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Article

Integrating Corpus Linguistics and Text Mining to Analyze European Media Coverage on China–EU Electric Vehicle Dispute

by
Jinsong Fu
1,2,* and
Min Yang
1,*
1
Department of English, School of Foreign Languages, Renmin University of China, Beijing 100872, China
2
School of Global and Area Studies, Renmin University of China, Beijing 100872, China
*
Authors to whom correspondence should be addressed.
Journal. Media 2025, 6(4), 196; https://doi.org/10.3390/journalmedia6040196
Submission received: 1 September 2025 / Revised: 12 November 2025 / Accepted: 20 November 2025 / Published: 24 November 2025
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Abstract

This study innovatively moves beyond traditional mono-method research by employing an integrated approach that synergizes corpus linguistics and text mining. Through sentiment, thematic, and collocational analyses, it critically examines the representation of China’s image in European media coverage of the China–EU electric vehicle dispute. Initially, sentiment analysis of news reports concerning EU tariffs on Chinese electric vehicles was conducted. Subsequently, four key themes emerged from analyzing a corpus consisting of 202 news articles: “market reaction,”; “trade war,” “China’s response,” and “dialogue and negotiation.” Finally, collocation analysis of the keywords “China” and “Beijing” reveals four main images of China in European media: China is framed as the unfair-subsidy provider, threatener, negotiator, and defender. The key conclusion is that European media coverage is characterized by discursive ambivalence, simultaneously portraying China as both a threat and a partner. These findings are significant as they illuminate how media discourse serves as a key arena where the economic and political complexities of the China–EU trade conflict are negotiated, legitimized, and managed.

1. Introduction

Policymakers from both China and the European Union (EU) assert that their bilateral relationship is crucial not only to each other but also to the rest of the world, as they are key global actors (Freeman, 2022). Economics lies at the heart of the China–EU relationship but has also been a major source of disputes between China and the EU (Lai, 2023). A pivotal shift occurred in 2019 when the EU’s EU-China Strategic Outlook formally labeled China as a “systemic rival” (European Commission, 2019), signaling a move towards more assertive policies. This is evident in areas such as screening foreign direct investment and enacting new anti-dumping and anti-subsidy rules (Pavlićević, 2022). Against this backdrop, on 13 September 2023, European Commission President Ursula von der Leyen announced an anti-subsidy investigation into electric vehicle (EV) imports from China (von der Leyen, 2023a). The subsequent provisional conclusion in June 2024 to impose tariffs underscored the escalation of trade tensions, making the EV dispute a microcosm of broader China–EU friction (European Commission, 2024a).
The media functions as a crucial arena for discursive legitimation, where power structures are reinforced or contested (Ganter & Loeblich, 2021; Vaara et al., 2024). News is inherently ideological, shaping public perception through selective framing (van Dijk, 2009). While existing research has examined China’s image in Anglo-American or its own English-language media, there is a notable gap in the systematic analysis of its portrayal within the diverse European media landscape, particularly regarding a politically and economically significant issue like the EV dispute.
To address this gap, this study employs an innovative integrated methodology that combines corpus linguistics (CL) and text mining, guided by the lens of critical discourse analysis (CDA). This approach moves beyond traditional mono-method research by combining text mining’s capacity for macro-level mapping of sentiment and themes with corpus linguistics’ strength in micro-level, context-rich interpretation of discursive patterns. Our purpose is to systematically analyze the sentiment, themes, and dominant images of China in European media, thereby providing new insights into the interplay between media, trade policy, and international relations. Accordingly, this study aims to systematically examine European media coverage of the China–EU EV dispute to answer the following research questions:
RQ1: What sentiment is expressed by the European media in their coverage of EU tariffs on Chinese EVs?
RQ2: What key themes do European media focus on in this context?
RQ3: What types of China’s images are portrayed through the prominent language patterns employed?

