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The rise of blockchain cannot be separated from the promotion by blockchain media. So, do you know what blockchain media is? In fact, blockchain media is not just media that reports on blockchain content, but a new type of media format that relies on blockchain technology applications and brings revolutionary changes to the media content field.

Blockchain media is primarily in the form of reporting blockchain news and has undergone changes that can easily shift from hot to cold. With regulation ending the wild growth of the cryptocurrency sector, where should blockchain media go next?

1. Information sharing, blockchain achieves data liberation

Everyone can have complete historical data on the blockchain, which means that limited reporters and editors can have access to a vast amount of material, greatly reducing the cost of manually collecting and organizing data. The information sharing of blockchain not only differs from the traditional way reporters collect information but also differs from regular online searches, as reporters can not only quickly obtain complete information through blockchain but also "directly contact" the information sources to further analyze the content behind the information.

2. Blockchain combats fake news

In the internet age, professional news production is increasingly becoming amateurish, making it difficult to guarantee the credibility and authority of sources; at the same time, some self-media outlets resort to unscrupulous means to attract user attention, spreading false information, leading to the proliferation of fake news. Almost all news content platforms have a set of algorithms to rank news content, but these algorithms are not transparent, and the controllers of news content dissemination platforms have significant leeway to manipulate this filtering and ranking. This manipulation of rankings is also an important means of profit for platforms, with the most representative example being "bidding rankings" and other marketing tactics.

In addition to the evaluation proof methods mentioned above, PressCoin has built an ecosystem for independent news organizations for the production and trading of news products. Independent news organizations living in the PressCoin system are interdependent and symbiotic. Each independent news organization is allocated a data block that stores its information, and PressCoin evaluates the contributions of these independent news organizations to the entire system through blockchain for redistribution. All independent news organizations' information has been recorded on the blockchain, and it cannot be arbitrarily tampered with without a private key. Once an organization is found to publish false information, the system can directly locate the source of the publication through the blockchain and impose corresponding penalties.

3. Blockchain tracks and protects copyright

In the digital age, information dissemination no longer relies on physical carriers, making it difficult for authors to control digital works, leading to a large number of plagiarism and content rewriting. Blockchain can provide proof of the existence of news works at a specific point in time, as the blockchain technology database stores complete transaction data, including the creation time of the work and the time of copyright transactions, with records that are transparent, accurate, and unique. In the vast array of news production works, tracking and tracing can be conducted based on data, providing strong proof and robust technical support for copyright protection.

4. Blockchain protects social media user privacy

Personal information protection is a significant issue faced by network security worldwide, as users' identity information, browsing traces, and created content are not under their control. Blockchain uses P2P technology, requiring no identity information, and can achieve data encryption while obtaining trust through a fixed algorithm. In this model, when to open personal data, to whom, and to what extent is all authorized by the user, rather than being arbitrarily disposed of by the network information platform.

5. Driving rapid advancements in public opinion analysis technology

Blockchain has the characteristics of authenticity and immutability, making it an excellent entry point for accurately analyzing online public opinion. Current popular public opinion warning platforms collect information that often suffers from vagueness, chaos, and inaccuracy, with the time cycle required for public opinion processing being too long; even with appropriate strategies, the targets may still be incorrect. Based on blockchain, public opinion response plans will have high precision. The details of which people the plans will target, what means will be used for guidance, and when to implement them will significantly improve efficiency.

Paul Levinson proposed the concepts of "compensatory media" and "the humanization trend of media," pointing out that humans continuously make rational choices in the evolution of media. Human technology is becoming increasingly perfect, but new media brings new problems. The evolution of media is the result of human choices, with media that better meets human needs being retained. To some extent, blockchain technology is a compensation for past technologies, such as privacy protection and source verification, and it can better meet the current needs of society. However, blockchain technology is merely a technology that adapts to the development of the times; it cannot solve all problems.

