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Examining the Roles, Sentiments, and Discourse of European Interest Groups in the Ukrainian War through X (Twitter)

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05 June 2024

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06 June 2024

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Abstract
This research focuses on examining the responses of interest groups listed in the European Transparency Register to the ongoing Russia-Ukraine war. Its aim is to investigate the nuanced reactions of 2,579 commercial and business associations and 2,957 companies and groups to the conflict, as expressed through their X (Twitter) activities. Utilizing advanced text mining, NLP and LDA techniques, this study conducts a comprehensive analysis encompassing language dynamics, thematic shifts, sentiment variations, and activity levels exhibited by these entities both before and after the outbreak of the war. The results obtained reflect a progressive decreasing in negative emotions regarding the conflict over time. Likewise, multiple forms of outside lobbying are identified in the communication strategies of interest groups. All in all, this empirical inquiry into how interest groups adapt their messaging in response to complex geopolitical events holds the potential to provide invaluable insights into the multifaceted role of lobbying in shaping public policies.
Keywords: 
Subject: 
Social Sciences  -   Political Science

1. Introduction

The conflict between Russia and Ukraine, which began on February 24th, 2022, constituted a major event involving numerous international actors. This geopolitical crisis triggered significant global consequences, leading to tensions and raising concerns within the international community. The events related to this invasion elicited a wide range of opinions and responses from governments, organizations, and interest groups worldwide. The political complexity and international dynamics make this conflict a crucial subject for research and analysis in academic and political spheres.
The theoretical contextualization of this paper introduces the concept of grassroots lobbying as a valuable political tool for interest groups and delves into how social media, particularly Twitter (onwards referred as X), has transformed the communication dynamics of these groups. Furthermore, it reviews research addressing the relationship between X and the conflict in Ukraine, emphasizing the need for studies in this field. In line with this perspective, the study aims to examine the potential participation of European interest groups in the context of the Russian invasion, with a specific emphasis on their activities on the social media platform X. The analysis focuses on two categories of interest groups: “trade and business associations” and “companies and groups”. These groups represent a wide spectrum of the economic interests in the European Union.

1.1. Grassroots Lobbying and Its Transformative Impact on EU Policy Dynamics

The practice of outside lobbying, also known as grassroots lobbying, has become a fundamental strategy in the realm of political influence and public policy formulation [1,2]. In contrast to direct interactions between interest groups and decision-makers, it relies on the use of public communication channels as the primary means to achieve its objectives [3]. Notably, in the European Union, outside lobbying has assumed a relevant role, influencing decision-making processes and responding to the growing politicization and public scrutiny that characterize policy formulation within this supranational entity.
This approach encompasses tactics that involve interaction with journalists, issuing press releases, conducting public campaigns, and organizing protest events [4]. While grassroots lobbying has traditionally been considered a tool used by political actors with limited influence or as a last resort in their strategic arsenal [5], prior research in the European context has revealed that numerous organized interest groups rely to varying degrees on such actions [6,7]. This suggests that outside lobbying may have evolved into a relevant and effective strategy for influencing EU policy decisions, especially in response to the increasing public attention in the policy formulation process. By utilizing public communication as a tool, these lobbying groups aim to demonstrate strong public support and attract a broad audience of stakeholders in the political debate. Their goal is to exert pressure on policy-makers to persuade them to take actions aligned with their interests. Failing to respond to this pressure carries the risk of eroding their reputation or facing negative electoral consequences [8].
In the academic sphere, a debate has arisen regarding the predominance of lobbying strategies, whether they are internal or external, in relation to the typology of interest groups. According to the perspective of Dür and Mateo [9], the frequency of employing outside lobbying actions, varies depending on the category in which the group operates. For instance, in the case of citizen groups, their reliance on these strategies is higher because their organizational viability often depends on public support. In contrast, business associations and companies typically do not require the same level of grassroots lobbying since they have the capacity to exert direct influence on policies. This divergence in the dependence on outside lobbying is partly attributed to its dual function: it allows interest groups for the promotion of policies aligned with their values while providing an opportunity to attract new supporters to their causes [10]. On the other hand, the opposing viewpoint argues that all interest groups, regardless of their typology, depend on outside lobbying. One of the key arguments in this regard is based on the pronounced politicization component characterizing the current social context, where the increase in visibility and controversy of a particular issue further accentuates the disparity in the use of these lobbying strategies [11,12,13].
Beyond this debate, it is undeniable that the advancement of technologies has radically transformed the paradigm of interactions among actors involved in public policy formulation, leading to an increase in the use of external lobbying strategies [14]. This shift has been evident in the substantial rise in the relevance of social media platforms, the dissemination of information online, and the capacity for mass mobilization through digital media in the realm of political influence or positioning [15]. These technological tools have empowered interest groups to expand their reach and exert influence more effectively in the public sphere, reshaping traditional lobbying methods [16]. Consequently, strategic adaptation to these technologies has become a critical component for the success of lobbying campaigns.

