcement, as shown in Table 1 and Table 2.

Event name	Pre-event posts	Post-event posts	Total
Liberation Day	191	1,186	1377
Reduced Chinese Tariffs	439	356	795
Fentanyl Tariffs	137	523	660
Liberation Day Reactivation	241	158	399
Trump’s Tariff Threats	147	167	314
Total	1,155	2,390	3,545

Subreddit	Pre-event posts	Post-event posts	Total
r/AskConservatives	32	78	110
r/AskEconomics	47	245	292
r/Askpolitics	18	69	87
r/Congress	0	6	6
r/Conservative	41	106	147
r/democrats	27	44	71
r/economiccollapse	53	62	115
r/Economics	71	179	250
r/economy	68	175	243
r/ImportTariffs	27	52	79
r/Liberal	13	29	42
r/moderatepolitics	36	57	93
r/PoliticalDiscussion	22	56	78
r/politics	110	183	293
r/Republican	11	44	55
r/republicans	11	76	87
r/StockMarket	187	285	472
r/stocks	163	266	429
r/Tariffs	52	93	145
r/Trump	49	113	162
r/wallstreetbets	117	172	289
Total	1,155	2,390	3,545

This study utilized a quasi-experimental pre-post comparison method to provide stronger analysis compared to current correlational studies, as announcement timing is independent of Reddit’s discussion patterns, since policymakers do not plan announcements around them. Thus, this paper’s findings can more strongly identify associations between policy events and changes in the discourse following each event.

The independent variable was the timing of a Reddit post relative to a policy announcement (pre-event vs. post-event). The dependent variables investigated included technical language use and post engagement. Technical language was measured by counting the number of domain-specific terms within each post to determine whether users’ vocabulary required policy knowledge. Specialized terms were identified by creating a dictionary (n=77) sourced from the World Trade Organization’s glossary²⁵. The complete list is provided in Appendix C. Terms were related to trade policy (e.g., ad valorem tariff and anti-dumping duties), trade governance (e.g., General Agreement on Tariffs and Trade and Dispute Settlement Body), and trade concepts (e.g., tariff binding and market distortion). Engagement was quantified using Reddit’s own scoring system, upvotes minus downvotes, to reflect both the visibility and community approval of a post. Finally, each announcement was also labeled as escalatory (events that raise conflict or pressure, e.g., new tariffs and stricter trade restrictions) or de-escalatory (events that reduce conflict or pressure, e.g., lowered tariffs and eased trade restrictions) to provide additional descriptive context.

A quasi-experimental pre-post comparison design first estimated the aggregate relationship between policy announcements and technical language and engagement of Reddit posts. A t-test across all events compared each outcome with the unadjusted pre- and post-event means and then again within each event. Because six hypotheses were tested on the same dataset, a Bonferroni correction controlled for the family-wise error rate, yielding a new significance threshold of α=.008. The following OLS regression model was then estimated for each dependent variable: Outcome = β₀ + β₁(time_trend) + β₂(post_event) + β₃(post_event_trend) + β₄(controls) + ε. Both models included subreddit fixed effects to control for variation across subreddits and posting date, and posting time to control for temporal variation. The technical language model also controlled for post length, and the engagement model also controlled for the amount of technical language. All reported coefficients are unstandardized.

Results

Comparing the unadjusted pre-event and post-event means of technical terms across all events using a t-test revealed insignificant overall changes (3.7 pre vs. 3.27 post, p=.051). Yet, results differed under individual event examination. After conducting event-specific t-tests and applying a Bonferroni correction (α=.008), Trump’s Tariff Threats significantly correlated with an increase in technical language (3.78 pre vs. 6.50 post, p = .006), as displayed in Table 3. Initially, Reduced Chinese Tariffs (p=.032) and Liberation Day Reactivation (p=.028) were also significant, but neither event remained significant after applying the Bonferroni correction. These pieces of data demonstrate how, while aggregate event analysis was insignificant, the individual event, Trump’s Tariff Threats, demonstrated a significant shift.

