Interplay of Cryptocurrencies with Financial and Social Media Indicators: An Entropy-Weighted Neural-MADM Approach

Cryptocurrency, a concept that revolutionized the financial sector, operates as a virtual currency on a decentralized ledger [1]. Circulating through peer-to-peer networks, it negates the necessity for mediators such as financial or governmental authorities [2]. Originating in 2008, the cryptosystem, pioneered by Satoshi Nakamoto through the Bitcoin project, marked a pivotal shift in the digital financial landscape [3]. Regarded as a digital counterpart of traditional currency [4], its transactional presence in the digital business realm has escalated rapidly [2]. Among various cryptocurrencies, Bitcoin stands out due to its unique features of decentralization, speed, anonymity, and transparency, which distinctly separate it from traditional currencies [1]. The decentralization aspect, in particular, makes Bitcoin's price movements highly sensitive to investors’ perceptions rather than institutional regulations [3].

The increasing popularity of cryptocurrencies extends beyond early adopters and enthusiasts, attracting widespread attention. However, recent incidents like the Terra-Luna episode and the 2022 bear market have underscored the complexities in valuing these digital assets, revealing their susceptibility to market sentiment [5]. Hence, elucidating the cause-effect and feedback relationships between cryptocurrencies and social network indicators has become imperative. Yet, the impact of social media indicators on the utility function of cryptocurrencies remains under-explored. This research focuses on eight pivotal social media indicators: BBC Breaking News; (US) Department of State; Donald Trump; Elon Musk; Twitter-based Economic Policy Uncertainty (EPU); Google Trends; Risk Aversion; and United Nations. In the domain of cryptocurrency analysis, the influence of social media and governmental entities is increasingly recognized as pivotal. The BBC, as a prominent UK-based news broadcasting platform, significantly impacts audience reactions, eliciting both positive and negative responses [6]. The United States Department of State, responsible for foreign policy and relations, plays a substantial role in influencing the predictability, returns, and volatility of digital currencies [1]. The tweets of the former US President Donald Trump are considered a notable indicator within the cryptocurrency utility function, reflecting their impact on market dynamics [7]. Similarly, Elon Musk, a globally recognized entrepreneur, wields considerable influence over the decentralized cryptocurrency market through his Twitter activities [8]. This study also incorporates Twitter-based EPU as a tool to gauge economic variability, providing insights into market conditions [1]. Google Trends, which tracks and analyzes user search behavior, is identified as a valuable predictor of Bitcoin returns [5]. Furthermore, this research considers various financial proxies that encapsulate investor risk aversion and economic uncertainty, contributing to the development of a utility-based aversion coefficient [6]. The role of the United Nations is also examined, given its significant presence in the cryptocurrency market and its efforts towards integrating digital currency into global sustainability initiatives [1]. Additionally, this study analyzes data from six major cryptocurrencies: Bitcoin, Ethereum, Dogecoin, Ripple, Litecoin, and Tether, all notable for their market capitalization. A diverse array of financial indicators, including precious metal prices, oil prices, major stock indices, the EUR/USD exchange rate, the VIX, the MSCI ACWI ETF, and Tesla stock prices, are incorporated to comprehensively assess the dynamics of the cryptocurrency market.

Extensive research within behavioral and financial economics has led to the prediction of cryptocurrency price fluctuations and their extrinsic correlation with social media and financial catalysts. In the realm of behavioral economics, it is posited that emotions shape investor sentiments, thereby influencing their decision-making process regarding financial investments [9]. These sentiments, in turn, significantly impact the pricing, return, and volatility of digital assets within the financial market [10]. Such evidence underscores the pivotal role of sentiment in the pricing mechanisms of digital financial assets, including Bitcoin.