First, blockchain, as a new technology, faces the most urgent issue of lacking a complete set of unified standards and legal procedures. The issue of the lack of responsible parties for smart contracts and the fact that the copyright proof provided by blockchain does not have direct legal effect indicate that the legal and regulatory framework for blockchain urgently needs improvement.

Second, the immutability of blockchain conflicts with the right to be forgotten. Due to the development of digital technology and the global network, the pattern of human memory and forgetting has undergone fundamental changes, with forgetting becoming the exception and remembering becoming the norm. If negative information about an individual spreads on the internet, then, without violating the law, individuals have the right to request data controllers to permanently delete such information. However, the immutability of blockchain places the right to be forgotten in an awkward position of uncertainty.

Additionally, whether blockchain technology can truly suppress fake news is also questionable. On one hand, in terms of technology, very few people in the news industry understand how blockchain technology operates. Since news content platforms already have "algorithmic black boxes," does relying on algorithmic programs in blockchain technology also present the same issues?

The media revolution has arrived,

In this world of massive information and echo chambers, media is constructing a new ecosystem based on blockchain technology that is distributed and decentralized.

What is media? The textbook definition—media is the carrier that transmits information from the communicator to the receiver. In the blockchain era, the attributes of media are undergoing a qualitative change.

The evolution of media can be roughly divided into four eras.

The first is the classical media era, where traditional newspapers, magazines, and broadcasts belong to classical media;

The second is the social media era, where social media serves as a platform for people to share insights and opinions with each other. People no longer need to sit in front of the television at 7 PM to watch the news; all information sources have shifted to scrolling through friends' circles and Weibo, with "friends' circles becoming a way of life";

The third is the smart media era, where big data calculates your reading habits. When you click on a news article, countless similar articles appear. However, in the reading world constructed by algorithms, people can easily fall into a vortex of subjective preferences for information and opinions, leading to a lack of unified public opinion and an increasingly fragmented presentation of the world;

The fourth era is the blockchain media era, where this system can eliminate fake news while also providing content incentives and monetization, allowing both writing and reading articles to earn money, with everyone being a creator and a beneficiary.

True blockchain media is not media that reports on blockchain (Blockchain Reports), but a new distributed media (Distributed Media) that utilizes the distributed, decentralized, immutable, collaboratively maintained, and smart contract characteristics of blockchain technology to issue tokens. Based on blockchain technology and applications, operating media with blockchain thinking and methods represents a brand new media ecology in the development of human society to date.

Represented by media, the media of the first three eras are all centralized platforms, holding the power of life and death over articles, with review mechanisms that are not transparent. For example, in WeChat public accounts, whether an article can be published requires machine or manual editorial review, and all articles must go through a centralized platform, where the platform's review standards affect the author's creation;

If an article's title contains sensitive topics, the review time will be greatly extended. Even if the review is approved, once reported, the platform will immediately delete the article for self-protection. In the blockchain media era, distributed and decentralized technology will make the publishing and distribution of content fairer, more just, and objective, allowing everyone to publish articles, everyone to evaluate articles, and everyone to benefit based on their contributions.

The arrival of blockchain technology will address the pain points in media development, heralding the arrival of a new media era.

From self-media to self-branding, and then to self-business, media will reshape brand communication, overturn many industry concepts, and open up new perspectives.

The clustering situation from 2013 to 2017 is shown in Figure 3-1, where clusters #7, #8, and #9 are all discussions related to virtual currencies and their impacts, reflecting the early research hotspots of blockchain, from the market fluctuations of Bitcoin's value, the impact of currency value changes on investors, to the status of virtual currencies in the financial system, among many studies. The domestic prohibition of ICOs (Initial Coin Offerings) has also sparked academic discussions on the legal aspects of virtual currencies, which can be clearly reflected in the research hotspots.

On the technical level, clusters #0 and #6 are closely related to blockchain technology research. The earliest research focused on information security, represented by cryptography; between 2013 and 2014, Vitalik Buterin first proposed the concept of Ethereum, leading to widespread academic attention on smart contracts and attempts to expand their applications to other fields.