2. Literature Review

2.1. CL and Text Mining in CDA

CDA, rooted in the premise that language use is a social practice intertwined with power and ideology, analyzes how “social power abuse, dominance, and inequality are enacted, reproduced, and resisted by text and talk in social and political contexts” (van Dijk, 2001). Given that mass media is a crucial intermediary in constructing power and ideology (van Dijk, 1995), the need to uncover these naturalized power structures makes CDA particularly appropriate for this study. However, a common critique of CDA is its reliance on small, selectively chosen datasets that seem to support predetermined conclusions (Baker, 2012). In this context, CL has been increasingly incorporated into CDA to address deficiencies in methodological rigor by integrating quantitative CL with qualitative CDA (Baker, 2012; Baker et al., 2008; Bednarek & Caple, 2014; Salama, 2011). CL techniques, such as keywords, frequency, and collocation, are common methods used to explore elusive linguistic patterns, and concordance helps further examine node words within specific texts in a corpus (Wang et al., 2023).
Recent advances in natural language processing (NLP) have led to the development of new text-mining techniques that offer valuable insights into CDA topics, with sentiment and thematic analyses being the most predominant methods (Lee, 2019; Wang et al., 2023; Yue et al., 2019). However, researchers should exercise caution with this robust and effective approach because of the inherent subjectivity in interpreting computational results (Bednarek, 2024). Combining traditional corpus and discourse analyses can partly address or reduce the limitations of text-mining techniques (Bednarek, 2024).
In short, CDA, as a research initiative focused on problem-solving, embraces a range of methods that enable the integration of linguistic and social analyses to fulfill its research agenda (Liu, 2024; Wodak, 2011). Guided by CDA, this study analyzes how European media discourse constructs power relations during the EV dispute. It focuses on (1) China–EU power renegotiation, (2) intra-EU political–economic tensions, and (3) the media’s role in mediating between policy agendas and industrial interests.

2.2. The Corpus-Based CDA of China’s Images in the Media

The concept “national image” can be traced back to Kenneth Boulding’s work (Boulding, 1959), in which he defines it as the images which “a nation has of itself and of those other bodies in the system which constitute its international environment.” From the perspective of international journalism, a primary research discipline on national image, mass media plays a vital role in constructing and disseminating national images (Hu, 2023; Tang, 2021), with framing theory (Entman, 1993) and agenda-setting theory (McCombs & Ghanem, 2001; Riker, 1986) emerging as the most representative frameworks. Methodologically, media studies on national images primarily focus on content analysis of news coverage to identify recurring frames or agenda patterns quantitatively and/or qualitatively (Tang, 2021), such as corpus-based or corpus-driven CDA.
Given China’s rise as a defining process in international politics due to global wealth redistribution and its potential to end unipolarity (Song et al., 2021), this context has driven an increase in studies examining China’s media representation through the methodological synergy of CL and CDA. According to different media sources, research on China’s image can be categorized into mainstream English outlets (Hu, 2023; Huan, 2024; Tang, 2021) and China’s English-language newspapers (Wang, 2018; Wang & Ma, 2021; Yu et al., 2024a, 2024b). In addition, scholars employing comparative analyses have converged on the finding that Chinese official English-language newspapers prefer to construct a positive and responsible national image, whereas foreign media outlets predominantly portray China as an authoritarian state and a geopolitical threat (Liu & Li, 2017; Zhang & Wu, 2017). Such negative perceptions may stem from the tendency to frame China’s rise through a liberal democracy lens, particularly in Anglo-American media (Song et al., 2021).

2.3. Economic Nationalism and Green Protectionism: Motives Behind the EU’s EV Policies

Recent scholarship has increasingly framed the EU’s assertive trade policies, particularly towards China in strategic sectors like EVs, through the perspectives of economic nationalism and green protectionism. In the post-pandemic era, the EU’s pursuit of “strategic autonomy” and “technological sovereignty” reflects a resurgence of “new economic nationalism” (Hauge et al., 2025), where industrial policy is leveraged to secure supply chains and protect core industries deemed vital for European “future economic prosperity and national security” (von der Leyen, 2023b). The EV battery sector exemplifies this shift, with the EU framing its dependence on external suppliers, especially China, as a critical vulnerability (Ragonnaud, 2025).
Concurrently, the EU is engaging in “green protectionism,” utilizing its first-mover advantage in environmental regulation to create de facto trade barriers. The EU Green Deal (EGD) is a prime example (Vela Almeida et al., 2023), and the discourse around EVs extends this logic, often framing Chinese products as benefiting from unfair state subsidies (European Commission, 2024a). This narrative allows the EU to align its protectionist measures with its green leadership ambitions.
The EU’s anti-subsidy investigation into Chinese EVs clearly manifests this dual motive. Tariffs are not merely an economic remedy but a strategic tool to shield the European automotive industry (Ghiretti, 2024). The EU simultaneously defines the terms of “green” and “fair” trade to disadvantage competitors, as evidenced by a range of regulatory changes, such as the EU Foreign Subsidies Regulation, the EU Battery Regulation, and the Supply Chain Due Diligence (Deloitte, 2024). This literature provides a critical foundation for understanding the EU’s complex motivations that are likely to underpin and be discursively legitimized within the European media coverage examined in this study.
While substantial research has examined China’s media image through CDA and CL, a significant gap remains in systematically analyzing its portrayal within the diverse European media landscape, particularly regarding a politically and economically significant issue such as the EV dispute. Furthermore, while the literature on economic nationalism and green protectionism provides a crucial backdrop for understanding the EU’s motives, little attention has been paid to how these complex policies are discursively mediated and legitimized in the European news coverage. Our study aims to address this gap.