A recent advancement in behavioral finance is the utilization of social media platforms to gauge investor sentiment [3]. These platforms offer a plethora of perspectives, opinions, and information, aiding decision-makers in the financial realm [11]. Platforms like Twitter and various chat tools have been identified as primary sources for financial analysts, investors, and regulatory bodies in making key financial decisions [12]. Empirical studies have highlighted the influence of social media on Bitcoin, with particular attention to Twitter's impact on market dynamicsor [4], [7], [8], [9], [10]. For instance, Huynh [7] investigated the correlation between sentiments expressed in Donald Trump's tweets and Bitcoin price movements, employing textual analysis to identify spillover effects. Similarly, research has examined Elon Musk's influence on Bitcoin price volatility through his Twitter activities, employing Granger causality to understand the relationship with market returns [8]. Yousaf et al. [4] explored the interconnection between the S&P 500 Twitter sentiment index and various asset classes, revealing that investor sentiments have a pronounced impact during periods of positive or negative sentiment shocks.

However, previous studies have often focused exclusively on individual social media platforms, such as Twitter or stock market sentiment indices, without considering a comprehensive view of all relevant social media platforms. This study aims to fill this gap by examining a wider array of the above eight social media indicators. Furthermore, earlier research did not fully explore diverse financial indicators that could provide insights into both predictive and commodity aspects of the financial market [6].

The structure of this paper is organized into four additional sections. Section 2 presents a detailed literature review, followed by an explanation of the methodology in Section 3. Section 4 delves into the analysis and discussion of results. Finally, conclusions are drawn in Section 5.

The allure of cryptocurrency has captivated financial investors, policymakers, and regulatory bodies globally [5], as evidenced by its remarkable 2394% market growth from 2016 to 2017 [2]. Increasingly perceived as a financial safeguard against global economic volatility, cryptocurrencies are being integrated into investment portfolios for their hedging capabilities against traditional financial instruments like gold and the US dollar, as well as against business policy uncertainty and shocks in the Asia Pacific region [1]. Consequently, an upsurge in investment towards this virtual currency is observed [13]. However, the hedging potential of these digital assets is intricately linked to social media and web analytics platforms, such as Twitter and Google Trends. It is posited that cryptocurrency price fluctuations are more influenced by public perceptions than institutional regulations [2]. This observation has spurred behavioral economists and academics to investigate the relationship between cryptocurrency price variations and social media networks [1]. Diverging from existing studies, financial researchers are also exploring the interplay between traditional financial assets and the heterogeneous nature of cryptocurrency assets [6]. This paper synthesizes two prominent strands of the cryptocurrency literature. The first strand investigates the interaction between traditional and alternative financial assets with cryptocurrencies, including their time-varying effects. The second, an emerging research area, examines how media attention and social networks influence cryptocurrency behavior.

In their research, Kumar et al. [14] utilized a Generalized Vector Autoregression (VAR) framework, discovering that indices such as S&P500 and NASDAQ have a more significant influence on cryptocurrencies like Bitcoin and Ethereum, rather than vice versa. Similarly, Ciner et al. [15] employed a LASSO quantile regression approach, finding that US government bond indices and small-cap returns are predictive of the tail behavior of cryptocurrency returns. Elsayed et al. [16] investigated the spillover effect between cryptocurrencies and other assets, identifying that cryptocurrency policy uncertainty significantly impacts return spillovers to other variables, while gold predominantly receives both return and volatility spillovers. Another set of studies, including those by Kumar et al. [14], have examined the interconnectedness between traditional assets and cryptocurrencies in a time-varying manner. They observed a notable increase in connectedness of returns and volatility among these markets following the onset of the COVID-19 pandemic.

