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Mechanisms of Information Flow Transmission and Reader Interpretation in English and American Literature in the Network Era

  
Mar 21, 2025

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Introduction

In the context of globalization, the exchange and integration of different cultures are becoming more and more frequent, and British and American literary works, as representatives of Western culture, contain cross-cultural elements in their works involving geographical environment, historical background, religious beliefs, and other aspects [1-3], and through cross-cultural interpretation, the study of British and American literary works can provide a deeper understanding of the history, traditions, and values of British and American culture, so as to better understand and appreciate British and American literary works and promote the development of cultural diversity [4-6].

Anglo-American literary works are an important part of Anglo-American culture, which contains rich cultural connotations, and through the interpretation of these works, the deeper meaning behind Anglo-American culture can be better understood [7-8]. Anglo-American literary works often deal with major human propositions and universal values, but there are some differences in the interpretation of propositions and values in different cultures [9-11]. In the context of the network era, readers’ interpretation of English and American literature helps to eliminate cultural differences and misunderstandings, and promote understanding and communication between different cultures. Through the dissemination of the flow of information on the English and American works, the commonalities between different cultures can be found and the deeper meanings embedded in these commonalities can be explored in depth [12-15], for example, in interpreting the British writer Jane Austen’s novel Pride and Prejudice, it can be seen that thinking about marriage and love is a universal human subject, and the perspectives and expressions may be different in different cultures, but the core emotions and conflicts are common [16-17].

This paper proposes an improved semantic Web mining algorithm by adding semantic information on the basis of traditional Web mining algorithms and introducing computational methods such as node conditional probability, semantic depth and semantic branching degree. It deeply mines book reviews, texts, and other critical resources on Chinese Web 2.0 websites with great influence and development potential, and explores the impact of information flow transfer on British and American literary works. Aiming at the three typical theoretical paradigms of author-centered, text-centered and reader-centered, a mechanism of “author-text-reader” integrated multi-perspective interpretation is constructed. A questionnaire was employed to analyze how the author-text-reader interpretation mechanism affects readers’ comprehension of English and American literature.

The effect of information flow transfer in English and American literature
Semantic Web Mining Algorithms

In this section, the semantic Web mining algorithm is mainly used to count the commonly used labels of the books on the network platform, and 337 different labels are assigned to the book resources based on their different cultural backgrounds and social experiences, and the 8 labels with the highest frequency of use are placed in the “commonly used labels”, so that users can find information resources they are interested in by consulting the relevant resources labeled with the labels. Users can find the information resources they are interested in by checking the relevant resources labeled with the tags. A study of the dissemination of English and American literature in China.

Analysis of Semantic Distance Calculation Methods

Semantic distance is a related concept in semantic similarity, which refers to the path length of two concept nodes in the ontology tree. Semantic distance is an important reference aspect to measure semantic similarity. At present, there have been many scholars in the semantic similarity has a very in-depth study, but also put forward the corresponding semantic distance calculation method, for the two page nodes in the ontology tree of the semantic distance calculation has been a relatively unified idea, that is, for the page nodes between the sum of the path weights, that is, according to the definition of the concept of the semantic distance itself to calculate the generalized calculation formula as equation (1): Dist(C1,C2)=i=1nweighti

C1 and C2 represent two page nodes in the ontology tree, weighti represents the semantic weight on the ith path connecting C1 and C2, and Dist represents the semantic distance. Therefore, the key point here is how to find the semantic weights on a certain path, and the more representative calculation methods are the following three.

Considering the calculation method of node conditional probability, it is stipulated that the formula for calculating the semantic distance from the conceptual child node to the parent node in the ontology tree is shown in Equation (2): distJC(c,par(c))=logp(c|par(c))

Where c denotes the child concept node, par(c) denotes the parent concept node, p(c|par(c)) represents the conditional probability of the child node with respect to the parent node, and dist denotes the semantic distance, and the formula for calculating the semantic distance between any two concept nodes in the ontology tree is equation (3): distJC(c1,c2)=2logp(lso(c1,c2))(logp(c1)+logp(c2))

Where lso(c1, c2) represents the nearest public parent node of (c1, c2), and p(c) represents the probability of the node appearing in a particular ontology library. The key point of semantic distance here is to find the nearest public parent node of the two conceptual nodes and compute the conditional probability of each in order to get the final semantic distance.