3. Data and Methods

3.1. Data Collection

A specialized corpus, the European News Corpus of European Tariffs (ENCET), was constructed for this study to ensure thematic focus and analytical validity. The data collection procedure was designed for reproducibility and consisted of the following steps:
Corpus building. The initial dataset was retrieved from the LexisNexis database using the “English Language News” module, filtered by “Location by Publication: Europe” to capture the specifically European English media perspective.
Search strategy. News articles were generated using the search query: HEADLINE (“EU tariffs” OR “EU duties”) and DATE (between 12 June 2024 and 22 November 2024). This timeframe was selected to cover from the announcement of the provisional tariffs to the conclusion of the investigation and subsequent negotiations. Provisional EU duties of up to 38.1% on imported Chinese EVs were scheduled to take effect by 4 July (European Commission, 2024a), and the Commission concluded its anti-subsidy investigation by imposing definitive five-year countervailing duties on 29 October (European Commission, 2024b). However, Bernd Lange, the chair of the European Parliament’s trade committee, told a German broadcaster on November 22 that Brussels and Beijing were nearing a solution to the EV dispute (China Daily, 2024a).
Quality control. All retrieved articles underwent a two-stage screening process. First, LexisNexis’s automated filtering removed exact duplicates (articles with identical titles, publication dates, and sources). However, similar reports from different outlets were retained to maintain media diversity. Second, each article was manually verified by the authors to confirm that its core focus was on EU tariff policies regarding Chinese EVs.
This rigorous process resulted in a final corpus of 202 news articles. The corpus comprised 99,192 tokens after being processed and tokenized using the default settings of KH Coder. Table 1 displays 51 news outlets, including broadcast and print, online and offline, and regional and national news media outlets.

3.2. Analytical Methods

This study employed sentiment, thematic, and collocational analyses with concordance lines to address the research questions. Regarding sentiment analysis, this study employed document-level sentiment analysis to classify documents as expressing negative, positive, or neutral sentiments or opinions, thereby facilitating the detection of sentiment orientation in European media. Next, the co-occurrence network in KH Coder identified key themes in ENCET for thematic analysis. KH Coder can display groups of frequently occurring words, allowing researchers to discern the main themes of the text (Koichi, 2016). Finally, regarding collocation, the term refers to a pattern of co-occurrence between two items that often appear near each other, though not always next to one another (McEnery & Hardie, 2012, p. 123). Collocation analysis is crucial for linking corpus linguistics with discourse analysis, as it facilitates an understanding of the contextual meanings of words (Elsoufy, 2024). According to Baker (2023, pp. 157–158), concordance lines containing collocates suggesting a semantic preference or discourse prosody help flesh out how the two words are related.