While research linking sentiment to cryptocurrencies is burgeoning, it remains underdeveloped. Yousaf et al. [4] noted that the connectedness between the S&P 500 Twitter Sentiment Index and traditional assets (excluding cryptocurrencies) is stronger at the extremes of the return distribution. This finding indicates a pronounced impact of sentiment during extreme positive or negative sentiment shocks. Including cryptocurrencies, Naeem et al. [17] applied the bivariate cross-quantilogram method of Han et al. [18], finding a strong and persistent predictive relationship between happiness sentiment and most cryptocurrency returns. Behavioral economists have increasingly focused on the connection between social media sentiments and investor perceptions and attitudes, recognizing their influence in predicting cryptocurrency prices [3]. Bollen et al. [19], employing neural networks and causality networks, linked social media sentiments to cryptocurrency price forecasting. Wolk [2] explored the relationship between social media platforms and price prediction, using a multi-model approach. However, none of these studies have incorporated an integrated perspective of social media sentiment, encompassing heterogeneous drivers like news headlines, economic variability information, and business policy updates. This study aims to provide a comprehensive view, encompassing a range of social media indices to explore their impact on cryptocurrency market price variations. Alongside social media drivers, this research incorporates a variety of financial indicators, contributing significantly to understanding the speculative price fluctuations of crypto assets from a behavioral finance perspective.

The methodology of this study is predicated on an analysis of secondary datasets, from which seventeen distinct variables were selected, categorized under 'social media indicators' and 'financial indicators' (refer to Table 1).

The influence of the BBC as a social media indicator on Bitcoin price direction is significant, given its standing as a reputable information source in the United Kingdom [6]. The BBC's impact on audience perception is dual-natured: positive when broadcasting hopeful events and negative when focusing on adverse aspects of human nature [10]. The US Department of State's announcements are considered to have a direct influence on Bitcoin price fluctuations. Similarly, the United Nations is acknowledged for its leading role in the digital currency sphere [2]. The tweets of former US President Donald Trump are also analyzed for their impact on cryptocurrency, despite Twitter not being an official channel for policy communication in the US. Trump's tweets are found to correlate directly with Bitcoin’s trading volumes, volatility, and returns [7], [20]. Elon Musk's tweets are also considered due to their notable influence on the cryptocurrency market [8]. Parameters such as time, date, username, photos, hashtags, like counts, reply counts, and mentions are used to analyze and visualize Musk’s impact on the digital currency world [21].

In the field of cryptocurrency market analysis, Google Trends has emerged as a pivotal tool for predicting investor attitudes and perceptions. This web-based search tool offers extensive data on specific search terms over designated time intervals [2]. It has been observed that a higher frequency of Google searches correlates with increased positive returns in Bitcoin, leading to a surge in trading volume [1]. Furthermore, the concept of EPU has gained prominence in discussions about drivers of Bitcoin returns. An increase in global EPU is found to adversely affect Bitcoin returns [22]. Interestingly, the predictive power of the EPU index on Bitcoin returns exhibits geographical variance. For instance, the EPU index in China is effectively predictive of Bitcoin returns, while the EPU indexes of the United States and Japan do not demonstrate such predictive capacity [1]. In this study, the category of 'Twitter-based EPU' is utilized to gauge economic variability. Additionally, this research integrates a 'risk-aversion index' into its utility function to represent the crypto market's risk perspective. This index comprises financial indices related to risk aversion and economic uncertainty, formulated as a utility-based aversion coefficient [1].

Data pertaining to the query 'Bitcoin' was sourced from Google Trends [2]. This includes the total number of mentions of 'Bitcoin', scaled and compared across various timeframes. Subsequently, the log return of Bitcoin was calculated based on the obtained time series.

To analyze the correlation between influential individuals’ Twitter posts and Bitcoin price movements, raw data from the Twitter accounts of Donald Trump and Elon Musk were collected [7], [8]. A textual analysis was conducted to differentiate between positive and negative sentiments, following the approach of Loughran and McDonald [23]. Two proxy variables were developed for positive and negative words:

For the financial indicators influencing Bitcoin price fluctuations, this study considers the prices of precious metals (Gold, Platinum, Palladium, Silver), oil (Brent and WTI), stock indices (NASDAQ and S&P500), and Tesla stock prices. These indicators offer alternative investment perspectives due to their predictive and commodity aspects in the crypto market [1]. Data sources include MSCI (ACWI), Yahoo Finance (EUR/USD, Tesla), the NASDAQ Composite (NASDAQ), and SP Global (S&P 500) for financial indicators, and the London Bullion Market Association and Fred Economic Data for Metal and Petroleum data [4].