1.2. Social Media as a Key Component in Contemporary Lobbying Strategies

As previously mentioned, in the landscape of outside lobbying strategies employed by interest groups, those related to the use of social media and digital platforms have gained increasing prominence in the contemporary communication of these organizations [17,18]. Technological tools of this nature enable the rapid and widespread dissemination of messages, while also providing the capability to interact directly and personally with the audience [19]. In fact, acquiring online audiences is now an essential goal in lobby communication strategies [20].
The role of social media in the array of strategies implemented by interest groups extends beyond mobilizing their support bases in front of legislators. These platforms also offer the opportunity to establish strategic positions in the political agenda. Social media allows interest groups to closely monitor real-time political debates, identify emerging trends, and actively engage in the public sphere. This represents a shift from traditional channels of influence, requiring the construction and effective management of a strong digital presence as a fundamental requirement in the process of influence [19]. Besides that, globalization influences communication strategies by extending the reach of messages to an international audience, making it a factor to consider [14].
Furthermore, it is necessary to recognize that the digital communication of lobbies also impact the social context from an informative point of view, as it contributes to keeping citizens informed about current political issues. Therefore, social media platforms serve as an important channel for disseminating information and promoting public discourse, empowering recipients to critically formulate their own opinions and develop a more comprehensive understanding of the issues at hand. This enrichment fosters active citizen participation in the democratic process [21]. In this regard, it should be noted that citizens tend to consume messages related to issues that have a direct repercussions on their lives more frequently, at the expense of those that do not immediately relate to their specific needs [14]. However, this trend does not apply uniformly in all cases, underscoring the importance of considering individual and contextual variations in the perception of the relevance of the topics addressed. This, in turn, highlights the need to adapt messages according to the objectives pursued and the target audience [19].
Indeed, this strategic imperative for interest groups to comprehend the context and actors involved in their communicative processes, serves as a determining factor in formulating the study’s second objective. This objective seeks to discern the discursive frames employed by European interest groups both prior to and subsequent to the Russia-Ukraine conflict.
Besides the indirect impact of communications through social media, there is also a potential for direct influence, ensuring that messages reach legislators. In such instances, the receptive atmosphere for these messages facilitates lawmakers’ consideration of the arguments during the decision-making process [22].
In the digital sphere, X stands out as one of the most prominent tools for interest groups [23,24]. This platform offers unique versatility by allowing these organizations to communicate across the three key dimensions. Firstly, it proves to be an effective tool for mobilizing their bases, generating strong media attention, and actively participating in real-time public debates. Secondly, X provides the capacity to establish a clear positioning for lobbying groups regarding current issues. Lastly, it offers the opportunity for direct interaction with political figures, government officials, and other stakeholders involved in the decision-making process, facilitating the building of close relationships and effective promotion of their political agendas.

1.3. X Data Exploration of the Russia-Ukraine War

Several datasets containing information about activities related to the war in Ukraine have been compiled, enhancing the understanding of different aspects of the conflict [25,26,27,28]. Academic research has analyzed different features from these datasets, such as hashtags, presidential references, and contextual words, focusing on sentiment analysis and the polarization in discussions about the invasion [29,30,31].
Each of the studies takes a unique approach and delves into aspects of the Russia-Ukraine war. For example, Sazzed [28] concludes that negative feelings towards the conflict in general prevail, while there are positive feelings associated with humanitarian support and Ukrainian resistance.
Building upon these findings and with the aim of conducting a more comprehensive analysis of message sentiment, a third goal is introduced. This objective utilizes an evolutionary analysis approach to evaluate the shifts and trends in both positive and negative sentiments within messages emanating from interest groups following the Russia-Ukraine conflict.
Likewise, the most discussed topics on the platform have been thoroughly investigated, revealing high interaction on issues such as fundraising, sanctions, and gas and oil prices, among others [26,27,28]. For example, the analysis carried out by Nisch [31] regarding the speech of Volodýmyr Zelenski (President of Ukraine) on X revealed the use of eight different frames of reference in his communications. During the study period, the most prominent frames were those related to dialogue, solidarity, defense and love, indicating an optimistic orientation in communication aimed at promoting unity and resilience.
Despite the breadth of topics investigated so far, there have been no studies focusing on the behavior and role of interest groups on X in relation to the conflict. Therefore, it is pertinent to develop research in this direction to enrich the understanding of the communication used by these international actors. This study not only contributes to the detailed analysis of their participation on the platform but also provides a scientific basis for understanding the strategies employed by these interest groups in promoting their lobby agendas on a global scale, particularly in the context of pressing contemporary issues. Considering that, the last objective of this study is designed to analyze those interest groups that have exerted significant influence through retweets and original tweets during the post-war period, thereby offering insight into their impact on the discourse surrounding this critical international event.

2. Materials and Methods

2.1. Objectives and Hypothesis

Although the research objectives have been progressively detailed throughout the previous section, in this one, they are explicitly presented along with their corresponding hypotheses.
Objective 1: 
Investigate whether there is involvement by European interest groups in the Russian invasion through their digital communication activities on X.
Hypothesis 1: 
European interest groups are communicatively involved in the Russian invasion by actively posting tweets related to the conflict on X.
Objective 2: 
Analyze the changes in discursive frames employed by European interest groups on X before and after the outbreak of the Russia-Ukraine war.
Hypothesis 2: 
There is a shift in the discursive frames employed by European interest groups on social media before and after the onset of the Russia-Ukraine conflict, indicating a change in communication paradigms and including the conflict in their agendas.
Objective 3: 
Examine the evolution in positive and negative sentiment within tweets from European interest groups after the outbreak of the conflict.
Hypothesis 3: 
There is a decrease in negative sentiment in the messages from European interest groups as the conflict progresses.
Objective 4: 
Identify the interest groups that have had the greatest influence through original tweets and retweets during the post-war period.
Hypothesis 4: 
There are significant differences in the influence exerted by interest groups through original tweets and retweets during the post-war period.

2.2. Groups of Interests Data Collection

This study utilizes the European Union’s Transparency Register [32] as the primary data source, a key platform for registering and disclosing lobbying activities in the European Union. To guarantee data integrity and reliability, a filtering process was implemented to selectively include only those groups of interest that fall into the two relevant categories for the study: “trade and business associations” and “companies and groups”.
Through the filtering process, a total of 5,548 distinct groups of interest registered in the European Transparency Register were identified.

2.3. X Activity Data Collection

Due to the dynamic nature of social media and recent policy updates on platform X, verifying the authenticity of accounts presents a challenge. To enhance the validity of the dataset, only accounts listed on the official websites of the respective interest groups were included. This approach aimed to minimize the inclusion of potentially misleading or unverified accounts.
As mentioned, initially, 5,548 groups were identified, of which 112 organizations did not have a published website on the Transparency Register. Wickham’s [33] method was then applied to extract the available X accounts from the respective organizations’ webpages, resulting in the identification of 3260 accounts. Further refinement of the data involved excluding accounts with incomplete information, those that were closed, and accounts where the username could not be extracted, resulting in a total of 2,722 X accounts. The reduction from 2,722 to 2,260 accounts in the final sample, is due to the absence of published tweets during the study period.
To gather tweets from the selected interest groups, the ‘rtweet’ tool developed by Kearney [34] was used, leveraging the X API. The data collection took place intermittently between April 4th and April 10th, 2023, due to limitations of the X API. All tweets posted by these groups from February 14th to March 6th, 2022, were collected.
Lastly, to enhance the focus and relevance of the analysis, the sample of tweets was narrowed down by excluding those associated with interest groups headquartered outside the European Union and tweets that could not be translated into English. This stringent selection criterion resulted in a final sample size of 36,831 tweets, which will serve as the basis for the analysis.

