Global analysis of Twitter communication in corporate social responsibility area: sustainability, climate change, and waste management

Introduction

Corporate social responsibility (CSR) initiatives have become a priority for business organisations around the world in recent years (Chae & Park, 2018; Suttipun et al., 2021; Wang et al., 2016), as a result of increased competition among businesses to obtain customers by building their image in society through socially responsible activities such as support of employee’s well-being, charity’s programs, cooperation with the local community, or environmental initiatives (Rizwana, Atif & Cheema, 2012; Tan, Rasoolimanesh & Manickam, 2022; Yang, Ngai & Lu, 2020).

As the global emphasis on economic and environmental sustainability grows, firms are compelled to declare their corporate social responsibility (Chen, Hung & Wang, 2018; Nguyen, Nguyen & Hoai, 2021). Regulators, investors, and other stakeholders, particularly in emerging economies, are interested in this trend toward mandating CSR disclosure (Wu, Zhang & Wei, 2021).

The exponential growth of social media (SM) has evolved in a transformation in the communication environment in which stakeholders as users are able to publish and share their opinions about businesses and their products (D’Andrea et al., 2019). This is a particularly important activity as recent studies have shown the benefits of CSR communication with stakeholders (Bartikowski & Berens, 2021; Husnaini, Sasanti & Cahyaningtyas, 2018; Viererbl & Koch, 2022; Withisuphakorn, 2018). This means that the board of directors, attorneys, the HR department, sales department, customer service department, marketing and PR department, and other divisions can use social media in practically every aspect of an organisation’s operation (Edyta, Rizun & Paulina, 2016). In the context of the increasing popularity of social media, which now has over 4.6 billion users, social media presents significant conceptual and theoretical challenges to a CSR monitoring and reporting literature that has previously mostly concentrated on one-way, yearly communication. Disclosure with CSR reports has been impacted by social media, which provides a more dynamic, participatory public forum (Neu et al., 2020).

Twitter is one of the most popular social networking platforms, used by 329 million monthly active users worldwide (Dixon, 2022). The rise in popularity of Twitter, as well as people’s acceptance of it, has created an opportunity for more research on this social media medium (Manuel, 2015). In the last decade, Twitter has established itself as an essential research platform, which has been used in more than ten thousand research articles (Antonakaki, Fragopoulou & Ioannidis, 2021), because Twitter allows for obtaining a large amount of real-time textual data of public opinion on the monitored issue (Gaytan Camarillo et al., 2021), tracking trends (Vargas et al., 2021), and analysing sentiment (Park, Choi & Jung, 2022). In the field of CSR, Twitter was used as a data source, for example, in a study by Dong & Rim (2019), which identified and interpreted the non-profit organisations communication strategies used when they are communicating on Twitter about their CSR collaborations; a study by Okazaki et al. (2020) analysed the level of interaction in eight brands’ CSR Twitter dialogues and investigated the reason for the observed level of interaction; or in the study by Ali, Frynas & Mahmood (2017), where the author identified the determinants of corporate social media accounts for CSR disclosures. However, none of the previous research dealt with the topic structure of CSR and the sentiment that the communicated topics evoke. Similarly, previous studies have yet to deal with trends in CSR communication on Twitter. Based on these findings, the aim of this study is to investigate the use of hashtags, the topic structure, sentiments, and trends in corporate social responsibility (CSR)-related discussions on Twitter.

To achieve the research aim, this study will address the following research questions:

1. What are the most frequently used hashtags in tweets related to CSR?

2. What is the topic structure of CSR-related discussions on Twitter?

3. What sentiments are expressed in CSR-related topics on Twitter?

4. What are the trends in connection with CSR communicated on Twitter?

This study used a social media data analysis with a focus on hashtags and tweets to identify key characteristics of CSR communication on the Twitter social network worldwide. This article contributes to the literature in several ways. First, some previous studies have focused on the social media data analysis of CSR communication at the national (Amin, Mohamed & Elragal, 2021), or analysing selected companies (Maiorescu-Murphy, 2022; Okazaki et al., 2020); this article offers a comprehensive view and offers new findings and insights into CSR communication on Twitter worldwide. When examining the detection of characteristics of CSR communication on Twitter, the article uses an automated machine learning approach to automatically analyse content in tweets instead of using the manual coding techniques commonly used in mainstream CSR communication research. From a methodological point of view, this article represents a new research approach to the analysis of CSR communication on Twitter. The study also expands the current knowledge by identifying dominant topics communicated on Twitter, identifying the sentiment that the communicated topics evoke, and identifying the trend of individual hashtags in connection with the hashtag #CSR independently of the trend of the entire topic #CSR.