From the application level, clusters #1, #2, #3, and #4 all reflect the academic exploration in related fields. It can be seen that early blockchain applications were concentrated in the economic and financial sectors such as banking and insurance, with blockchain being characterized by many institutions, especially financial institutions, as a disruptive and valuable emerging technology, alongside artificial intelligence and big data.

These types of social networks use blockchain to record the information dissemination and propagation within the social network, making all users' statements traceable, and rewarding contributors and disseminators of quality content while punishing creators and disseminators of false and spam information. These characteristics increase the cost of producing and disseminating information, and compared to users in traditional social networks, users in these social networks pay more attention to their statements within the community and are more rational in viewing various information released within the community, thus constructing a new type of information dissemination environment.

The profit-risk matrix proposes a public opinion dissemination model for social networks in the blockchain environment. However, most current research focuses on the application prospects of blockchain technology in the field of information dissemination and improving the efficiency of information dissemination in blockchain, as well as reducing information storage costs. Only a few studies have proposed dissemination models for blockchain social networks, but these studies still do not consider the opposing opinion groups in social networks and the impact of different incentive policies on the dissemination behavior of each group.

First, as a decentralized distributed ledger, due to the technical characteristics of the incentive layer in the infrastructure, each node in the blockchain needs to verify data to reach consensus and keep accounts, so it is necessary to design reasonable incentive measures to align the interests of each node in the blockchain with the overall consensus. This underlying technical characteristic translates to the application layer as various blockchain-based social network platforms issuing economic tokens to quality content creators and disseminators as incentives, providing economic motivation for platform users' creations. Thus, users will be more rational in expressing their views to gain as much recognition and token incentives from other users as possible. While users regulate their behavior, social network platforms can also effectively guide platform users by adjusting the incentive policies for tokens.

Second, the consensus layer of the blockchain technology infrastructure efficiently forms consensus in a highly decentralized system by utilizing the characteristics of blocks. During the dissemination process, users on blockchain social network platforms influence the effectiveness of information dissemination to a certain extent. Users can pay platform tokens to vote on whether a piece of content is quality (or low quality) information and whether it should be prioritized for visibility to allow more users to see it, thereby promoting the dissemination of quality content and receiving token incentives from the platform.

Finally, the data stored in the blockchain is traceable and difficult to tamper with. Blockchain technology uses timestamps and digital signatures to ensure the stability and reliability of the information stored in the blockchain. Users' dissemination behaviors and content will be stored in the blockchain and cannot be deleted; even if users delete a local record of certain information, that information will still be recorded in other distributed ledgers. With this characteristic, other users on the social network platform can preliminarily determine the authenticity of the information received by querying the historical posting and dissemination records of the user who created (or disseminated) that information.

The actual situation of information dissemination in blockchain social networks assumes that there exists information T (T being supportive or opposing information of a certain topic) in the SNS, dividing the nodes in the SNS into susceptible nodes S (Susceptible), exposed nodes E (exposed), supportive nodes A (Advocates), opposing nodes O (Objector), and immune nodes R (Removed), where supportive nodes A and opposing nodes O are collectively referred to as infected nodes (Infected). The S node indicates that the user has not yet been exposed to information T. The E node indicates that after being exposed to information T, the node is temporarily in a wait-and-see state to maximize its economic benefits. The A node indicates that after being exposed to information T, the node holds a supportive opinion and chooses to disseminate supportive information. The O node indicates that after being exposed to information T, the node holds an opposing opinion and chooses to disseminate opposing information. The R node indicates that the node is no longer influenced by the information.