2.4. Comprehensive Methodological Explanation

This study adopts a quantitative approach to analyze the X discourse of trade and business associations, companies and groups registered in the EU Transparency Register before and after the outbreak of the Russia-Ukraine war. It utilizes numerical data and statistical techniques, such as frame analysis, sentiment analysis, and network analysis, to examine the discursive framing, changes in sentiment, and network dynamics in this specific context.
The first phase encompassed the cleaning of the 36,831 available tweets throughout text mining techniques [35,36,37,38]. When dealing with a multilingual perspective, it is advisable to first translate all texts into the language that is most prevalent in order to proceed with a more appropriate cleaning and standardization of the texts. In this case, the Lucas and Tingley [39] tool is utilized to translate all non-English languages using Google API Translate. This approach provides other researchers the option to conduct further analysis using both the data and the code employed in this study.
The second step of this investigation entails frame analysis before and after the invasion began. To initiate this analysis, the research team applies topic modeling technique Latent Dirichlet Allocation (LDA) [40,41,42,43]. The intention is to assess the differences in discursive topics using the outbreak of the war as a turning point for comparison.
Despite the acknowledged limitations of this method [44], utilizing the R package developed by Grün and Hornik [45], and assuming that each document is a mixture of various topics and each topic is characterized by a distribution of words, it is possible to observe which topics are present before and after the war.
Once the relevant topics are identified, sentiment analysis using Natural Language Processing (NLP) is performed to assess the emotions and attitudes associated with each tweet. This involves categorizing the sentiment expressed in the tweets as positive, negative, or neutral. Furthermore, the sentiment analysis is refined by focusing on the topics related to the Russia-Ukraine war. The sentiment evaluation tools used are Affin and Bing, which allow for distinguishing sentiments through the use of lexical datasets [46]. Weekend data is excluded from the sentiment analysis to improve accuracy and clarity of results. This is because weekends tend to show significantly lower tweet activity from the target groups, which could distort the study’s findings.
Finally, network analysis is used to explore the connections and interactions between groups. By examining patterns of retweets, mentions, and replies among these entities, the analysis aims to identify influential actors based on original tweets and the most retweeted content. Additionally, it offers insights into the formation of alliances, collaboration, and the spread of sentiments among network participants.

3. Results

3.1. Content Analysis: Which Topics Are Prevalent before and after the War?

As mentioned, the proposed LDA model aims to assess variations in the discourse of interest groups before and after the war between Ukraine and Russia. The first step is to conduct the Griffiths & Steyvers [47] test, which allows for the establishment of a mathematical index of explainability of a discourse based on a number K of topics. In other words, it numerically evaluates the coherence, quality, and interpretability of a dataset through a reduced number of themes. The nature of this test necessitates the intervention of researchers to ensure the representativeness of the approach. After a systematic qualitative analysis of the different possible combinations, it is determined that the ideal number of topics to select is 20 (K=20), and the optimal number of descriptive terms for each topic is 10. This ensures a sufficiently broad representation of different discursive lines to determine temporal variations. Furthermore, the explainability index obtained in the Griffiths & Steyvers [47] test with K=20 exceeds 0.80, confirming the relevance and validity of the selection process.
Using the specified value of K and number of terms, the LDA model was run separately on tweets from before (N=19734) and after (N=15573) the Russian invasion of Ukraine on February 24th, 2022. Comparative analysis shows distinct shifts in the discourse of interest groups. Notably, Topic 18 in the post-invasion data shows a strong link to the conflict, with key words including “Ukraine”, “Russia”, “War”, and “Impact”. To confirm the relevance of these messages to the topic, two researchers and a third party reviewed them, ranking each based on its connection to the conflict as defined by the model. This review confirmed that a high percentage of the tweets were directly related to the conflict. A further analysis to pinpoint when the tweets shifted away from war-related topics revealed that the first 1,500 messages were predominantly about the war, and these were selected for subsequent sentiment analysis. The comparison of topics in the discourse in X of European interest groups before and after the outbreak of the conflict can be seen in Figure 1.

3.2. Sentiment Analysis: How Do Sentiments Differ before and after the Outbreak of the War?

According to the results obtained from the Bing algorithm, there was no statistically significant relationship between positive sentiment and time (standard error = 1.7926e-07, statistic = -0.146, p-value = 0.883). Similarly, the results from the Affin algorithm indicated a lack of statistical significance between positive sentiment and time (standard error = 3.90e-07, statistic = -0.005, p-value = 0.995). Therefore, the evidence does not support the conclusion that positive sentiments exhibit an increase or decrease over time.
In contrast, both the Bing and Affin algorithms yielded statistically significant results concerning the relationship between negative sentiment and time. The Bing algorithm estimated a sentiment coefficient of 5.479e-07 (standard error = 1.651e-07), with a statistic of 3.317 and a p-value of 0.001. This suggests that as time increases, negative sentiments tend to decrease. In a complementary manner, the Affin algorithm also shows a statistically significant sentiment coefficient of 6.775e-07 (standard error = 3.323e-07), with a statistic of 2.038 and a p-value of 0.042. These findings further support the notion that negative sentiments diminish as time progresses. In summary, there is a statistically significant drop in negative sentiments over time. The detailed information can be seen in Table 1.