3.3. Analysis Procedure

We employed the VADER sentiment analysis tool (Hutto & Gilbert, 2014) to analyze European media coverage because of its proven effectiveness with news content (Bucur et al., 2024). This approach was adopted to capture the overall tone of each news article regarding EU tariffs on Chinese EVs, providing a macro perspective of the media stance. Following text pre-processing (lemmatization and stopword removal processed by Python’s NLTK library (version 3.8.1), VADER generated a polarity score between −1 and 1 for each document, which we categorized as positive (≥0.05), negative (≤−0.05), or neutral (between −0.05 and 0.05).
Second, KH Coder generated a co-occurrence network to further interpret the main themes of the ENCET. When using KH Coder for content analysis, the stop-word list could be loaded through the global settings, and the menu option “Pre-processing” allowed for the extraction of words by using “TermExtract,” which helped better retain meaningful vocabulary combinations. To display a readable and interpretable network that balances informational richness with visual clarity, the parameters were set after iterative testing: the minimal number of “Filter words by Term Frequency” was set to 65 to filter out noise while retaining salient terms, and the top of “Filter edges” was set to a maximum of 240 to avoid an excess of subgraphs. The “Draw the minimum spanning tree only” option was subsequently selected to further distill the most essential connections for thematic analysis, with the default setting for the rest of the parameters.
Third, AntConc was used to extract the collocates of “China” and “Beijing” and to examine concordance lines for these terms. Collocation analysis was conducted with a default search window size of 50, applying an MI score threshold of ≥3 and sorting the results by frequency. An MI of 3 or above “can be taken to be significant” (Huston, 2002, p. 71) and helps to strike a balance between excluding and including low-frequency collocations (Xia et al., 2024).

4. Findings

4.1. Sentiment Analysis

A strip plot (Figure 1) illustrates the sentiment distribution across news reports, with positive, neutral, and negative reports comprising 45.05%, 1.49%, and 53.47% of the total, respectively. This indicates a notable polarization of sentiment within the reports, with fewer articles exhibiting ambiguous sentiments. Additionally, the concentration of data points near −1 and 1 suggests that most news reports express a strong sentiment.
Based on the classification of reports, each word in both positive and negative reports was examined to calculate the sentiment score. Words in positive reports with a sentiment score exceeding 0.5, or those in negative reports with a score below −0.5, were identified as strongly emotional, and their frequency was recorded. The top 20 results are listed in Table 2. Sentiment analysis revealed a clear dichotomy in media framing. Positive reports focus on the principles of free trade and competition, often linking tariff policies to economic success and to strategic resilience. In contrast, negative reports frame tariffs as punitive measures and potential triggers of trade conflict, expressing concerns over economic harm and questioning the policies’ legitimacy.

4.2. Themes

This section explores some significant themes across the ENCET to answer the second research question. Four key themes were identified based on the intriguing visualization of the co-occurrence network, as shown in Figure 2.

4.2.1. Market Reaction

Categories 1, 2, and 4 reveal the European media’s dual focus on the market impact of tariffs. On the Chinese side, attention has been centered on major manufacturers such as BYD, Geely, and SAIC, which have faced higher-than-expected tariff rates. This prompted considerations of relocating production to Europe. Nevertheless, the European Commission’s own projection of a rising market share underscores the resilience and strong momentum of Chinese EVs in the market (Example 1).
Example 1.
Chinese-made EVs currently hold an 8% share of the EU market and the Commission predicts this will rise to 15% by the end of 2024 (AutoCar, 2024).
Concurrently, European media extensively covered the tariffs’ implications for their automotive industry. The EU’s decision, following an anti-subsidy investigation, was widely reported in the media. However, significant internal disagreement existed, most notably from Germany, whose auto industry lobbied against the measures, arguing that they would not enhance European competitiveness (Example 2).
Example 2.
Germany’s influential auto-industry lobby urged the German government on Wednesday to oppose proposed punitive EU tariffs on imported Chinese electric vehicles (dpa international, 2024).

4.2.2. Trade War

Category 5, as shown in the visualized network, focuses on trade conflict. The “Stats” function of KWIC concordance reveals that the top three collocations frequently co-occurring with the bubble “trade” are “war” (50), “tension” (42), and “conflict” (32), indicating that the imposition of EU tariffs on Chinese EVs escalated trade tensions between China and the EU, increasing the likelihood of a trade conflict. Despite divisions among EU member states over the implementation of duties, the EU moved forward with controversial tariffs and ignored warnings from several European countries (Example 3).
Example 3.
The decision came despite opposition from major EU member states like Germany and Hungary, which warned that the tariffs could trigger a trade war with China (Invezz, 2024).
Regarding China’s attitude, it has repeatedly pointed out that the European tariff decision brought nothing but grave consequences, warning that EU tariffs would unleash a trade war if Brussels did not return to the negotiating table. Nonetheless, some European analysts have observed that China, still affected by US tariffs, was reluctant to become involved in another trade conflict with the EU. Certain officials within the European Commission adopted an unconcerned stance regarding China’s potential reactions (Example 4).
Example 4.
Valdis Dombrovskis, a European Commission vice-president, brushed aside concerns of trade war retaliation from Beijing against European business in light of the EV tariffs (The Guardian, 2024).