Materials and Methods

The data analysis was based on the extended SMAHR framework (Pilař et al., 2021). The data analysis process based on SMAHR framework consisted of four steps (Fig. 1):

1. Data acquisition:

   The aim of this step was to collect data from the Twitter social network that contain the hashtag #csr on the Twitter social network from January 1st, 2017 to April 30th, 2022, using the Twitter API.

   The Twitter API was used to obtain the data (Platform Developer, 2022).

   Tweets were downloaded from the Twitter social network using the Twitter API v2 (Platform Developer, 2022). The Tractor software was used for this purpose (Tractor, 2023). The software downloaded tweets that included the hashtag #csr. During that timeframe, 520,638 Tweets of 168,134 unique Twitter users were recorder. This dataset includes all tweets with the hashtag #csr that users posted to the Twitter social network during the observation period.

2. Data filtering: In the next stage, it was necessary to filter messages that referred to the topic of corporate social responsibility. An example is the computer game “CSR Racing”. Filtering was perform manually using the MS Excel program via the content filtering function.

3. Data mining:

   The aim of this step is to extract useful and relevant information from the vast amounts of data generated by users on social media platforms.

   The network was described using the techniques listed below:

   1. Frequency: A metric called frequency describes how frequently a hashtag appears in a network. The aim of this step was to identify the most used hashtags. Through Hashtag extractor software (Pilař et al., 2021), all hashtags were extracted from individual Tweets. The calculation of the number of individual hashtags was performed by importing into Gephi 0.9.2 software (Bastian, Heymann & Jacomy, 2009).

   2. Topic analysis:

      Topic analysis is a method used to identify the main topics or themes that are being communicated within a large dataset, such as social media posts. In complex networks, such as social media networks, some nodes (i.e., hashtags or words) are more interconnected with each other than with the rest of the network. It is possible to identify topics based on clusters of individual hashtags.

      The aim of this step was to identify topic structure of CSR-related discussions on Twitter. Compared to frequency analysis, topic analysis was created based on whole tweets (not only hashtags). For topic analysis, Graphext software was used (Graphext, 2023). To analyse the community structure of our network, Graphext utilized a modified version of the Louvain algorithm (Blondel et al., 2008). The network was created based on the interconnectedness of individual words in the Tweet. The Louvain algorithm employs an iterative process of allocating nodes to clusters with the aim of optimizing a performance metric known as modularity. This metric gauges the relative density of edges within clusters compared to those between clusters. The number of distinct communities in the dataset was calculated as follows:

      $$\Delta Q=\left[\frac{{\displaystyle {\sum }_{in}^{}+}2k_{i,in}}{2m}-{\left(\frac{{\displaystyle {\sum }_{tot}^{}+}{k}_i}{2m}\right)}^2\right]-\left[\frac{{{\displaystyle \sum }}_{in}^{}}{2m}-{\left(\frac{{{\displaystyle \sum }}_{tot}^{}}{2m}\right)}^2\right],$$where ${\sum }_{in}$ is the sum of weighted links inside the community, ${\sum }_{tot}$ is the total number of weighted connections inside the community, k_i is the total number of weighted links related to community hashtags, and i, k_i,in is the total weighted linkages from an individual to community hashtags, and m is the normalization factor, calculated as the total weighted links over the entire graph (Blondel et al., 2008).

   3. Visual analysis: Network visualization techniques such as force-directed layouts can be used to highlight different aspects of a network, such as the density of connections, or polarization of topics. The aim of this step was to identify the polarity of the identified topics.Based on the ForceAtlas2 layout technique, a two-dimensional graph was generated for visual analysis. An improved version of the ForceAtlas algorithm called ForceAtlas2 focuses on massive networks was used. This approach uses visual representations of smaller samples to identify network communities’ intercommunity connections (Jacomy et al., 2014). Visual analysis was created using Graphext software (Graphext, 2023).

   4. Sentiment analysis: The purpose of sentiment analysis on Twitter social media is to determine the general attitude or opinion expressed in tweets about a particular topic. Sentiment analysis is a process of categorizing text into positive, negative, or neutral sentiment based on the context and tone of the language used.