In the domain of MADM, various models have been employed to compute attribute weightings [24], [25], notably including the Entropy Method [24], [26], Information Entropy Weight (IEW) [27], Analytic Hierarchy Process (AHP) or [24], [28], [29], Fuzzy AHP [30], [31], and Rough AHP [32]. Recently, SWARA has been recognized for its efficacy in calculating attribute weights within performance measurement frameworks [33]. Liang and Ding [34] have emphasized the significance of accurately determining these weights, noting the potential for bias due to inherent uncertainties and subjectivity in scaling. Information entropy, conceptualized as a probabilistic measure of uncertainty, plays a pivotal role in this context [35]. It captures the variability in randomness levels across different analysis sub-groups. An attribute’s discriminatory power is directly proportional to its information entropy value.

This study utilizes information entropy to establish the initial order of importance for social media indicators in SWARA, facilitating the computation of unbiased weights. These weights are then utilized as inputs in the COPRAS method. Unlike other MADM approaches, COPRAS effectively establishes a utility function for each cryptocurrency [36], [37]. The concept of utility, fundamental in economics and MADM, quantifies latent perceptions or preferences across various criteriaor [38], [39], [40]. COPRAS's utility function approach, favored for its simplicity, lacks stringent requirements on latent preference structures beyond the aggregation formula, thus straightforwardly linking social media indicators to partial value functions [41], [42]. This additive aggregation’s simplicity renders the approach particularly suitable for subsequent multivariate analysis inputs [43].

The following steps outline the SWARA methodology applied in this research [44]:

Step 1: Social media indicators are initially sorted based on their information entropy, from highest to lowest.

Step 2: The first-ranked social media indicator is assigned a null latent preference value. Subsequent social media indicators are assigned preferences relative to the first, based on their pairwise relative importance. This is denoted by $S_j$, representing the ratio of comparison between a given social media indicator and the one with the highest entropy.

Step 3: Pairwise social-media efficiency $K_j$ is calculated using the formula:

Step 4: Relative weights ($q_j$) for each social media indicator are computed, based on their sorted pairwise efficiency and importance rank:

Step 5: Final weights are determined as $W_j=\frac{q_j}{\sum_{k=1}^n q_k}$, where $W_j$ denotes the weight of each social media indicator $j$.

The COPRAS method, introduced over two decades ago [37], has been widely explored in various research contexts. It has been combined with SWARA or [45], [46], [47], integrated into Fuzzy COPRAS [48], and applied alongside other MCDM methods [49], [50]. This section outlines the application of COPRAS in deriving utility functions for cryptocurrencies based on the social media importance weights identified in the previous section.

Step 1: A decision matrix $X$ is constructed, encompassing $m$ time-series observations and $n$ cryptocurrencies:

Step 2: The decision matrix $X$ is normalized:

Resulting in:

Step 3: A weighted normalized decision matrix is computed:

Thus:

Step 4: The larger, more preferable values, denoted as $P_i$, are summed:

Step 5: The smaller, more preferable values, denoted as $R_i$, are summed:

The number of cryptocurrencies to be minimized is determined by the difference $m-k$.

Step 6: The minimization of $R_i$ is performed as per Eq. (8):

Step 7: The relative significance of each cryptocurrency $Q_i$ is calculated:

Step 8: The optimal cryptocurrency $i$, indicated by $K$, is identified:

Step 9: Cryptocurrencies are prioritized in descending order.