4.2.3. China’s Response

Categories 3 and 7 pertain to China’s protective measures against EU tariffs on EVs. The Commission argued that tariffs were required to counter “unfair subsidies” from China and protect European industry from “unfair competition.” In reply, China pointed out that the EU move was a protectionist practice of “unfair competition” in the name of “fair competition.” Simultaneously, China launched an anti-subsidy investigation of brandy and pork imported from Europe (Example 5). Moreover, China filed a complaint using the WTO’s dispute resolution process to safeguard the rights and interests of the electric vehicle industry and to encourage international collaboration in green transformation (Example 6).
Example 5.
In response, China has launched investigations into French cognac exports and European pork in what some analysts fear could develop into an economically harmful trade war with the EU (Belfast Telegraph, 2024).
Example 6.
The ministry said that the EU’s provisional decision to impose tariffs lacks a factual and legal basis, violates WTO rules, and undermines global cooperation over the tackling of climate change (dpa-AFX, 2024).

4.2.4. Dialogue and Negotiation

Lastly, Category 6 highlights the bilateral willingness to resolve this trade conflict, with two keywords “negotiation” and “talk” representing the critical approach to reaching a satisfactory agreement for both parties. The bubble “continue” reflects the permanence and complexity of dealing with the EV dispute. After eight rounds of negotiations in Brussels, China and the EU failed to reach a mutually acceptable solution. China and the EU would remain committed to reach a solution or resolve the issue through talks, and this was also a cry of some in the European automobile industry (Example 7).
Example 7.
The industry association (The German Association of the Automotive Industry) called on the EU to cancel the tariff increases and find a “negotiated solution” with Beijing (Agence France Presse, 2024).

4.3. Collocation Analysis

The terms “China” and “Beijing,” which frequently appeared with 1145 and 282 occurrences in ENCET, were selected for collocation analysis to investigate China’s portrayal in European media and to address the second research question. Table 3 presents the top 20 collocates associated with “China” and “Beijing.” The principal national images in Table 4 were derived from these collocates through manual coding, based on semantic prosody and contextual analysis.

4.3.1. China as the Unfair-Subsidy Provider

The collocate “unfair(ly)” carries a negative semantic prosody, suggesting the EU’s perception of China’s subsidies as injustice and wrong-doing, not only to undeservedly benefit its domestic industry, but also to harm European automakers. In the ENCET corpus, the pattern of association between China’s unfair trade practices and terms that suggest an official investigation is reinforced by the presence of “Brussels’ probe” or “an investigation” as shown in Figure 3. In other words, the European media emphasized the justification for the EU’s tariff imposition, framing the EU’s actions as a necessary response to unfair trade practices. This is evident in the lexical patterns in Figure 4, where phrases such as “because of unfair subsidies” or “is necessary to” attribute legitimacy to the EU’s countermeasure. This media framing aligns with Vaara et al.’s (2024) study, which suggests that media outlets serve as a vital arena for legitimation, in which the selective representation of events can significantly shape public perceptions.

4.3.2. China as the Threatener

In Figure 5 and Figure 6, the source of action is clearly marked by the noun possessive “China’s retaliation” and the prepositional phrase “from Beijing.” This indicates that the European media connected the retaliatory measures against the EU’s tariffs to the Chinese government. In ENCET, the collocate “retaliation” carries a negative semantic prosody and often co-occurs with terms that suggest threat (e.g., “risk a retaliation,” “repeatedly threatened retaliation,” “fury and retaliation,” “threats of retaliation,” and “fear of retaliation”). European media perceived China as a threat and used the term “retaliation” to imply a loss-loss situation when referring to China. The Oxford Advanced Learners’ Dictionary defines retaliation as an “action that a person takes against somebody who has harmed them in some way.” European media portrayed China’s actions as retaliatory and potentially harmful to EU-China relations. However, different political positions lead to different media perspectives. While the European media depicted China’s retaliatory measures in response to the EU’s tariff decision, the Chinese media presented a diametrically opposite view of China’s anti-dumping investigation into European products. According to China Daily (2024b), China did not target any specific EU member state and would conduct the investigation openly and transparently, while fully safeguarding the rights of all stakeholders. In other words, China’s media portrayed the anti-dumping investigation as a fair and impartial process, emphasizing its commitment to international trade norms.