      The aim of this step was to identify sentiments, which are expressed in CSR-related topics on Twitter.

      In this research, VADER (Valence Aware Dictionary and Sentiment Reasoner) was used by sentiment analysis. VADER is a Lexicon and Rule-Based Sentiment Analysis Tool (Hutto & Gilbert, 2014).

   5. Trend analysis: The purpose of trend analysis is to identify and understand patterns and changes over time. The aim of this step was to identify the trends in connection with CSR communicated on Twitter via hashtags trend analysis. For trend analysis was used SPSS 21 software. In the field of trend analysis, three following steps were used:

      1. Tests for trends in time series: There was assessed general development tendency of all-time series through the linear trend function. Along with it there was developed the null hypothesis, meaning that the slope measured by regression coefficient is not statistically significant. To verify the reject or fail to reject of the null hypothesis, the t-test was used. Decisions are made by comparing the maximum first type error (the p-value), based on our data, and the first type error of alpha (Tufféry, 2011).

      2. Cluster analysis: Time-series clustering is a process that can help us to understand the pattern in fluctuating and large time series data (Tufféry, 2011). Time series clustering is an unsupervised learning problem similar to the clustering in other data and variable types, but we also take the time variable into account (Murtagh & Legendre, 2014). We mainly perform time series clustering to minimize the data similarity across the clusters and increase the similarity within the cluster. We used agglomerative clustering in our analysis, because we explore the time series, and the number of clusters is not known. We prepared the time series for analysis, checked multicollinearity, and standardized all input values using z-score. Then we calculated the similarity using Euclidian distance and we employed Ward’s method as the clustering algorithm. The homogeneity of clusters was measured by R-square and Semipartial R-square (Tufféry, 2011). R-square is the proportion of the sum of squares explained by the clusters (between-cluster sum of squares/total sum of squares). The nearer it is to 1, the better the clustering will be. Semipartial R-square measures the loss of the between-cluster sum of squares caused by grouping two clusters together (Tufféry, 2011). Thus, the value should be small to imply that we are merging two homogeneous groups.

      3. Chow test: We examined whether two parts of the time series show a different slope. A method commonly method used for this is the Chow test (Binkley & Young, 2020), which tests for group effects by comparing the error sum of squares (ESS) from regressions on the individual time series to the ESS from a pooled regression using an F-test.

         Consider a standard k-variable regression model $Y=\alpha +\mathbf{X}\beta$ (Binkley & Young, 2020), where e is the usual error term and k includes an intercept. Data is available from two distinct parts, the first and the second section of the time series. Let us denote the groups as A and B, the interest is in whether the same equation applies to both. There we test the Chow test null hypothesis $\beta A=\beta B$. The Chow test statistic is as follows (Chow, 1960 in Binkley & Young, 2020):

         $$F={\displaystyle \frac{ES{S}_P\left(ES{S}_A-ES{S}_B\right)}{ES{S}_A+ES{S}_B}\times {\displaystyle \frac{n_A+n_B-2k}{k}}}$$where the ESS’s are the error sum of squares from the regressions. The statistic has an F-distribution with $k$ and $nA+nB-2k$ degrees of freedom. The null hypothesis tested assumes equal regression coefficients.

4. Knowledge representation: Knowledge represenation is a procedure that uses visualization tools to explain the findings of data mining. Knowledge representation highlights the synthesis of individual values and outputs from the phase of data evaluation. The aim of this step is to highlight important findings of previous analyses.

Results and discussion

First of all, data from the period 2017–2022 was used to analyse the frequency of hashtags in relation to the #CSR hashtag. The hashtags most posted in connection with #CSR are #sustainability, #esg, #green and #sdgs—see Table 1. According to many authors (Księżak & Fischbach, 2018; Pan, Sinha & Chen, 2021; Varyash et al., 2020), CSR is based on the Triple Bottom Line concept, according to which a business is based on three pillars—profit, people, planet (Uadiale & Fagbemi, 2012), i.e., it bears economic, social, and environmental responsibility for its activities (Braccini & Margherita, 2018). The results of the hashtag frequency analysis on Twitter show that environment-related hashtags are the most common. The top 30 most frequent hashtags in CSR communication include #sustainability, #green, #sdgs, #sustainable, #environment and #climatechange.