The state transition process of the SEAOR model

Let S(k,t), E(k,t), A(k,t), O(k,t), R(k,t) represent the densities of susceptible nodes, exposed nodes, supportive nodes, opposing nodes, and immune nodes at time t with scale k, and at any time: S(k,t) + E(k,t) + A(k,t) + O(k,t) + R(k,t) = 1. The transition rules between states are described as follows:

1. When susceptible node S comes into contact with target information, S may convert to supportive node A with probability psa, or to opposing node O with probability pso, or choose to remain in a wait-and-see state due to economic incentives and penalties with probability pse. Here, psa, pso, and pse are the probabilities of susceptible node S supporting, opposing, and waiting, respectively.

2. When exposed node E comes into contact with supportive node A or opposing node O again, it may convert to supportive node A with probability pea, to opposing node O with probability peo, or to immune node R with probability per. Here, pae, peo, and per are the probabilities of exposed node E supporting, opposing, and becoming immune, respectively.

3. Supportive node A converts to immune node R with probability par, where par is the immunity probability of supportive node A to the target information.

4. Opposing node O converts to immune node R with probability por, where por is the immunity probability of opposing node O to the target information.

5. Once a node becomes an immune node R, its state no longer changes.

Based on the above state transition rules and system dynamics, the information dissemination model in blockchain social networks can be expressed as:

(1)

where pcon is the probability that any random edge in the network is connected to an infected node.

In the matrix, x is the probability that an exposed person receives messages from infected nodes, i.e., x = pea + peo, and 1-x = per. y and z are the probabilities of supporters and opponents disseminating information, and when infected nodes stop disseminating messages, they will turn into immune nodes, i.e., 1-y and 1-z are respectively pir and por.

When groups S or E receive messages, they convert to group I (A or O), incurring a voting cost c, and based on the densities of A and O among all I (λ and 1-λ), they obtain their basic income λE and (1-λ)E from the community's economic incentives. When I successfully influences healthy nodes, thereby expanding the range of information dissemination, I will receive corresponding additional income. At the same time, the messages disseminated by I may be deemed low-quality content, and I will face corresponding economic penalties, i.e., the penalty risk of disseminating information.

uE1=(yλ-zλ+z)E-(y+z)c

(2)uE2≡0

Constructing the dynamic equation for the probability of the wait-and-see node E adopting the strategy "accept"

(5)F(x)=x(1-x)[(yλ-zλ+z)E-(y+z)c]

(6)

• (1) If (yλ-zλ+z)E-(y+z)c=0, then F(x)≡0, meaning that regardless of the proportion of wait-and-see node E choosing the strategy "accept" or "not accept," their strategy will not change over time. At this point, per and pea + peo remain unchanged.

• (2) If (yλ-zλ+z)E-(y+z)c≠0, letting F(x)=0 leads to x=0 and x=1 as the two stable points for x. That is, in the absence of mutants choosing opposing strategies, the proportion of wait-and-see node E choosing a specific strategy (stabilizing in "accept" or "not accept") will no longer change. At this point, differentiating F(x) yields

The trends and functions of transition probabilities per, par, and por at time t can be derived. The participants in the above game are the wait-and-see node E and all infected nodes, so the above calculations cannot define the trends of transition probabilities pei and peo when wait-and-see node E chooses the strategy "accept." When E chooses to accept information disseminated by A, the game matrix indicates that its income is λE-c; when E chooses to accept information disseminated by O, the game matrix indicates that its income is (1-λ)E-c.

λ represents the current density of A among all infected nodes, and (1-λ) represents the current density of O among all infected nodes. Therefore, under the condition that E selects the strategy "accept," its income is related to the densities of A and O among all infected nodes, meaning that the side with more participants will always yield higher income for E. This somewhat reflects the phenomenon in real social networks where individuals suppress their doubts and conform to the prevailing beliefs in the face of public opinion, a phenomenon known as the bandwagon effect. Wan Youhong et al. described the impact of the bandwagon effect on the probability of information dissemination by combining the initial dissemination rate of information and the density of disseminators at a certain time. Based on the existing research and the practical context of this paper, the dynamic change equations for transition probabilities pea and peo under the condition that E selects the strategy "accept" can be derived.