3.3. Network Analysis: Who is Related to Whom? Who Are Important Users?

3.3.1. Original Messages

The top 10 most retweeted original messages from lobbying groups are posts from the following profiles: WithSecure Corporation (@withsecure), ExxonMobil Petroleum & Chemical (@exxonmobil), Planet Labs Germany (@planet), Volkswagen Group (@VWGroup), Elisa Oyj (@ElisaOyj), Suomen Osuuskauppojen Keskuskunta (@sryhma), Valio Oy (@ValioFi), KPN (@kpn), Vodafone Belgium (@VodafoneGroup), and A.P. Møller - Mærsk A/S (@Maersk). Table 2 shows the total number of retweets each message has received along with the text.
The top original message in terms of retweets, posted by WithSecure Corporation, received 584 retweets and announced the free availability of FSecure Freedome VPN in Ukraine. Similarly, a tweet from ExxonMobil, which received 489 retweets, shared the company’s official stance on the situation in Ukraine, further enhancing its reach and engagement. The remaining messages encompass various subjects, such as generic information about the Russian attack on Ukraine, the suspension of vehicle production in specific regions, solidarity with Ukraine, business closures in Russia, condemnation of the attack, the provision of free communication services to Ukrainian customers, and attempts to influence legislators.
To provide a more detailed understanding, Table 3 showcases a network analysis of the top 500 most retweeted original tweets from the European Union interest group community concerning the Russia-Ukraine war. This analysis assesses the centrality of users within the network by focusing on three metrics: degree centrality, betweenness centrality and eigenvector centrality. It is important to note that some users in the table have missing values. These gaps indicate either a lack of original tweets from those users in the analyzed network or insufficient data to accurately calculate their centrality measures.
In the context of original tweets, degree centrality measures the number of connections a user has based on their tweets. České Dráhy (@ceskedrahy_) shows a high degree centrality of 23, indicating a substantial network presence. Other users like Bank Gospodarstwa Krajowego (@BGK_pl) and Land en Tuinbouw Organisatie Nederland (@ltonederland) have moderate connectivity, with centrality scores of 8 and 7, respectively. Additionally, betweenness centrality gauges a user’s role as a conduit among other users in the network, influencing information flow. A higher value indicates greater influence. Thales Group (@thalesgroup), for instance, has a betweenness centrality of 62, playing a critical role in linking various users. Finally, eigenvector centrality evaluates a user’s significance based on both the quality and the quantity of their connections. High values indicate connections to other influential users. For example, Thales Group (@thalesemploi) registers an eigenvector centrality of 0.004, close to zero, suggesting minimal influential connections despite their central role.
The graphical representation of the network analysis of the original tweets can be seen in Figure 2.

3.3.2. Retweeted Messages

The top groups of interest in terms of retweets related to the conflict, from profiles inside or outside the network, include Hotelverband Deutschland (@hotellerie_de), Polish Chamber of Milk (@PolskaIzbaMleka), Gesamtverband der Arbeitgeberverbände der Metall- und El-ektro-Industrie (@MEArbeitgeber), Spitzenverband Fachärzte Deutschlands (@SpiFa_eV), and Polskie Zrzeszenie Producentów Bydła Mięsnego (WierzbickiJerzy). The information about the top 10 most retweeted retweets by these groups can be seen in Table 4.
The Hotelverband Deutschland (@hotellerie_de) group recorded the highest number of retweets related to the related to the Russia-Ukraine war, with 30 tweets that collectively garnered 395,019 retweets. Their most retweeted involvement featured a tweet with 137,624 retweets, highlighting a compelling speech by Patrick Moelleken, a Ukrainian actor and filmmaker. Furthermore, the group Polish Chamber of Milk (@PolskaIzbaMleka) retweet a tweet of 118,396 retweets. The tweet describes people marching through central Moscow, chanting “No to War”. The remaining groups of interest also contribute to the retweet activity, mentioning topics such as Russian liberation, peace rallies and demonstrations, legal actions, air-line bans, and troop movements.
Once again, to provide a more detailed view of the networks formed through interactions, a network analysis is conducted, focusing on the retweets of European interest groups rather than their original messages. This analysis covers the top 500 most retweeted retweets by these entities, regardless of whether the original poster is part of the network or not. Table 5 delves deeper into the three centrality measures.
In the assessment of degree centrality, Polskie Zrzeszenie Producentów Bydła Mięsnego (@WierzbickiJerzy) shows the highes value, indicating a network of retweet connections. Following closely is Hotelverband Deutschland (@hotellerie_de), which also demonstrates influence with a value of 30. This trend of leadership continues as Polskie Zrzeszenie Producentów Bydła Mięsnego also leads in betweenness centrality with a value of 1520. This confirms the organization’s function in facilitating the flow of information and connecting disparate users within the network, reinforcing its central role in network communications. Further solidifying its position at the nexus of the network, Polskie Zrzeszenie Producentów Bydła Mięsnego achieves the highest eigenvector centrality score. This indicates not only its connection to other influential users but also its influence over the network’s structure and reach.
The graphical representation of the network analysis of the retweets can be seen in Figure 3.