The World Commission on Environment and Development (United Nations, 1987) defines sustainability as utilizing resources to meet the ‘needs of the present without compromising future generations’ ability to meet their own needs’. The close link between CSR and sustainability has long been confirmed by numerous studies (Carroll & Shabana, 2010; Christensen, Hail & Leuz, 2021; Kang et al., 2015; Meseguer-Sánchez et al., 2021). A bibliometric analysis from 2003–2021 conducted by Sánchez-Teba et al. (2021) found that the main topics driving CSR research between 2003 and 2021 were sustainability and the environment. The strong link between the concepts is particularly clear in Europe, where for the European Union CSR is the basic means of supporting sustainable development (Yıldız & Ozerim, 2014).

Environmental, social and governance (ESG) can be seen as the three non-financial dimensions of business, which take into account the impact on the environment, respect for social values and the quality of company management (DeGennaro & Barry, 2020). ESG developed from the CSR concept (MacNeil & Esser, 2022) and represents an evaluable result regarding a company’s overall sustainability performance (Polley, 2022). ESG offers quantitative metrics and thus enables a shift away from qualitative descriptions of CSR (Cini & Ricci, 2018). The importance of ESG has risen particularly in recent years, in connection with the new EU directive on corporate sustainability reporting, which from 2024 obliges enterprises with 250 or more employees to report non-financial information (European Commission, 2021), thus greatly increasing the number of businesses that will be obliged to file non-financial reports.

The concepts of CSR, ESG and sustainability have much in common; certain (Fatemi, Glaum & Kaiser, 2018; Gillan, Koch & Starks, 2021) even consider them to be equivalent. However, despite their apparent similarity, these are separate concepts with fundamental differences (MacNeil & Esser, 2022).

The hashtag #SDGS, which analysis has shown to be the 5th most frequently posted hashtag in connection with CSR, refers to the 17 Sustainable Development Goals adopted by all UN Member States in 2015. SDGs represent a plan to protect the environment, eradicate extreme poverty, and combat injustice and inequality (United Nations, 2015).

Topic analysis

Topics analysis allows us to better understand the dynamics of the entire communication through the identification of links between individual hashtags. The results of the topic analysis are shown in Table 2.

Table 2:

Identified topics related to #CSR.

Absolute and percentage size and key words are listed for each topic.

Topic	Size of the topic		Key terms
	%	Absolut
Charity	16.06%	83,691	Charity, support, volunteer, help, donate, donation, volunteering, community, philanthropy, nonprofit
Company government	15.78%	82,203	Business, company, corporate, impact, work, community, strategy, value, community, good
Sustainability	10.96%	51,383	Sustainability, green, sustainable, esg, sdg, report, environmental, business, change, future
Education	7.06%	36,800	School, education, child, student, project, skill, young, support, initiative, program
Climate change	5.24%	27,305	Climate, energy, carbon, climatechange, renewable, emission, green, sustainability, solar, environment
CSR reporting	3.60%	18,733	Corporate social responsibility, csr reporting, social impact, report, company, business, read, impact, esg, responsible
Waste	3.18%	16,581	Plastic, water, waste, recycle, food, circular economy, green, sustainability, packaging, reduce
Marketing	2.81%	14,633	Brand, purpose, marketing, consumer, customer, business, company, profit, market, social impact
Human resources management (HRM)	2.78%	14,470	Job, employee, hire, job opening, workplace, HR, employee management, career, manager, engagement

DOI: 10.7717/peerj-cs.1390/table-2

The largest topic identified is Charity which includes aspects relating to volunteer and philanthropic activities: charity, volunteer, donate, philanthropy, etc. These results are in accordance with the research conducted by Kvasničková Stanislavská et al. (2020), which drew on data from Instagram from 2019 to identify philanthropic responsibility as the biggest topic in both developing and developed countries. The same conclusion was also drawn by a study carried out by Ngai & Singh (2021), which analysed the CSR communication of Chinese companies on the Sina Weibo social network. Charity and philanthropic activities have formed a basic part of the CSR concept since its very beginning (Carroll, 1991); our results show that, despite the fact that CSR has seen a major shift towards sustainability, these still have their place in the concept.

The second largest topic identified is Company Government. This topic involves communications relating to the management of a company, such as a strategy, work, business, value, etc.