4.3.3. China as the Negotiator

In the ENCET corpus, the concordance lines for “China” and “Beijing,” along with their collocates “negotiations” and “talks” as shown in Figure 7 and Figure 8, indicate that these two node words most frequently appear as objects within prepositional phrases. The combinations “negotiations with China” and “talks with Beijing,” which suggest a positive semantic prosody characterized by solution-seeking actions, underscore the central role of dialogue and the importance of diplomacy in resolving trade disputes. European media adopted a subjective perspective to emphasize the EU’s commitment to maintaining effective communication and cooperation with China. These reports reflect China’s willingness to engage in discussions and negotiations, as evidenced by the phrase “eight rounds of technical negotiations” in Lines 22 and 23 of Figure 7. Lines 3–6 in Figure 8 also display the Chinese perspective, citing the claim to establish minimum prices made by the Chinese Chamber of Commerce to the EU.

4.3.4. China as the Defender

Finally, in ENCET, the format of proper nouns is the frequent pattern of association between the two node words and terms that suggest official institutions and personnel, as shown in Figure 9 and Figure 10. These proper nouns contain the following: “China Chamber of Commerce to the EU,” “China’s Commerce Ministry(er),” “Beijing’s Commerce Ministry”, etc. In journalism, a key function of reported speech is evidentiality, “used to provide evidence for claims in the text” (Waugh, 1995). European media still quoted statements from the Chinese side to disseminate China’s voice within the EU objectively, aside from Europe’s negative perceptions of China. By including China’s attitudes (e.g., “shock, disappointment and dissatisfaction,” “firmly opposes,” “does not agree with or accept”), European media discursively constructed China as a defender that opposed the EU’s tariff measures and safeguarded legitimate rights.

5. Discussion

When employing corpus linguistics and text mining, it is essential to consider the social, political, historical, and cultural contexts of the data to better understand the results of quantitative and qualitative analyses (Baker et al., 2008). Sentiment, theme, and collocation analyses reveal that European media discourse on the China–EU EV dispute is characterized by a discursive conflict, simultaneously constructing China as both a geoeconomic threat (the unfair-subsidy provider and threatener) and an indispensable negotiating partner (the negotiator). Sentiment analysis confirmed this polarization, showing a near-even split between negative and positive stances, with few neutral reports. Thematic and collocational analyses further substantiated this ambivalence, revealing a discourse swaying between conflict (trade war and retaliation) and cooperation (dialogue and cooperation).
Our thematic analysis demonstrates that media discourse serves as a key arena where the political and economic divisions among EU member states are discursively reflected and amplified. The EU’s imposition of controversial tariffs, despite strong opposition from members like Germany and Hungary, was framed as an action that “could trigger a trade war.” This framing echoes the thematic prominence of “market reaction” and “trade war” identified in the co-occurrence network, illustrating how media discourse translates political fragmentation into a public narrative. The specific economic interests of member states, such as Hungary’s absorption of 44% of Chinese FDI in Europe (Kratz et al., 2024) and Germany’s robust economic ties with China, are not merely political facts but form the underlying rationale for the divergent national stances projected into the media narratives. For instance, the German automotive lobby’s opposition to tariffs, as in Example 7, directly reinforces the “dialogue and negotiation” theme and counters the “threat” image found in collocation analysis. These findings are consistent with 2018 the European Think-tank Network on China (ETNC) report (Rühlig et al., 2018), revealing that the stances of EU member states on China are shaped by three particularly important factors: historical legacy, economic relations with China, and Chinese pressure.
Beyond reflecting divisions, our concordance analysis shows that the media functions as an active agent of discursive legitimation (Vaara et al., 2024). The shift in the EU’s perception of China, from a partner to a “systemic rival” (European Commission, 2019), is discursively manifested in media reports that frame China’s subsidies as creating “unfair competition” and “undercutting” European automakers. Such framing is agenda-setting, serving to legitimize the EU’s imposition of countervailing duties as a necessary and justified response. By consistently highlighting China’s “unfair subsidies” and “retaliatory measures” as in Section 4.3.1 and Section 4.3.2, European media participate in shaping the EU’s public evaluation of China, translating complex political and economic decisions into accessible narratives of threat and justification. This process exemplifies the media’s constitutive role in shaping political reality.
While the primary focus of this study has been on the European media’s construction of China’s image, our findings also invite an intriguing intertextual question: how might Chinese official media, such as China Daily, construct the image of the European Union in reverse? Although a systematic analysis is beyond our scope, a preliminary review of the coverage suggests potential discursive mirroring. For instance, when European media framed the EU’s tariffs as a necessary response to “unfair subsidies,” Chinese official discourse often countered by framing the EU’s actions as “protectionism” and a deviation from WTO rules (China Daily, 2024c, 2024d, 2024e), thereby constructing an image of the EU as an unreliable and unfair trade partner. This initial observation suggests that discursive conflict is not one-sided but involves a co-constructive process where each side positions the other as the transgressor to legitimize its own stance. This interplay forms a compelling object for future research, specifically designed to undertake a balanced and systematic comparative discourse analysis.