Event	Event Type	Pre-Event Mean	Post-Event Mean	Difference	p-value
Fentanyl Tariffs	Escalatory	3.31	3.01	-0.30	.583
Liberation Day	Escalatory	2.71	3.02	0.31	.477
Reduced Chinese Tariffs	De-escalatory	4.44	3.39	-1.05	.032
Trump’s Tariff Threats	Escalatory	3.78	6.50	2.72	.006
Liberation Day Reactivation	De-escalatory	3.31	2.28	-1.03	.028

Next, regression models were estimated for technical language and engagement to examine whether or not policy announcements significantly correlated with the dependent variables after accounting for temporal trends and controls. The full results are displayed in Table 4 and Table 5, respectively.

			95% CI
Variable	β	SE	Lower	Upper
Constant	-0.13	0.24	-0.59	0.34
Pre-Event Time Trend	-0.10*	0.04	-0.17	-0.02
Post-Event Indicator	0.53†	0.27	-0.01	1.07
Post-Event Time Trend	0.11*	0.05	0.01	0.20
Text Length	0.01***	0.00	0.01	0.01
Adjusted R²	.61

While the unadjusted pre-post comparison demonstrated a minor decrease (though insignificant) in technical language (3.7 vs. 3.27, p=.051), the regression analysis that controlled for the subreddit, post length, and temporal trends exhibited an increase in technical language (β=0.53). This finding suggests that after controlling for confounding variables, policy announcements actually correlated with greater technical language use. As listed in Table 4, technical language significantly declined over time prior to the event (β=-0.10, p=.015). However, after the event, technical terms increased per post, which approached significance (β=0.53, p=.053), suggesting a potential immediate rise in technical language use. The post-event time trend also revealed a significant increase in technical language use after the event (β=0.11, p=.026). Lastly, text length proved to be a significant positive predictor (β=0.01, p < .001).

			95% CI
Variable	β	SE	Lower	Upper
Constant	835.88***	128.29	584.36	1087.41
Pre-Event Time Trend	59.91**	21.31	18.13	101.68
Post-Event Indicator	-409.91**	148.11	-700.30	-119.52
Post-Event Time Trend	-36.38	25.62	-86.62	13.85
Number of Technical Terms	0.95	5.73	-10.29	12.19
Adjusted R²	.002

Although there was limited overall explanatory power (adjusted R²=.002), the model presented in Table 5 showed a positive and significant increase in engagement prior to each event (β=59.91, p=.005) as well as a significant decrease in engagement directly after each event (β=-409.91, p=.006).

By visually highlighting findings from two separate events, Figure 2 illustrates how shifts in technical language use differed by the event. While Trump’s Tariff Threats was associated with an increase of 2.72 technical terms per post, Liberation Day Reactivated was associated with a decrease of 1.03 technical terms per post. Together, these pieces of data reinforce the finding that changes in technical language are not uniform across all policy announcements and require event-specific analysis.

Discussion

This research makes several important contributions to political communications literature. Methodologically, by applying a quasi-experimental pre-post comparison design to online political discourse, it helps create a more structured approach to research about policy-related discussions. It also treats announcement timing as exogenous to Reddit patterns and can therefore provide more credible evidence than existing studies that are purely correlational.

Theoretically, this study challenges the assumption from the focusing events theory that events create uniform patterns in discourse. Despite the aggregate analysis of technical language use being insignificant, event-level analysis for Trump’s Tariff Threats became significant. This discrepancy provides preliminary suggestive evidence that certain escalatory events may correlate with increased technical language use, although this pattern did not hold consistently across other escalatory events in the sample. Notably, Liberation Day, the largest escalatory event within the study, did not yield any significant results. A possible explanation may be that its large scale and media saturation drew in a greater number of casual participants to Reddit, which diluted the amount of technical content compared to smaller events. Altogether, this paper suggests that both the type and scale of a policy announcement may matter just as much as the occurrence of the announcement.

Empirically, the findings in this paper suggest that online policy discourse complexity may respond in event-specific ways rather than uniformly. The significant event-specific finding compared to the insignificant aggregate finding discourages making broad generalizations about relationships between policy announcements and public discourse. Rather, it highlights a greater need for research across various policy domains.

Pertaining to democratic theory, this experiment’s findings also suggest that online discourse may be more responsive to institutional actions than previously assumed, although in uneven ways. Because only certain announcements were associated with an increase in discourse complexity, it raises an important question of which features of an announcement matter in contributing to shifts in communication patterns. Announcements also seem to increase audience participation. Reddit posts nearly doubled post-announcement, increasing from 1,155 posts to 2,390 and were potentially associated with greater technical language usage (β=0.53), suggesting a wider, more curious audience base. Yet, engagement per post also dropped sharply post-announcement (β=-409.91). These pieces of data collectively point to the idea that while announcements may broaden overall participation in discussions, it may come at the cost of fragmenting attention through diluting the concentration of engagement. That said, it is important to note that given the low adjusted R² of the engagement model (.002), these patterns should only be considered conceptual observations requiring further validation, instead of an empirical finding.