4. Discussion

In the first instance, using a LDA analysis, two discursive frameworks were identified in the tweets of the interest groups: one ten days before the invasion of Ukraine and another after the conflict began. The comparison reveals that the war influenced the digital communication agenda of these organizations. In addition to messages explicitly related to the conflict, there was a shift in other discursive themes, which began to focus mainly on issues such as energy and price increases, effects derived from the war. This influence of the war on communication strategies has been documented and verified in various studies [48,49,50]. These findings confirm the first two hypotheses of the research, demonstrating that European interest groups actively participate in this issue and that there is a change in the dominant discursive lines in their online communication.
Delving into the topic directly related to the war, it is noteworthy that the ten key words identified by the algorithm reveal that the majority of the included messages express their support for Ukraine against the Russian invasion. This finding is consistent with the prevailing argument in the scientific literature [4,14], which argues that interest groups incorporate external lobbying activities into their communication strategies not only to mobilize the masses but also to position themselves on current relevant issues. In this regard, the strategies employed by interest groups include actions aimed at amplifying external messages to mobilize support for protests, closures of Russian offices or productive sectors as a pressure tactic, and the promotion of solidarity activities in support of Ukraine. Additionally, efforts were made to intensify messages aimed at influencing policymakers and promoting the imposition of sanctions on the Russian government. These tactics align with findings from previous research on similar situations [11,12,13].
In terms of sentiment analysis, the third hypothesis of the research was confirmed: there was a decrease in negative sentiments in the 10 days following the invasion, as suggested by Sazzed [28]. Typically, the most negative messages appear in the early days of the conflict, indicating that interest groups took a more radical initial stance, which moderated as the event unfolded. Other research [51,52,53] also highlights the high level of extremism in messages on X about a topic in the initial days following its occurrence, especially considering the context of the event. In contrast, no correlation was observed between positive sentiments and the passage of time.
Finally, regarding the specific behavior of interest groups on the network, significant differences were observed between the original tweets issued by the groups and their participation in the amplification of messages through retweets. Original tweets generally included statements from companies and commercial organizations about their activities, while retweets tended to enhance support for Ukrainians in various aspects.
The most influential entities inside the original tweets network were those that used the platform for promoting coordination among various interest groups. These organizations provided mutual support, fostering a collaborative environment. Several companies were specifically identified as employing this strategic approach to communication.
Contrastingly, when examining the retweet network, a different picture emerged. There was a noticeable lack of clear coordination efforts for amplifying messages, whether those messages originated within the network or came from external sources. This discrepancy confirms the fourth hypothesis and suggests that while original content from certain influential companies focused on unity and mutual aid, the broader retweeting behavior did not reflect a unified strategy to enhance message dissemination.

5. Conclusions

This study enhances knowledge of digital communication in outside lobbying strategies by analyzing how interest groups adapt their messaging on X in response to geo-political events like the Russia-Ukraine conflict. Researchers can utilize the insights from this study to develop a deeper understanding of crisis communication, specifically war situations strategies, within social networks.
Specifying the explanatory potential of the techniques used in this study, LDA analysis provides information on the evolution of public discourse and event-driven conversations on social network X. Similarly, sentiment analysis is used to assess organizational responses to the conflict, while network analysis helps identify dynamics of influence, highlighting which entities have more impact and influence within the network. By integrating these techniques and aligning them with the stated objectives, it is possible to contribute to understanding how interest groups shape public discourse on social networks and lay a foundation for exploring the implications this has for policy development.
In this regard, it is important to note that a significant limitation of this study is that the evolution of discursive frameworks analyzed cannot be considered longitudinal. Although it covers a broad period before and after the start of the war, which serves as a turning point for comparisons, future research could involve analyzing discursive development using other events as comparative markers. This could also deepen the observed trend of a decrease in messages with negative sentiments. On the other hand, studying the impact of the conflict in areas such as energy policies and price increases through case studies could provide a more detailed view of these issues, since, as mentioned, they do not directly condition the discourse about the war; but they do mark important elements in agendas caused as an indirect consequence of the conflict itself.
Closely related to the previous limitation, it should be considered that this research focuses on understanding the influence of interest groups during conflicts, primarily focusing on the short-term effects, which may overlook the broader and long-term impact of the conflict. Although it analyzes how these groups coordinate their actions, there is a risk of not fully capturing the extent of their influence. To overcome this limitation, it is suggested to conduct research focused directly on the effects of these communications, beyond their form and the connections and dependencies that exist between the senders. It would also be valuable to explore the differences in influence based on the alliances and communication strategies employed.

Supplementary Materials

Not applicable.

Author Contributions

Conceptualization, A.G.-C., Á.S.-O. and A.M.-C.; methodology, A.G.-C., Á.S.-O., A.M.-C., A.A.-M. and A.C.-E.; software, A.G.-C.; validation, A.G.-C., Á.S.-O., A.M.-C., A.A.-M. and A.C.-E.; formal analysis, A.G.-C., Á.S.-O. and A.M.-C.; investigation, A.G.-C., Á.S.-O., A.M.-C., A.A.-M. and A.C.-E.; resources, A.M.-C., A.A.-M. and A.C.-E.; data curation, A.G.-C. and Á.S.-O.; writing—original draft preparation, Á.S.-O., A.M.-C., A.A.-M. and A.C.-E.; writing—review and editing, Á.S.-O., A.M.-C., A.A.-M. and A.C.-E.; visualization, A.G.-C., A.A.-M. and A.C.-E.; supervision, A.A.-M. and A.C.-E.; project administration, A.A.-M. and A.C.-E.; funding acquisition, A.M.-C., A.A.-M. and A.C.-E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Lobby y Comunicación en la Unión Europea” of the Ministry of Science and Innovation (Spain). The State R&D&I Programme for Proofs of Concept of the State Programme for Societal Challenges, State Programme for Scientific, Technical and Innovation Research, 2020–2023 (PID2020-118584RB-100).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Databases of the study can be consulted in Castillo-Esparcia, Almansa-Martínez & Gorostiza-Cerviño [54] https://doi.org/10.7910/DVN/HDEFGL