Step 10: The utility degree N of each subsequent cryptocurrency $i$ is determined:

The information flow between two cryptocurrencies, denoted as $i$ and $j$, is measured by integrating Shannon Entropy [51] and Kullback-Leibler distance [52], assuming a Markov process with $k$ and $l$ levels or factors, respectively [53]. Given the probability distributions $p(i)$ and $p(j)$ for cryptocurrencies $i$ and $j$, and the joint probability $p(i, j)$, the information flow from cryptocurrency $j$ to $i$ is defined by the following equation [54]:

This equation quantifies the deviation from the generalized Markov process $p\left(i_{t+1} \mid i_t^{(k)}\right)=$ $p\left(i_{t+1} \mid i_t^{(k)}, j_t^{(l)}\right)$ at the marginal conditional distribution odds-ratio $\log \left(\frac{p\left(i_{t+1} \mid i_t^{(k)}, j_t^{(l)}\right)}{p\left(i_{t+1} \mid i_t^{(k)}\right)}\right)$.

Analogously, the information flow from $i$ to $j$ is measured, allowing for the determination of causation direction between two cryptocurrencies based on the net information flow. This flow is computed as the difference between flows from $i$ to $j$ and vice versa. The statistical significance of the net information flow between cryptocurrencies is ascertained through bootstrapping the inherent probability distributions for each factor/level in each criterion, enabling repeated execution of this Markov process.

The response of each cryptocurrency to a set of financial indicators is analyzed using Artificial Neural Networks (ANNs), specifically focusing on Multi-Layer Perceptron (MLP) network architecture, known for its efficacy in forecasting [55]. The ANN regression reveals the non-linear impacts of financial indicators on the variations of each cryptocurrency, while controlling for its latent utility function. The Connection Weight Approach (CWA), as described by Olden et al. [56] and Olden and Jackson [57], is utilized for quantifying the relative importance of each financial indicator on the response levels of each cryptocurrency.

This methodological approach is characterized by two distinctive features. Firstly, it elucidates cause-effect and feedback relationships among cryptocurrencies' utility functions, influenced by the entropy levels of social media indicators. The maximal entropy principle, an established concept in information theory, posits that the most representative probability distribution for a given cryptocurrency's utility function is one with the highest entropy. Secondly, this research diverges from previous studies by investigating the impact of financial indicators on the utility functions of various cryptocurrencies. By computing the information entropy of each social media indicator, the focus is directed towards the most significant criteria for policy making, and their socio-demographic drivers, which are otherwise indeterminable a priori.

Descriptive statistics for the social media indicators, considered as proxies for the utility function of cryptocurrencies, are presented in Table 2. Drawing on the work of García-Medina and Huynh [1], variables listed in Table 1 are posited as potential influencers of Bitcoin price direction.

The Twitter-based Economic Policy Uncertainty index (EPU-Twitter) is formulated using daily tweet data containing keywords related to economic uncertainty [1]. The 'risk-aversion' category includes financial indices reflecting risk aversion and economic uncertainty, conceptualized as a utility-based risk-aversion coefficient [1]. The influence of news sentiment, particularly from BBC Breaking News, on the cryptocurrency market has been acknowledged in recent studies [58], [59]. RavenPack News Analytics is employed to process real-time news from the BBC [13]. The United Nations is also included, given its significant role in promoting digital currency for achieving financial security and sustainable investment [60].

In the current utility function, BBC Breaking News, Elon Musk, Google Trends, and the United Nations are designated as positive criteria. Positive BBC headlines are seen to boost investor enthusiasm, leading to an immediate increase in Bitcoin returns [11]. Elon Musk's tweets, characterized by a positive tone, have been influential in guiding investor perceptions about Bitcoin trading [8]. Google Trends data reflects an optimistic pursuit of information about crypto assets, correlating positively with Bitcoin returns [2]. The United Nations’ efforts to integrate digital currency transactions with sustainability goals are perceived positively by financial investors [60].

Conversely, the Department of State, Donald Trump, EPU Twitter, and risk aversion variables are regarded as negative criteria. Post-2018, cryptocurrency trading platforms have become more sensitive to Donald Trump’s negative sentiments, with his tweets acting as a predictive driver for Bitcoin trading dynamics [7]. Increases in EPU Twitter and Risk Aversion are posited to deter demand for cryptocurrencies, thereby reducing their prices.