6. Conclusions

This study’s integrated analysis yields three definitive conclusions that directly answer the research questions and underscore the role of the media.
First, in direct response to RQ1–RQ3, the data reveal a strategically ambivalent discourse: sentiment is polarized, themes oscillate between conflict and cooperation, and China is simultaneously framed as both a geopolitical threat and a negotiating partner. This duality directly mirrors the EU’s conflicted policy stance and internal political divisions.
Second, and more significantly, the media do not function as a passive mirror but as an active agent that simultaneously legitimizes and manages political discourse. Through its framing of China as the “unfair-subsidy provider,” it legitimizes the EU’s tariff actions, while simultaneously managing public controversy by balancing “threat” and “cooperation” narratives to avoid a one-sided consensus and reserve room for policy adjustment. This dual process ultimately shapes the political reality of the dispute, directly influencing internal policy debates and public attitudes in the EU. Thus, European media discourse is a primary site where the political and economic tensions of the China–EU relationship are not just reported but actively negotiated, legitimized, and managed.
This study had several limitations. First, the corpus was restricted to English-language publications from European media outlets, thereby excluding non-English European media reports, which may offer different perspectives. Second, the analysis was confined to a specific time frame (June to November 2024) surrounding the immediate tariff dispute, potentially missing longer-term discursive shifts. Future research could address these limitations by constructing a multilingual corpus to enable cross-linguistic comparisons, extending the temporal scope to track the evolution of media discourse, and constructing a balanced corpus to systematically compare discourse across different media types in framing China’s image.

Author Contributions

Conceptualization, J.F. and M.Y.; methodology, J.F.; software, J.F.; formal analysis, J.F.; data curation, J.F.; writing—original draft preparation, J.F.; writing—review and editing, J.F. and M.Y.; visualization, J.F.; supervision, M.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset of news articles generated and analyzed during this study is available through the LexisNexis database.