It can be seen that the incentive mechanisms in blockchain social networks and the prevalent bandwagon psychology in social networks will influence the dissemination behaviors of different groups in blockchain social networks. Users' expected income is related to their basic income E, dissemination cost c, dissemination penalty risk R, and additional income from dissemination, while the density of different infected nodes also affects their basic income and additional income. Therefore, adjusting the above parameters will impact the density changes of each group. Since the time for information dissemination is relatively short, this paper does not consider the dynamic changes in network scale in subsequent experiments.

Since the dissemination income of groups E, A, and O is always greater than their dissemination risks, the densities of groups A and O will rise within a short time and will not decrease, ultimately reaching a steady state.

Adjusting the incentive policies allows the dissemination income of groups E, A, and O to potentially be greater than 0 or less than 0, with the trends of each group shown in Figure 6.

The economic incentives provided by blockchain social networks can profoundly influence users' dissemination behaviors within social networks. Economic benefits can greatly stimulate users' enthusiasm for disseminating information, while the economic penalty mechanism coexisting with incentives can keep users rational and skeptical when faced with different information, preventing them from easily believing false and low-quality information. In actual public opinion monitoring, platforms can adjust incentive policies according to different situations to highlight high-quality content and suppress the dissemination of low-quality information, which is more conducive to creating a positive and healthy online public opinion environment.

A Sean model for content recommendation was proposed, which was compared with other content-based recommendation methods such as CF algorithms on a dataset constructed on the blockchain social platform Steemit, achieving good results. This paper uses its publicly available Steemit user relationship dataset to construct a complex network. The network topology is shown in Figure 2. This figure contains 7,242 nodes and 273,942 edges. The color and size of the nodes represent their degree. The darker the color and the larger the size, the greater the degree of the node.

Social networks use incentive mechanisms to highlight high-quality information and suppress low-quality information. To some extent, blockchain social networks can utilize their incentive mechanisms to encourage users to suppress the dissemination of low-quality information. The suppression effect can be represented by the density difference of different information disseminators when information reaches the maximum dissemination range. Whether the model can describe this effect is also an indicator of the model's rationality. In the blockchain environment, due to economic incentives, users will be more cautious in choosing dissemination behaviors rather than disseminating information indiscriminately upon receiving it. When users frequently encounter a certain type of opinion, they are more inclined to choose to disseminate that type of viewpoint. Thus, the proportion of various disseminators at the start of dissemination will significantly impact users' dissemination behaviors after they are exposed to information. This experiment selects different initial density ratios of various types of disseminators to observe the density differences of different information disseminators when information reaches the maximum dissemination range. This paper compares the model with traditional communication models.

In addition to identifying core users, the scale of dissemination is also a key factor affecting the final dissemination effect of Weibo. By predicting the scale of information dissemination, the final impact range of information dissemination can be discovered in advance. Relevant research focuses on information dissemination modeling, influence maximization, and other aspects.

First, this paper defines the identification of core users by analyzing the complete forwarding links in the Weibo network.

Second, this paper extracts relevant features from the Weibo network and comprehensively analyzes the factors affecting forwarding, considering the user influence and information reinforcement effects. Using the linear threshold model (LT) and the infectious disease model (SEIR) as initial templates, it improves the threshold representation method to predict the final dissemination scale of a single Weibo post.

The attention relationship is an important component of its social network structure, with the attention relationships between users collectively forming the network's in-degree and out-degree. By analyzing 88,829 pieces of user attention data, the following findings were made:

• 8,420 individuals (10%) have a maximum of 993 followers, and we analyze that the maximum crawling amount during data scraping was 993.

• A large number of users have follower counts in the range of 100 to 200, which aligns with general logic, as most people have limited energy to handle social affairs.