This study was subject to several limitations. Reddit has its own specific demographics and communication norms, so findings may not generalize elsewhere across social media or to in-person policy discussions. The collected data also only surrounds five tariff-related events in a relatively short span of time, reducing its generalizability to other policy contexts. Moreover, the measure of technical sophistication relied solely on the number of technical terms used, which is only one aspect of technical sophistication. Accordingly, future work could incorporate other dimensions such as evidence citation and argument complexity. Similarly, by only operationalizing engagement as upvotes minus downvotes, this study does not account for other features such as number of comments or depth of discussions, which could be included in future research to more accurately reflect online communication. Finally, the engagement model’s low adjusted R² (.002) means its findings can only explain little of variance, so adding other variables such as subreddit size and content sentiment may be able to create a more complete model that accounts for greater variation.

Future research could expand on this study through investigating other social media platforms and studying events under longer timeframes to paint a broader picture of discourse trends. Studies could also analyze specific components of policy announcements to better understand which features actually create changes in discourse and which features do not. Moreover, doing so would help refine the theoretical framework developed in this study. More comprehensive measures of discourse sophistication and engagement would also further strengthen understanding of the relationship between policy announcements and online discussions.

As governments increasingly rely on online platforms to convey major policies, better understanding how their announcements shape discourse is no longer just an academic question, but one of democratic importance. The way that online discussions ensue may depend on what, how, and when policymakers communicate to the public.

Appendix

Appendix A. Subreddit List

r/AskConservatives, r/AskEconomics, r/Askpolitics, r/Conservative, r/Congress, r/democrats, r/economiccollapse, r/economy, r/Economics, r/ImportTariffs, r/Liberal, r/moderatepolitics, r/PoliticalDiscussion, r/politics, r/Republican, r/republicans, r/stocks, r/StockMarket, r/Tariffs, r/Trump, r/wallstreetbets.

Appendix B. Retrieval Keywords

Aluminum tariffs, China trade, dispute, do tariffs work, effects of tariffs, new tariffs, retaliatory tariffs, section 232, section 301, steel tariffs, tariff, tariff backlash, tariff retaliation, trade tariff list, trade tensions, trade war, US-China trade war.

Appendix C. Technical Terms Dictionary

Ad valorem equivalent (AVE), ad valorem tariff, anti-dumping duties, Applied rates, Applied tariff, Balance of payments basis, binding, border protection, border tax adjustment (BTA), bound, cabotage, Cost, insurance, freight (C.i.f), Committee on Regional Trade Agreements (CRTA), Committee on Trade and Development (CTD), Committee on Trade and Environment (CTE), Council for Trade and Goods (CTG), countervailing measures, Customs duty, customs union, de minimis, distortion, Doha Development Agenda (DDA), Doha Round, Domestic support, Dispute Settlement Body (DSB), Dispute Settlement Understanding (DSU), dumping, Enabling Clause, enquiry point, Equivalent Measurement of Support (EMS), European Free Trade Association (EFTA), European Union (EU), Export competition, Export prohibitions and restrictions, Foreign direct investment (FDI), free trade area, free-rider, G7, G8, General Agreement on Trade in Services (GATS), General Agreement on Tariffs and Trade (GATT), Generalized System of Preferences (GSP), Harmonized System, HS 6-digit, harmonizing formula, import licensing, International Trade Centre (ITC), Least-developed countries (LDCs), Lisbon Agreement, Madrid Agreement, Market Access, Market distortion, Minimum import price, Most-Favoured-Nation (MFN) tariff, North American Free Trade Agreement (NAFTA), nuisance tariff, Paris Convention, Preferential trade arrangements (PTAs), price undertaking, Regional trade agreements (RTAs), Rome Convention, schedule of concessions, Sensitive products, Special safeguard mechanism (SSM), Specific tariff, Tariff binding, Tariff escalation, tariff overhang, Tariff peaks, Tariff quota, tariff rate quota, Tariff water, Tariffication, Tiered formula, Variable import levy.

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