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The rise of blockchain cannot be separated from the promotion of 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 experienced fluctuations from hot to cold. With regulation, the wild growth of the cryptocurrency circle and blockchain media has come to an end. Where should it go next?

1. Information sharing, blockchain achieves data liberation

Everyone can have complete historical data on the blockchain, which allows limited reporters and editors to have access to a wealth of materials, greatly reducing the cost of manually collecting and organizing data. The information sharing of blockchain is not only different from the traditional way journalists collect information but also differs from regular online searches, as reporters can not only quickly obtain complete information through the blockchain but also "directly contact" the information source 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 for ranking 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 platform profit, with the most representative example being "bidding rankings" and other marketing tactics.

In addition to the evaluation proof method mentioned above, PressCoin has built an ecosystem for independent news organizations to produce and trade 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 information from independent news organizations is recorded on the blockchain and 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 copyrights

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 rewriting. Blockchain can provide proof of the existence of news works at a specific point in time, with the complete transaction data stored in the blockchain technology database, including both the creation time of the work and the copyright transaction time, 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, where 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 whom, and to what extent users' personal data is opened is authorized by the users themselves, rather than being arbitrarily disposed of by network information platforms.

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. Currently 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 enhance efficiency.

Paul Levinson proposed the concepts of "compensatory media" and "the humanization trend of media," pointing out that humans continuously make rational choices during 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, and media that better meets human needs is retained. To some extent, blockchain technology is a compensation for past technologies, such as privacy protection and source verification, and can better meet the needs of people in today's society. However, blockchain technology is merely a technology that adapts to the development of the times; it cannot solve all problems.

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

Second, the immutability of blockchain conflicts with the right to be forgotten. Due to the development of digital technology and global networks, 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, the individual has the right to request data controllers to permanently delete such information, provided it does not violate the law. 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, few in the news industry understand how blockchain technology operates, and since news content platforms themselves already have "algorithmic black boxes," does relying on algorithmic programs in blockchain technology not present the same issues?

The media revolution has arrived,

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

What is media? The textbook definition—media is the carrier that transmits information between the communicator and 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 easily fall into a whirlpool 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 providing content incentives and monetization, allowing both writing and reading articles to earn money, making everyone 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. Operating media based on blockchain technology and applications, using blockchain thinking and methods, represents a brand new media ecosystem 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 non-transparent review mechanisms. 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 significantly 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 publication and distribution of content fairer, more just, and more objective. Everyone can publish articles, everyone can evaluate articles, and everyone can benefit based on their contribution.

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-commercialization, 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 on virtual currencies and their impacts, reflecting the early research hotspots of blockchain, from the market fluctuations of Bitcoin value, the impact of currency value changes on investors, to the status of virtual currencies in the financial system, among many other research angles. The domestic ban on 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 the field of 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 application 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 fields such as banking and insurance, where blockchain has been qualitatively defined by many institutions, especially financial institutions, as a disruptive and valuable emerging technology, alongside artificial intelligence, big data, and other technologies.

These types of social networks use blockchain to record the information dissemination and transmission 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 this type of social network pay more attention to their statements in the community and are more rational in viewing various information published in 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, reducing the cost of information storage, etc. Only a few studies have proposed dissemination models for blockchain social networks, but these studies still do not consider the opposing 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 level 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. Therefore, users will express their opinions more rationally 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 token incentive policies.

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 that content should be prioritized for visibility to allow more users to see it, thus promoting the dissemination of quality content and receiving platform token incentives.

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 the content they disseminate 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 initially determine the authenticity of the information received by querying the historical publication and dissemination records of the user who created (or disseminated) that information.

In the actual situation of information dissemination in blockchain social networks, suppose there is information T (T represents support or opposition information for a certain topic) in the SNS, the nodes in the SNS are divided into susceptible nodes S (Susceptible), exposed nodes E (exposed), agreeing nodes A (Advocates), opposing nodes O (Objector), and immune nodes R (Removed). The agreeing nodes A and opposing nodes O are collectively referred to as infected nodes (Infected). The S node indicates that the user has not yet come into contact with information T. The E node indicates that after coming into contact with information T, the node is temporarily in an observation state to maximize its economic benefits. The A node indicates that after coming into contact with information T, the node holds a supportive opinion and chooses to disseminate supportive information. The O node indicates that after coming into contact with information T, the node holds an opposing opinion and chooses to disseminate opposing information. R indicates nodes that are 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, agreeing nodes, opposing nodes, and immune nodes at time t with scale k, respectively, 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 agreeing node A with probability psa, or to opposing node O with probability pso, or choose to temporarily observe due to economic incentives and penalties, converting to exposed node E with probability pse. Here, psa, pso, and pse are the probabilities of susceptible node S agreeing, opposing, and observing the information, respectively.

2. When exposed node E comes into contact with agreeing node A or opposing node O again, it may convert to agreeing 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 agreeing, opposing, and becoming immune, respectively.

3. Agreeing node A converts to immune node R with probability par, where par is the immunity probability of agreeing node A regarding 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 regarding 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 observer receives messages from an infected person, i.e., x = pea + peo, and 1 - x = per. y and z are the probabilities of supporters and opponents disseminating information, respectively. When an infected person stops disseminating messages, they will become immune, i.e., 1 - y and 1 - z are respectively pir and por.