Figure 1 presents density plots for the social media indicator weights computed using SWARA, based on their respective information entropy distributions. The United Nations, EPU Twitter, and Elon Musk emerge as the most relevant criteria, indicating that cryptocurrency utility functions are largely influenced by perspectives on technological innovation and global socio-economic stability. The least relevant criteria, Risk Aversion, and BBC Breaking News, pertain to daily issues and natural risk aversion related to blockchain technology. However, the closeness of SWARA weights across social media indicators is evident in the overall distribution of the utility function, as depicted in Figure 1. The utility functions exhibit a positive bias, with all COPRAS scores exceeding 0.60 (Figure 2), suggesting the endogenous nature of cryptocurrencies and the potential for obscuring the impacts of other financial indicators as drivers for investment decisions (Table 3).

Transfer entropy and neural network results, elucidating cause-effect relationships among cryptocurrencies and financial indicators, are depicted in Figure 3 Additionally, Table 4 details the optimal ANN architecture for each regression, determined after cross-validating the trained models with a randomly selected 20% of the sample. It is observed that Bitcoin and Ripple are pivotal cryptocurrencies, influencing the behavior of other coins such as Tether, Ethereum, Litecoin, and Dogecoin, each representing unique aspects of the cryptocurrency universe.

Economically, the findings underscore Bitcoin's role as a significant information transmitter to other cryptocurrencies (Figure 3). Ripple, in contrast, shows a unique dynamic, where its past values are observed to reduce uncertainty in Bitcoin and influence fluctuations in other cryptocurrencies. Interestingly, Litecoin demonstrates a more independent trajectory, being influenced only by Bitcoin and not acting as a shock transmitter.

The analysis confirms the significant role of Ripple in transmitting shocks within the cryptocurrency market, aligning with the findings of Assaf et al. [61] and Neto [62]. While Assaf et al. [61] reported Ripple's information transmission to Bitcoin and Ethereum, Neto [62] observed Ripple returns influencing attention in Ethereum and Bitcoin, with Litecoin having a limited capacity to transmit shocks. The unique position of Ripple, partly due to Ripple Labs' majority ownership and the SEC lawsuit implications, suggests that Ripple movements convey critical information about the future of cryptocurrency regulation, a crucial aspect of this market. Therefore, these results are congruent with the notion that Ripple movements carry regulatory information, positioning it as a shock transmitter rather than an absorber.

Asymmetries have been observed in the relationship between social media indicators and cryptocurrencies. Bitcoin, Ripple, and Ethereum exhibit different behaviors compared to Tether, Litecoin, and Dogecoin. Risk aversion, typically a negative predictor for the return of risky assets, adversely affects Bitcoin, Ripple, and Ethereum, while positively impacting the other cryptocurrencies. Notably, this positive relationship is anticipated for Tether, a stablecoin, where an increase in risk aversion heightens demand.

Other noteworthy relationships between social media indicators and cryptocurrencies have been identified. The EPU Twitter, while adversely predicting Bitcoin and Ethereum, positively influences Ripple, Tether, Litecoin, and Dogecoin. This suggests that price movements in these cryptocurrencies increase with heightened uncertainty in social media. The sentiments in tweets by Donald Trump and Elon Musk also resonate differently across the crypto market. While Trump’s tweets predominantly negatively affect the market (except for Litecoin), Musk’s tweets positively influence Dogecoin and Ripple (negatively impacting the remaining cryptocurrencies).

Bitcoin and Ethereum emerge as the most negatively affected cryptocurrencies by social media indicators in the sample. For Bitcoin, seven out of eight relationships are negative, with only the United Nations showing a positive predictive relationship. In contrast, Ethereum exhibits negative associations in six out of eight relationships, with BBC Breaking News and the Department of State as exceptions, showing positive predictive relationships. Conversely, Litecoin, Dogecoin, and Ripple react positively to most social media indicators. Notably, Litecoin and Dogecoin are positively influenced by Twitter sentiment (Elon Musk) and uncertainty (EPU Twitter). In contrast to Litecoin, Dogecoin also responds positively to investor attention (Google Trends), suggesting a strong linkage to social media attention, sentiment, and uncertainty.