Two networks and four indicators are discussed. Considering that core users have different definitions in different scenarios, in the context of information dissemination, this paper uses users' Weibo diffusion capability and their influence on lower-level users as measurement indicators to calculate the core degree of core users. Specifically, it employs the PageRank concept, based on the Weibo forwarding relationship network and user attention relationship network, to construct indicators that determine users' core degree based on information dissemination timeliness, user forwarding influence, the strength of influence on lower-level users' emotions, and users' positional information in static networks.

• Tokenize the text to identify sentiment words, negation words, and degree adverbs;

• Determine whether there are negation words and degree adverbs before each sentiment word, grouping them with sentiment words in the text;

• If there is a negation word before a sentiment word, multiply the sentiment word's sentiment weight by -1; if there is a degree adverb, multiply it by the degree adverb's degree value;

• Sum the scores of all groups, with positive sentiment scores greater than 0 and negative sentiment scores less than 0, where larger absolute values indicate stronger emotions.

Degree algorithm: In the process of information dissemination based on social networks:

• Forwarding influence: Reflects the information dissemination capability of the user being forwarded within the topic.

• User quality: Reflects how the user's information dissemination capability affects the strength of other users.

Therefore, this paper calculates the size of users' information dissemination capability within the topic by linearly combining these two metrics.

Classic dissemination theory posits that information dissemination can be divided into "mass communication" and "interpersonal communication." With the continuous development of social network analysis (SNA) methods, there has been an excessive "structural" phenomenon in predicting the scale of information dissemination, that is, overemphasizing network structure while neglecting the macro aspects of information dissemination. The interactions between individuals significantly impact the final dissemination scale, and exaggerating the role of network structure often contradicts actual situations.

This has blurred the boundaries between "unstructured dissemination" and "structured dissemination."

Satisfying the following assumptions:

1. Assumption 1: The status of users who publish or forward is that of infected users, and their direct followers are susceptible users.

2. Assumption 2: The probability of Weibo users transitioning from susceptible to infected is β.

3. Assumption 3: The probability of users transitioning from infected to immune status is α.

4. Assumption 4: Users who do not follow these infected users are considered external users. Such users read Weibo independently and have a probability of γ to forward.

Given a certain hot topic and trust circle at time t, in the SIRE model:

• S(t) represents the number of susceptible users at time t, who may forward;

• I(t) represents the users who have forwarded the Weibo post and have dissemination power;

• R(t) represents the number of immune users R, indicating the number of users who will no longer forward the Weibo post at time t.

This paper proposes a dissemination scale prediction model based on attention relationships, user influence, and information reinforcement effects, which focuses on the influence of different users on the basis of the linear threshold model (LT). The model consists of two parts: the initiation part and the subsequent dissemination part. The initiation part considers the influence of the original Weibo user u on the fan set fans(u) as PR(u), and the forwarding threshold for user v is set as a random number between 0 and the sum of PR values of all users followed by that fan (Fv), i.e., γv∈[0,sum(PR(Fv))]. If PR(u) > γv, user v does not forward; if PR(u) ≤ γv, user v forwards. The subsequent dissemination part accounts for the redundancy of information, resulting in reinforcement effects, with the total influence on users calculated as Influce(v):

The forwarding threshold for v is set as a random number between 0 and the sum of PR values of all users followed by that fan (Fv). Unlike the LR model, when nearly 90% of the users followed by v have forwarded the information, user v will certainly participate in forwarding.

The main goal of link prediction is to estimate the probability of a link existing between network nodes. This paper primarily studies the link prediction problem in the Weibo dissemination network based on forwarding relationships.

This paper uses forwarding data to conduct comparative analysis on different metrics, splitting the data into training and testing sets in a ratio of 0.85:0.15. It attempts various link prediction methods such as Adamic-Adar, Jaccard Coefficient, Preferential Attachment, Node2vec, and Variational Graph Auto-Encoders, with the main metrics for measuring the accuracy of link prediction algorithms being AUC and Precision, where AUC measures the overall accuracy of the algorithm, and Precision only considers whether the top L edges are predicted accurately.