After 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 in all I (λ and 1 - λ), they obtain their basic benefits λE and (1 - λ)E from the community's economic incentives. When I successfully influences healthy nodes, i.e., expands the range of information dissemination, I will receive corresponding additional benefits. 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 for disseminating information.

uE1 = (yλ - zλ + z)E - (y + z)c

(2) uE2 ≡ 0

Constructing the dynamic equation for the probability of the observer 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 observer E choosing the "accept" and "not accept" strategies, their strategy will not change over time. At this time, per and pea + peo remain unchanged.

• (2) If (yλ - zλ + z)E - (y + z)c ≠ 0, letting F(x) = 0, we can find x = 0 and x = 1 as the two stable points for x. That is, in the absence of mutants choosing opposite strategies, the proportion of observer E choosing a specific strategy (stabilizing in "accept" or "not accept") will no longer change. At this time, deriving F(x) gives us

We can derive the trends and functions of transition probabilities per, par, and por at time t. The participants in the above game are observer E and all infected individuals, so the above calculations cannot define the trends of transition probabilities pei and peo when observer E chooses the "accept" strategy. When E chooses the "accept" strategy: (1) If E chooses to accept the information disseminated by A, the payoff through the game matrix is λE - c; (2) If E chooses to accept the information disseminated by O, the payoff through the game matrix is (1 - λ)E - c.

λ represents the current density of A among all infected individuals, and (1 - λ) represents the current density of O among all infected individuals. Therefore, under the condition that E selects the "accept" strategy, its payoff is related to the densities of A and O among all infected individuals, meaning that the side with more participants will always yield higher benefits for E. This somewhat reflects the phenomenon in real social networks where individuals suppress their doubts and conform to the prevailing beliefs, a phenomenon known as the bandwagon effect. Wan Youhong et al. described the impact of the initial dissemination rate of information and the density of disseminators at a certain moment on the probability of information dissemination. Based on existing research and in conjunction with this article's actual situation, we derive the dynamic change equations for transition probabilities pea and peo under the condition that E selects the "accept" strategy.

It can be seen that the incentive mechanisms in blockchain social networks and the prevalent bandwagon psychology in social networks will influence the dissemination behavior of different groups in blockchain social networks. Users' expected benefits are related to their basic benefits E, dissemination costs c, dissemination penalty risks R, and additional dissemination benefits. The density of different infected individuals also affects their basic and additional benefits. Therefore, adjusting the above parameters will impact the density changes of each group. Since the information dissemination time is relatively short, this article does not consider the dynamic changes in network scale in subsequent experiments.

Since the dissemination benefits of groups E, A, and O are 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 benefits of groups E, A, and O to be either 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 article 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 the degree of the nodes. The darker the color and the larger the size, the larger 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 the information reaches the maximum dissemination range. Whether the model can describe this effect is also an indicator of whether the model is reasonable. 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 initial proportion of various disseminators at the start of dissemination will significantly impact users' dissemination behaviors after they encounter information. This experiment selects different initial density ratios of different types of disseminators to observe the density differences of various information disseminators when the information reaches the maximum dissemination range. This article 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 detected in advance. Relevant research focuses on information dissemination modeling, maximizing influence, and other aspects.

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

Second, this article extracts relevant features from the Weibo network and comprehensively analyzes the factors influencing forwarding, considering the user influence and information reinforcement effects that affect forwarding, using the linear threshold model (LT) and the infectious disease model (SEIR) as initial blueprints to improve the threshold representation method, achieving predictions for 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 in-degree and out-degree of the network structure. 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 analyzed that the maximum crawl volume 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 explored. Considering that core users have different definitions in different scenarios, in the context of information dissemination, this article uses users' Weibo diffusion ability and their influence on lower-level users as measurement indicators to calculate the core degree of core users. Specifically, using the PageRank concept, based on the Weibo forwarding relationship network and user attention relationship network, we construct indicators for the timeliness of information forwarding, users' forwarding influence, the strength of influence on lower-level users' emotions, and the users' own positional information in static networks to determine users' core degree.

• 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 the sentiment words in the text;

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

• Sum the scores of all groups, with positive sentiment scores greater than 0 and negative sentiment scores less than 0, where the absolute value indicates the strength of the sentiment.

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

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

• User quality: Reflects the strength of the user's influence on information dissemination.

Thus, this article calculates the size of users' information dissemination capabilities within the topic by linearly combining these two metrics.

Classic dissemination theory divides information dissemination into "mass communication" and "interpersonal communication." With the continuous development of social network analysis (SNA) methods, there has been an over-structuralization phenomenon in predicting the scale of information dissemination, which overly emphasizes 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 publishing or forwarding is that of infected users, and their direct followers are susceptible users.

2. Assumption 2: The probability of Weibo users becoming infected users from susceptible users 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 article proposes a dissemination scale prediction model based on attention relationships, user influence, and information reinforcement effects. This model builds on the linear threshold model (LT) while emphasizing the influence of different users. The model consists of two parts: the initiation part and the subsequent dissemination part. The initiation part considers the influence of the root 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 will not forward; if PR(u) ≤ γv, user v will forward. The subsequent dissemination part experiences reinforcement effects due to information redundancy, and the total influence on users is calculated as follows:

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 v follows have forwarded the information, user v will definitely participate in forwarding.

The main purpose of link prediction is to infer the probability of a link existing between network nodes. This article mainly studies the link prediction problem in the forwarding relationship of Weibo dissemination networks.

This article uses forwarding data to conduct comparative analysis on different indicators, splitting the data into training and testing sets in a ratio of 0.85:0.15. Various link prediction methods such as Adamic-Adar, Jaccard Coefficient, Preferential Attachment, Node2vec, and Variational Graph Auto-Encoders are attempted, with the metrics for measuring the accuracy of link prediction algorithms primarily 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.