Acknowledgments

During the preparation of this manuscript, the authors used DeepSeek V3 for the purposes of polishing the English language. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Journalmedia 06 00196 g001
Figure 1. Distribution of sentiment scores of European media reports.
Figure 1. Distribution of sentiment scores of European media reports.
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Figure 2. Co-occurrence network of ENCET.
Figure 2. Co-occurrence network of ENCET.
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Figure 3. Concordance lines of “China” and the collocate “unfairly”.
Figure 3. Concordance lines of “China” and the collocate “unfairly”.
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Figure 4. Concordance lines of “Beijing” and the collocate “unfair”.
Figure 4. Concordance lines of “Beijing” and the collocate “unfair”.
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Figure 5. Concordance lines of “China” and the collocate “retaliation”.
Figure 5. Concordance lines of “China” and the collocate “retaliation”.
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Figure 6. Concordance lines of “Beijing” and the collocate “retaliation”.
Figure 6. Concordance lines of “Beijing” and the collocate “retaliation”.
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Figure 7. Concordance lines of “China” and the collocate “negotiations”.
Figure 7. Concordance lines of “China” and the collocate “negotiations”.
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Figure 8. Concordance lines of “Beijing” with the collocate “talks”.
Figure 8. Concordance lines of “Beijing” with the collocate “talks”.
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Figure 9. Concordance lines of “China” and the collocate “Commerce” (lines 1–21).
Figure 9. Concordance lines of “China” and the collocate “Commerce” (lines 1–21).
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Figure 10. Concordance lines of “Beijing” and the collocate “Commerce”.
Figure 10. Concordance lines of “Beijing” and the collocate “Commerce”.
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Table 1. Composition of ENCET.
Table 1. Composition of ENCET.
Region/Country (Number)Name (Number of Articles)
EU (2)EurActiv (12), EuroNews (4)
Baltic States (3)Impact News Service (7), Impact Financial News (7), The Baltic Times (1)
the UK (20)Alliance News (11), Just Auto (8), Autocar (6), Invezz (6), MarketLine NewsWire (6), Business World Agency (5), Daily Mirror (3), Proactive Investors (3), The Guardian (3), The Independent (3), Belfast Telegraph (2), Mail Online (2), Silicon UK (2), The Telegraph (2), GB News (1), Just Drinks (1), Motor Trader (1), The Daily Telegraph (1), The Express (1), The Western Mail (1)
Germany (5)dpa (17), Deutsche Welle World (5), Die Welt (3), EQS news (1), German news (1)
Ireland (4)Irish Examiner (5), RTE News (5), Irish Independent (3), The Irish Times (1)
France (3)Agence France Presse (27), France 24 (1), RFI (1)
Turkey (3)TRT World (4), Anadolu Agency (2), Hürriyet Daily News (1)
Spain (2)Spanish News (4), EFE (2)
Hungary (2)Hungarian News Digest (3), Budapest Business Journal (1)
Luxembourg (1)Luxembourg Times (3)
Denmark (1)M-Brain Denmark News (1)
Uncertain (5)Automotive Monitor Worldwide (5), Business Monitor Online (3), ICIS (2), China Report Magazine (1), Future News (1)
Table 2. The frequency of emotion-laden words in European media coverage.
Table 2. The frequency of emotion-laden words in European media coverage.
SentimentFrequency
positivefree (28), strong (14), success (9), great (8), gain (7), confidence (6), generous (6), trust (5), happy (4), enjoy (4), successful (4), positive (4), rescue (4), encourage (3), brilliance (3), win (3), winning (2), justice (2), fun (2), fantastic (2)
negativewar (44), punitive (34), threat (31), harm (15), hurt (14), violate (9), negative (9), harming (6), threatening (6), bad (6), abuse (5), disappointment (4), ban (4), damaging (4), rejected (4), dead (3), fearing (3), rejection (3), violating (3), worsen (3)
Table 3. Collocates of node words “China” and “Beijing” (top 20).
Table 3. Collocates of node words “China” and “Beijing” (top 20).
Node Word “China”Node Word “Beijing”
RankCollocatesFrequencyMICollocatesFrequencyMI
1trade1153.13Brussels284.68
2imports533.37trade263.00
3commerce534.03subsidies254.25
4imported524.18ministry184.43
5ministry453.73commerce174.42
6exports403.68talks144.88
7economic323.78unfair114.09
8tensions314.35tensions114.87
9negotiations243.81launched114.85
10cooperation243.81investigations105.76
11retaliation234.39support94.09
12products213.64retaliation95.06
13continue203.96move93.64
14unfairly194.34war84.14
15association193.62unfairly85.12
16pork183.19probes85.66
17anti-dumping173.51domestic84.16
18minister163.50brandy83.71
19war153.02anti-subsidy83.34
20response153.67accused86.00
Table 4. China’s images in European media.
Table 4. China’s images in European media.
China’s ImagesCollocates of “China” and “Beijing”
the unfair-subsidy providerunfair(ly), subsidies, anti-subsidy, support, domestic, accused, trade
the threatenerretaliation, response, move, launched, anti-dumping, investigations, probes, pork, brandy, products, imported, trade, tensions
the negotiatornegotiations, cooperation, talks, continue, trade
the defendercommerce, ministry, minister, association
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Fu J, Yang M. Integrating Corpus Linguistics and Text Mining to Analyze European Media Coverage on China–EU Electric Vehicle Dispute. Journalism and Media. 2025; 6(4):196. https://doi.org/10.3390/journalmedia6040196

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Fu, Jinsong, and Min Yang. 2025. "Integrating Corpus Linguistics and Text Mining to Analyze European Media Coverage on China–EU Electric Vehicle Dispute" Journalism and Media 6, no. 4: 196. https://doi.org/10.3390/journalmedia6040196

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Fu, J., & Yang, M. (2025). Integrating Corpus Linguistics and Text Mining to Analyze European Media Coverage on China–EU Electric Vehicle Dispute. Journalism and Media, 6(4), 196. https://doi.org/10.3390/journalmedia6040196

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