This study, utilizing a novel neural-MCDM model, has rigorously analyzed the interplay between cryptocurrencies and a spectrum of financial and social media indicators. It is determined that Bitcoin and Ripple, along with Ethereum, function as primary shock transmitters and absorbers, respectively, within the realm of cryptocurrencies. A notable asymmetry is identified in the interactions between social media indicators and various cryptocurrencies. Specifically, Bitcoin and Ethereum are more adversely influenced by social media indicators compared to Tether, Ripple, Litecoin, and Dogecoin. Conversely, Litecoin, Dogecoin, and Ripple exhibit positive responses to social media influences, with Litecoin and Dogecoin being particularly sensitive to Twitter sentiment, including that of Elon Musk, and uncertainty as reflected in the EPU Twitter Index. Furthermore, Dogecoin is observed to react positively to increased investor attention on Google Trends, indicating a strong tie to social media dynamics.

This study contributes significantly to the growing body of literature in behavioral finance that explores the influence of social media platforms on investor sentiment. It extends upon existing research that predominantly focuses on individual social media platforms, offering a more comprehensive view by incorporating an array of platforms including Twitter, various news services, and Google Trends. This integrated approach provides novel insights into the diverse relationships between different social media indicators and cryptocurrencies, each with distinct characteristics. By applying the transfer entropy method, this research elucidates the cause-effect and feedback dynamics among the utility functions of major cryptocurrencies, influenced by the entropy levels of social media indicators. This approach sheds light on the heterogeneous nature of these relationships, enhancing the understanding of how social media indicators differentially impact cryptocurrencies. In doing so, the study offers valuable insights into the complex web of interactions that shape the cryptocurrency market.

Addressing the identified gaps in existing research, this study revisits a secondary dataset comprising a blend of social media and financial indicators. It employs a novel neural-MCDM model, structured in three stages, to derive unbiased utility functions for various cryptocurrencies. This approach, anchored in information entropy, evaluates the latent significance of each social media indicator in the computation of each cryptocurrency's utility function, leveraging the SWARA model for weight computation. Compared to alternative methodologies, information entropy offers reduced bias and enhanced robustness against overlooked assumptions, facilitating a more nuanced interpretation of how distinct cryptocurrencies' utility functions, as determined by COPRAS, are influenced by various social media indicators.

The findings, both anticipated and unanticipated, reveal that Bitcoin and Ripple, along with Ethereum, function as primary shock transmitters and absorbers, respectively, in the cryptocurrency sphere. Notably, the significant role of Ripple, aligning with the research of Assaf et al. [61]and Neto [62], suggests its potential as a conveyer of information regarding future cryptocurrency regulations, particularly in light of Ripple Labs' ongoing legal challenges with the SEC. Bitcoin and Ethereum are observed to be more negatively impacted by social media indicators compared to Litecoin, Dogecoin, and Ripple, which exhibit positive responses to these influences. Despite discernible patterns, the unique characteristics of each cryptocurrency must be considered when assessing their interactions with social media indicators.

The insights garnered from this research are invaluable for policymakers and regulators, enhancing their understanding of the intricate relationship between cryptocurrencies and social media indicators. It is imperative for governmental actions to acknowledge the distinct sensitivities of different cryptocurrencies to social media influences, including attention, sentiment, uncertainty, and posts from government or tech leaders. Such understanding is crucial for navigating the complexities of the digital asset industry, particularly in terms of policy development and regulation. By comprehending the specific channels through which investor sentiment may influence the cryptocurrency market, stakeholders can better anticipate and respond to fluctuations within this dynamic sector.