A significant proportion of CSR communication on Twitter is devoted to sustainability. As Pilař et al. (2019) state, as evidence of environmental risks mounts up, sustainability is being included into the agendas of legislators and organisations, as well as company objectives. The results of a cluster analysis identified this area in the topics Sustainability, Climate Change and Waste, which together make up 19.38% of CSR communication on Twitter. The dominance of sustainability in CSR communication is also highlighted by a hashtag frequency analysis (Table 1). Our findings can be explained as the importance that environmental issues have taken on in the eyes of the public, as also confirmed by studies by Connor et al. (2022), Jakučionytė-Skodienė, Krikštolaitis & Liobikienė (2022) and Wassmann, Siegrist & Hartmann (2023). Companies are recognised as sustainability leaders and use communication about sustainability to attract public attention and to increase pressure on key (Abeydeera, Tregidga & Kearins, 2016).

Another topic identified is Education, which includes terms such as school, student, child, project, and skill. Support for education within the framework of CSR activities is a routine part of CSR and is of fundamental significance particularly in developing markets, where companies often use CSR programs to take up the role of the state, where it fails to act itself (Valente & Crane, 2010), although they are still used in western countries, too (del Baldo, 2018; Chuah et al., 2022). According to Roy, Rao & Zhu (2022), investment in education and health care within the framework of CSR programmes gives companies higher stock market liquidity than investment in sustainability or social justice. A visual analysis (see Fig. 2) has identified that communication in the field of CSR and Education is closely linked to the field of CSR and Charity.

The topic Marketing communicates content aimed at promoting sales, such as purpose, brand, customer, market, etc. Okazaki et al. (2020) found that certain companies use CSR communication on Twitter solely as another advertising channel and do not make the most of the potential of dialogue with stakeholders for creating value. However, according to Jahdi & Acikdilli (2009), focusing excessively on boosting sales entails the risk of the company losing consumer trust in its CSR communication as a whole.

The last topic identified is Human resources management (HRM), which includes terms such as job, employee, workplace, career, etc. HRM can be classed as part of the internal social pillar within the Triple Bottom Line concept defined by Elkington (1994). There are also current studies focusing on CSR and HRM (Podgorodnichenko, Edgar & Akmal, 2022; Stahl et al., 2020; Turner et al., 2019; Yin et al., 2021) although previous research analysing CSR on social media had not identified HRM prior to then. This could therefore be a new trend, with the CSR concept gaining strength in HRM.

A visual analysis enabled us to identify the link between Charity and Education together with CSR HRM. Topics polarised from this group are those focusing on the environment and sustainability (Sustainability, Climate Change a Waste). These poles then include the topic Company Government, which overlaps into both groups. These results are in accordance with the partial results achieved by individual studies (Karagiannis et al., 2022; Khan, Lockhart & Bathurst, 2021; Ndubuka & Rey-Marmonier, 2019).

Sentiment analysis

Sentiment analysis is used to identify the emotions that are represented in the monitored tweets (Shamoi et al., 2022). Our results (Fig. 3) shows that the greatest negative sentiment is generated by communication on the topics of Waste (10.9%), Marketing (9.5%), and Climate Change (8.1%). Negative sentiment communicated in relation to the topics of Waste and Climate Change is not necessarily the result of public opposition to communicated CSR activities in these areas but is generally a consequence of negative emotions—such as the sadness and fear that environmental problems elicit in certain parts of the population (Gago & Sá, 2021; Hickman et al., 2021). In contrast, negative sentiment occurring the Marketing topic is probably related to consumer distrust and their concerns that CSR activities will be misused for companies’ marketing purposes and for so-called greenwashing (Ginder & Byun, 2022; Martínez et al., 2020).

In contrast, the highest positive sentiment can be identified with the topics of Charity (56.9%) and Education (41.8%). Neutral sentiment was most frequently elicited by topics communicated in connection with HRM (71.3%) and CSR Reporting (70%).

The results of the sentiment analysis indicate that companies focusing on topics related to charitable activities or supporting education on Twitter have a high potential to generate positive discussion. On the contrary, posts that deal with environmental topics such as waste management, the effects of climate change, etc., are dangerous to evoke more negative emotions in stakeholders than other topics. It is therefore crucial for companies to be aware of what kind of communication can generate positive reactions and what kind can have a negative impact on customer and public relations when publishing posts.