Acknowledgments

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kanol, D.; Nat, M. Group Type and Social Media Engagement Strategies in the EU: The Case of British Interest Groups on Facebook. J. Public Nonprofit Aff. 2021, 7, 205–219. [Google Scholar] [CrossRef]
  2. Kollman, K. Outside lobbying: Public opinion and interest group strategies, 1st ed.; Princeton University Press: Princeton: United States, 1998. [Google Scholar]
  3. Weiler, F.; Brändli, M. Inside versus outside lobbying: How the institutional framework shapes the lobbying behaviour of interest groups. Eur. J. Political Res. 2015, 54, 745–766. [Google Scholar] [CrossRef]
  4. De Bruycker, I.; Beyers, J. Lobbying strategies and success: Inside and outside lobbying in European Union legislative politics. Eur. Polit. Sci. Rev. 2019, 11, 57–74. [Google Scholar] [CrossRef]
  5. Della Porta, D.; Diani, M. Social movements: An introduction, 2nd ed.; Blackwell Publishing: Oxford, United Kingdom, 2006. [Google Scholar]
  6. Beyers, J. Voice and access: Political practices of European interest associations. Eur. Union Polit. 2004, 5, 211–240. [Google Scholar] [CrossRef]
  7. Chalmers, A.W. Trading information for access: Informational lobbying strategies and interest group access to the European Union. J. Eur. Public Policy 2013, 20, 39–58. [Google Scholar] [CrossRef]
  8. Smith, M.A. American business and political power: Public opinion, elections, and democracy, 1st ed.; The University of Chicago Press: Chicago, United States, 2000. [Google Scholar] [CrossRef]
  9. Dür, A.; Mateo, G. Lobbying in the face of politicisation: Interest group strategies in trade policy. J. Eur. Public Policy 2023, 31, 1–27. [Google Scholar] [CrossRef] [PubMed]
  10. Dür, A.; Mateo, G. Insiders versus outsiders: Interest group politics in multilevel Europe, 1st ed.; Oxford University Press: Oxford: United Kingdom, 2016. [Google Scholar] [CrossRef]
  11. Culpepper, P.D. Quiet Politics and Business Power: Corporate Control in Europe and Japan, 1st ed.; Cambridge University Press: New York, United States, 2011. [Google Scholar] [CrossRef]
  12. Hanegraaff, M.; Beyers, J.A.; De Bruycker, I. Balancing inside and outside lobbying: The political strategies of lobbyists at global diplomatic conferences. Eur. J. Political Res. 2016, 55, 568–588. [Google Scholar] [CrossRef]
  13. Zürn, M.; Binder, M.; Ecker-Ehrhardt, M. International authority and its politicization. Int. Theory 2012, 4, 69–106. [Google Scholar] [CrossRef]
  14. Brown, H. Does globalization drive interest group strategy? A cross-national study of outside lobbying and social media. J. Public Aff. 2016, 16, 294–302. [Google Scholar] [CrossRef]
  15. Castillo-Esparcia, A.; Caro-Castaño, L.; Almansa-Martínez, A. Evolution of digital activism on social media: Opportunities and challenges. Prof. Inf. 2023, 32, e320303. [Google Scholar] [CrossRef]
  16. Gheyle, N.; De Ville, F. Outside lobbying and the politicization of the transatlantic trade and investment partnership. In Lobbying in the European Union Lobbying: Strategies, Dynamics and Trends, 1st ed.; Dialer, D., Richter, M., Eds.; Springer: Cham, Switzerland, 2019; pp. 339–354. [Google Scholar] [CrossRef]
  17. De Cock, C. iLobby.eu: survival guide to EU lobbying, including the use of social media, 1st ed.; Eburon Academic Publishers: Delft, Netherlands, 2010. [Google Scholar]
  18. Widner, K.; MacDonald, M.; Gunderson, A. Lobbying Inside (and) Out: Interest Group Behavior on Social Media. Politics Int. Relations 2020. [Preprint]. [Google Scholar] [CrossRef]
  19. Chalmers, A.W.; Shotton, P.A. Changing the face of advocacy? Explaining interest organizations’ use of social media strategies. Polit. Commun. 2016, 33, 374–391. [Google Scholar] [CrossRef]
  20. Van der Graaf, A.; Otjes, S.; Rasmussen, A. Weapon of the weak? The social media landscape of interest groups. Eur. J. Commun. 2016, 31, 120–135. [Google Scholar] [CrossRef]
  21. Halpin, D.R.; Fraussen, B.; Ackland, R. Which Audiences Engage With Advocacy Groups on Twitter? Explaining the Online Engagement of Elite, Peer, and Mass Audiences With Advocacy Groups. Nonprofit Volunt. Sect. Q. 2021, 50, 842–865. [Google Scholar] [CrossRef]
  22. Grose, C.R.; Lopez, P.; Sadhwani, S.; Yoshinaka, A. Social Lobbying. J. Politics 2022, 84, 367–382. [Google Scholar] [CrossRef]
  23. Ilhem, G. Social Media: New Form of Lobbying. Rev. Eur. Droit Soc. 2022, 55, 34–55. [Google Scholar] [CrossRef]
  24. Popiel, P. The tech lobby: Tracing the contours of new media elite lobbying power. Commun. Cult. Critique 2018, 11, 566–585. [Google Scholar] [CrossRef]
  25. Fung, Y.R.; Ji, H. A Weibo Dataset for the 2022 Russo-Ukrainian Crisis. arXiv 2022, arXiv:2203.05967. [Google Scholar]
  26. Park, C.Y.; Mendelsohn, J.; Field, A.; Tsvetkov, Y. Challenges and opportunities in information manipulation detection: An examination of wartime Russian media. Findings Assoc. Computational Linguistics: EMNLP 2022, 5209–5235. [Google Scholar] [CrossRef]
  27. Pohl, J.S.; Seiler, M.V.; Assenmacher, D.; Grimme, C. A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022. SSRN Electron. J. 2022, 1–4. [Google Scholar] [CrossRef]
  28. Sazzed, S. The Dynamics of Ukraine-Russian Conflict through the Lens of Demographically Diverse Twitter Data. In 2022 IEEE International Conference on Big Data, Osaka, Japan, 2022. [CrossRef]
  29. Cetin, U.; Gundogmus, Y.E. A Glimpse to the Digital Social Universe in the Times of War. In 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey, 2022. [CrossRef]
  30. Chen, E.; Ferrara, E. Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia. arXiv 2022, arXiv:2203.07488. [Google Scholar] [CrossRef]
  31. Nisch, S. Invasion of Ukraine: Frames and sentiments in Zelensky’s Twitter communication. J. Contemp. Eur. Stud. 2023, 32, 110–124. [Google Scholar] [CrossRef]
  32. European Union Transparency Register. Available online: https://transparency-register.europa.eu (accessed on 2024).
  33. Wickham, H. rvest: Easily harvest (scrape) web pages [Manual]. CRAN 2022, https://cran.r-project.org/web/packages/rvest/index.html.
  34. Kearney, M. rtweet: Collecting and analyzing Twitter data. J. Open Source Softw. 2019, 4, 1829. [Google Scholar] [CrossRef]
  35. Alexa, M. Computer-assisted text analysis methodology in the social sciences. ZUMA 1997, 97, 1–40. https://www.ssoar.info/ssoar/handle/document/20084.
  36. Lucas, C.; Nielsen, R.A.; Roberts, M.E.; Stewart, B.M.; Storer, A.; Tingley, D. Computer-Assisted Text Analysis for Comparative Politics. Political Anal. 2015, 23, 254–277. [Google Scholar] [CrossRef]
  37. Pang, B.; Lee, L. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retrieval 2008, 2, 1–135. [Google Scholar] [CrossRef]
  38. Ullah, A.; Khan, S.N.; Nawi, N.M. Review on sentiment analysis for text classification techniques from 2010 to 2021. Multimed. Tools Appl. 2023, 82, 8137–8193. [Google Scholar] [CrossRef]
  39. Lucas, C.; Tingley, D. translateR: Bindings for the google and Microsoft Translation APIs. R package, 1.0 [Manual]. RDRR 2014, https://rdrr.io/cran/translateR/.
  40. Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent Dirichlet Allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. [Google Scholar] [CrossRef]
  41. Chen, Y.; Peng, Z.; Kim, S.H.; Choi, C.W. What we can do and cannot do with topic modeling: A systematic review. Commun. Methods Meas. 2023, 17, 111–130. [Google Scholar] [CrossRef]
  42. Grimmer, J.; Roberts, M.E.; Stewart, B.M. Text as data: A new framework for machine learning and the social sciences, 1st ed.; Princeton University Press: Princeton: United States, 2022. [Google Scholar]
  43. Maier, D.; Waldherr, A.; Miltner, P.; Wiedemann, G.; Niekler, A.; Keinert, A.; Pfetsch, B.; Heyer, G.; Reber, U.; Häussler, T.; Schmid-Petri, H.; Adam, S. Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Commun. Methods Meas. 2018, 12, 93–118. [Google Scholar] [CrossRef]
  44. Blei, D.M.; Lafferty, J.D. A correlated topic model of Science. Ann. Appl. Stat. 2007, 1, 17–35. [Google Scholar] [CrossRef]
  45. Grün, B.; Hornik, K. Topicmodels: An R Package for Fitting Topic Models. J. Stat. Softw. 2011, 40, 1–30. [Google Scholar] [CrossRef]
  46. Liu, B. Sentiment analysis: Mining opinions, sentiments, and emotions, 2nd ed.; Cambridge University Press: Cambridge, United States, 2020. [Google Scholar] [CrossRef]
  47. Griffiths, T.L.; Steyvers, M. Finding scientific topics. Proc. Natl. Acad. Sci. 2004, 101, 5228–5235. [Google Scholar] [CrossRef]
  48. Aviv, I.; Ferri, U. Russian-Ukraine armed conflict: Lessons learned on the digital ecosystem. Int. J. Crit. Infrastruct. Prot. 2023, 43, 100637. [Google Scholar] [CrossRef]
  49. Diesen, G. The Think Tank Racket: Managing the Information War with Russia, 1st ed.; SCB Distributors: Atlanta: United States, 2023. [Google Scholar]
  50. Jungblut, M.; Kümpel, A.S.; Peter, C.; Wulf, T. The Russian invasion of Ukraine in modern information environments: content, consumers, and consequences of digital conflict communication. Front. Polit. Sci. 2023, 5, 1227005. [Google Scholar] [CrossRef]
  51. Hong, S.; Kim, S.H. Political polarization on twitter: Implications for the use of social media in digital governments. Gov. Inf. Q. 2016, 33, 777–782. [Google Scholar] [CrossRef]
  52. Parmelee, J.H. The agenda-building function of political tweets. New Media Soc. 2014, 16, 434–450. [Google Scholar] [CrossRef]
  53. Urman, A. Context matters: political polarization on Twitter from a comparative perspective. Media Cult. Soc. 2020, 42, 857–879. [Google Scholar] [CrossRef]
  54. Castillo-Esparcia, A; Almansa-Martínez, A.; Gorostiza-Cerviño, A. Replication data for: Examining the Roles, Sentiments, and Discourse of European Interest Groups in the Ukrainian War through X (Twitter). Harvard Dataverse 2024. [Google Scholar] [CrossRef]
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Figure 1. (a) LDA topics representation for tweets before the Russia-Ukraine war; (b) LDA topics representation for tweets after the Russia-Ukraine war.
Figure 1. (a) LDA topics representation for tweets before the Russia-Ukraine war; (b) LDA topics representation for tweets after the Russia-Ukraine war.
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Figure 2. Network graph of the top 500 most retweeted original tweets by European group of interest (Fruchterman-Reingold algorithm).
Figure 2. Network graph of the top 500 most retweeted original tweets by European group of interest (Fruchterman-Reingold algorithm).
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Figure 3. Network graph of the top 500 most retweeted retweets by European group of interest (Fruchterman-Reingold algorithm).
Figure 3. Network graph of the top 500 most retweeted retweets by European group of interest (Fruchterman-Reingold algorithm).
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Table 1. Sentiment analysis of tweets during the Russia-Ukraine conflict: Multiple Linear Regression (MLR) using Affin and Bing algorithms (February 24th - March 6th, 2022).
Table 1. Sentiment analysis of tweets during the Russia-Ukraine conflict: Multiple Linear Regression (MLR) using Affin and Bing algorithms (February 24th - March 6th, 2022).
Term estimate standard error statistic p-value*
Positive Bing -2.626350e-08 1.792441e-07 -0.146523613 0.883
Positive Affin -2.016360e-09 3.908429e-07 -0.005159005 0.995
Negative Bing 5.479683e-07 1.651721e-07 3.317559707 0.001**
Negative Affin 6.775845e-07 3.323500e-07 2.038767728 0.042*
* (* p < .05, ** p < .01, *** p < .001).
Table 2. Most retweeted original posts from interest groups following the outbreak of the Russia-Ukraine war (February 24th, 2022 - March 6th, 2022).