Growing trend

Based on the trend analysis, it was possible to identify three hashtags that have a growing trend. (1) #ESG—this hashtag expresses “Environmental, Social and Corporate Governance”, (see Fig. 4), (2) #Socialimpact—this hashtag expresses “significant or positive changes that solve or at least address social injustice and challenges. Businesses or organisations achieve these goals through conscious and deliberate efforts or activities in their operations and administrations” (Ricee, 2021) (see Fig. 5), and (3) #Charity—this hashtag expresses “form of money, given freely to people who are in need, for example because they are ill, poor, or have no home” (English Cambridge Dictionary, 2022) (see Fig. 6).

Our results confirm the growing popularity of ESG, which is mentioned for example in studies Zainullin & Zainullina (2021), Clément, Élisabeth & Léo (2022), and Dmuchowski et al. (2023). One of the reasons for this trend may be that ESG represents the best available tool for evaluating CSR (Cini & Ricci, 2018). According to Sætra (2021), the concept of ESG even replaces the concept of CSR. For these reasons, companies should pay considerable attention to ESG in their social media communications to keep up with current trends.

The trend has not been proven

The four hashtags have not been proven a trend. (1) #sustainability (see Fig. 7), (2) #sgds—This hashtag expresses the Sustainable Development Goals (see Fig. 8), (3) #business—expressing the connection of CSR with the business environment (see Fig. 9) and (4) #leadership—connecting CSR and Leadership. Leadership is not about leaders, but about CSR processes that need to be supported (Lythreatis et al., 2021; Phillips, Thai & Halim, 2019) (see Fig. 10).

Cluster analysis of time series

The similarity in the development of the monitored time series was evaluated. The aim was to identify which time series show the most similar development and at the same time to explore whether some time series show a different trend development. Based on this analysis August 2020 was identified as a turning point where ESG values grew more faster than other time series. The most similar are the time series Charity and Leadership, to which the Socialimpact time series joined. Another similar pair is Philanthropy and Business. ESG was the only value that showed significantly different behaviour (see Fig. 11). On the basis of this analysis, the ESG values were performed more detailed to the break using the Chow’s test. The development of the time series is then very different for the Sustainability time series, which contained the highest shares of all monitored time series throughout the time series.

Conclusions

In the current competitive era, communicating CSR activities is essential to a company’s CSR strategy. By social media analysis with a focus on hashtags and tweets, the present study determined the method by which companies communicate CSR on the Twitter social network worldwide. Analysis of this communication has identified a high proportion of environment-related terms in CSR communication. Of the nine topics extrapolated, three relate to the environment: (1) Sustainability, (2) Climate Change, and (3) Waste. The environment also features strongly in CSR communication according to the results of an analysis of the frequency of the individual hashtags, specifically #sustainability, #esg, #green, #sustainable, and #environment. The results also indicate that meeting sustainable development requirements is a high priority for the business sector, which is in line with the results of studies conducted by Tsalis et al. (2020) and Bose & Khan (2022). When examining the detection of characteristics of CSR communication on Twitter, the article uses an automated machine learning approach to automatically analyse content in tweets instead of using the manual coding techniques commonly used in mainstream CSR communication research.

The results of the study show that CSR is a topic that is frequently communicated on Twitter. As pointed out by Sharma (2019), well-communicated CSR campaigns on social media are enormously beneficial to companies. Our results aid enterprises in developing communication strategies that support the company’s existence as a socially responsible subject and promote the management of the company’s reputation in accordance with the CSR philosophy.

This research brings new challenges for future studies. In connection with the results, in future research, it would be advisable to ascertain whether the topics communicated vary over time.

This research used the Modified Louvain algorithm to extract the individual topics. In follow-up studies, it would be appropriate to make use of other methods, such as Latent Dirichlet Allocation (Blei, Ng & Jirdan, 2003), or structural topic models (Roberts, Stewart & Airoldi, 2016).

In future studies, expanding the analysis by the local context of individual CSR activities, such as in the study focusing on Italy (Patuelli et al., 2021) would provide the identification of differences between individual countries in comparison with the global context and the overall trend of globalization.

Our study identified the trends of unique hashtags in connection with the hashtag #CSR. Further research is needed to learn more about the attributes that lead to these trends.

Limitation of this research, similar to earlier research (Dong & Rim, 2019; Ngai & Singh, 2021) that focused on the analysis of social networks, this study was focused on a single social network, namely Twitter, which opens up possibilities for further research for other social media, such as Instagram or LinkedIn.