Table 2. Most retweeted original posts from interest groups following the outbreak of the Russia-Ukraine war (February 24th, 2022 - March 6th, 2022).
X username Retweets Original tweet text*
withsecure 584 F-Secure FREEDOME VPN is now available for free in all of Ukraine. Protect your online privacy. Download from the link above.
exxonmobil 489 We issued the following statement regarding the situation in Ukraine today.
planet 378 Latest from Chuhuiv Airbase in Ukraine Imagery captured on February 21 and today February 24 2022.
VWGroup 324 Against the background of the Russian attack on has decided to stop the production of vehicles in Kaluga Nizhny Novgorod until further notice. Vehicle exports to Russia will also be stopped with immediate effect.
ElisaOyj 273 We want to show our support for Ukraine and we have decided to close the Russian Today channel.
sryhma 206 SOK has decided to give up business in Russia. SOK has 16 prisms in St Petersburg and three hotels and about 1000 employees. Business shutdown has begun.
ValioFi 191 We condemn the Russian attack on Ukraine and hope that the situation will stabilize as soon as possible. We immediately stop export from Finland to Russia.
kpn 185 The situation in Ukraine is dear to us. That is why we will not charge any costs for mobile calling and texting until the end of March calling and SMS and fixing it. We also make roaming calling data and SMS free of charge for our customers who are in Ukraine.
VodafoneGroup 176 A statement on the situation in Ukraine.
Maerks 139 We are deeply concerned by the crisis in Ukraine. We closely follow government posing new sanctions on Russia impacting operations from direct amp indirect restrictions. New Maersk bookings from Russia will be temporarily suspended except food, medical and humanitarian supplies.
* The text of the tweets are translations provided by the Google API.
Table 3. Network metrics for original tweets by European interest groups related to the Russia-Ukraine war, using Fruchterman-Reingold algorithm (February 24th - March 6th 2022).
Table 3. Network metrics for original tweets by European interest groups related to the Russia-Ukraine war, using Fruchterman-Reingold algorithm (February 24th - March 6th 2022).
X username Degree centrality Betweenness centrality Eigenvector centrality
ceskedrahy_ 24 770 0.0045
BGK pl 8 621 NA
ZPPnetpl 7 126 NA
Itonederland 7 NA NA
UNESID 7 976 0.0088
Elinkcinoelama 6 112 NA
dzbank 6 NA NA
withsecure 5 NA NA
Metinvest_group 5 NA NA
bdzvpresse NA 240 NA
Meta NA 174 NA
vnoncw NA 173 NA
ZDH news NA 144 NA
CDCargo NA NA 0.0131
Sjaak VanDerTak NA NA 0.0088
spcr NA NA 0.0088
BusinessEurope NA NA 0.0088
mapagob NA NA 0.0066
efpia NA NA 0.0044
TechFinland NA NA 0.0044
Table 4. Most retweeted retweets posts from interest groups following the outbreak of the Russia-Ukraine war (February 24th, 2022 - March 6th, 2022).
Table 4. Most retweeted retweets posts from interest groups following the outbreak of the Russia-Ukraine war (February 24th, 2022 - March 6th, 2022).
X username Original tweet text* Retweets Original tweet X username
hotellerie _de @ZelenskyyUa’s TV address to the Russian people might be the most moving speech that I’ve ever seen in my entire life. The whole world needs to see, understand and share this crucial Ukrainian message. 137,624 PMoelleken
hotellerie _de Starlink service is now active in Ukraine. More terminals en route. 130,671 elonmusk
PolskalzbaMleka People marching through central Moscow this evening chanting “No to War!”. 118,396 mjluxmoore
hotellerie _de 1/12 We - Russia - want to be a nation of peace. Alas, few people would call us that now. 29,093 navalny
hotellerie _de Bloody hell. Looking at a message from the Ukraine Library Association concerning the cancellation of their forthcoming conference. It basically says “We will reschedule just as soon as we have finished vanquishing our invaders”. Ukrainian Librarians, I salute you. 27,019 NickPoolel
MEArbeitgeber Russia’s “liberation”. 24,624 LukeDCoffey
SpiFa_eV More than 100,000 people attended a peace rally and demonstration in Berlin on Sunday in support of Ukraine. Organizers, which included peace and environmental groups, unions and churches, had expected 20,000 to gather. 19,065 nytimes
WierzbickiJerzy Ukraine has officially filed a lawsuit against the Russian Federation to the International Court of Justice in the Hague. 18,806 ZelenskyyUa
hotellerie _de Estonia is banning Russian airlines from our airspace. We invite all EU countries to do the same. There is no place for planes of the aggressor state in democratic skies. 18,280 kajakallas
WierzbickiJerzy According to Putin’s plan, Belarus’ troops had to enter Ukraine a week ago. But something went wrong. Some officers resigned, some fled Belarus and contacted us. Conscripts are massively fleeing. Apparently, some generals opposed the participation of Belarus in the war. 15,685 franakviacorka
* The text of the tweets are translations provided by the Google API.
Table 5. Network metrics for retweets by European interest groups related to the Russia-Ukraine war, using Fruchterman-Reingold algorithm (February 24th - March 6th 2022).
Table 5. Network metrics for retweets by European interest groups related to the Russia-Ukraine war, using Fruchterman-Reingold algorithm (February 24th - March 6th 2022).
X username Degree centrality Betweenness centrality Eigenvector centrality
WierzbickiJerzy 34 1520 1.0001
hotellerie_de elpia 30 1515 0.9111
efpia 14 730 0.1991
danske research 14 NA 0.0999
PolskalzbaMleka 12 1305 0.2000
jnaervig 12 NA NA
grupa pfr 11 1535 0.0888
MVFP_Presse 8 778 NA
ECG_Association 8 NA 0.0707
belzypresse 7 573 NA
DanskIndustri NA 648 NA
GrainClub NA 502 NA
FinanceLatvia NA 474 NA
ecosia NA NA 0.1888
ASCER_comunica NA NA 0.1322
Semantic Visions NA NA 0.1222
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