Improvement of Semantic Distance Calculation Methods

Consider the calculation method for semantic depth and semantic branching degree, as well as the depth of the concept node in the ontology tree and its corresponding parent node’s categorization fineness. Theoretically, the deeper the concept node is in the ontology tree, the finer it is categorized in terms of semantic degree, and thus the semantic information gap between deeper concept nodes is smaller and the semantic distance is smaller compared to the concept nodes closer to the root. Based on this idea, the following semantic weight calculation formula (4) is proposed: weight(C)={1Wid(C)C=R1Wid(C)×1α×weight(parent(C))CR

weight(C) represents the semantic weight of C node, Wid(C) represents the branching degree of C node, parent(C) represents the parent node of C, and the parameter α ≥ 2, from the definition of Eq. (4), it can be reasoned that the semantic weight of C node is inversely proportional to the degree of its categorization, i.e., Wid(C) is also inversely proportional to the depth in which it is located, and the specific proportion can be realized by adjusting the α parameter.

Semantic overlap refers to the number of concepts within the ontology that directly contain the same superordinate concepts, representing the degree of identity between concepts. Semantic depth refers to the depth at which a particular concept node is located in the ontology hierarchy tree, which is consistent with the statement presented here about the location of the concept node in the ontology tree.

Semantic density refers to the number of neighboring nodes of this concept node in the ontology hierarchy tree, i.e., the number of sibling nodes, the more sibling nodes a certain concept node has, the smaller the semantic distance is, and this point of view of the branching degree of the concept node is also consistent.

Semantic attribute is to consider the size of the distance between attributes from the perspective of the concept’s own attributes, if two concepts have similar attribute information, it means that the two concepts themselves have a certain degree of similarity, the greater the degree of similarity, the smaller the semantic distance between them should be.

Combining these four factors, the proposed improved formula is shown in equation (5): Dis(C1,C2)=Dis0(C1,C2)*α*β*γ*θ

Dis0(C1, C2) represents the semantic distance between concept nodes C1, C2 when the weights of all edges in the ontology tree are 1. The following α, β, γ and θ represent the four influencing factors of semantic overlap, semantic depth, semantic density, and semantic attributes, respectively, and their respective calculations are more complicated and will not be discussed in detail here.

Since the text information used in text mining is obtained within a time period, but this time period is not fixed, the length of time is also unknown, which users will visit during this time period, and the specific visiting habits of users are also unknown, so the quality of the data obtained from the text files is not well controlled. Generally speaking, in the web page layout of a website, the more information is contained in a certain web page, the more the corresponding categories are, i.e., the more branches are extended downwards.

Due to the limited access to text data and limited time period, the final parsed information does not cover all the branches on the whole website, so the probability information of the concept node is added in the calculation of the weight value here. The higher the probability of the occurrence of a concept node in the text data, the more information the corresponding page has, the more branches it has, and the smaller its own semantic weight value, i.e., the probability of the occurrence of a concept node is higher, the more information its corresponding page has, and the more branches it has. That is, there is an inverse relationship between the occurrence probability of a concept node and the semantic weight.

The improved formula is shown in equation (6): weight(C)={1Wid(C)×Info(C)C=R1Wid(C)×1α×weight(parent(C))×Info(C)CR

Parameters α ≥ 2, Info(C) denote the amount of information about the weights of the C node, and P(C) denotes the probability of occurrence of the C node, which is computed here using the method used in information theory, i.e., Info(C) = − lg(P(C)). It can be seen that Info(C) is decreasing with P(C), where: P(C)=CThe number of times a node page appearsTotal number of node pages

Finally the semantic distance between any two nodes Cl, C2 in the tree is calculated as defined in equation (6).

Sampling data set of book labels across the web

Information flow transmission refers to the dissemination and flow of information. Based on this, compared with the study of the dissemination of Anglo-American literature in China over a longer period of time, the slice-and-dice status quo study of the current event nodes can reflect the dissemination of Anglo-American literature in China more concretely. Therefore, in order to study the real dissemination of British and American literature in China, this study randomly selects any day in the first half of 2023 and takes the 90 days after that date as the monitoring period, i.e., June 14, 2023~September 10, 2023, as the monitoring period to study the dissemination status of British and American literature. At present, many Web2.0 platforms have certain capabilities of resource annotation and label sharing, but the requirements of different social annotation systems in terms of format specification and semantic expression of labels are not completely consistent, in order to avoid this kind of discrepancy due to the existence of factors such as the users, resource scale, and technological level between different systems from influencing the experimental results. The Web2.0 website, which has a good reputation in the industry and has been recognized as a highly influential and development potential website in China, is selected as the empirical research object for the mining of semantic relationship between tags. On the surface, the platform can provide book reviews, music reviews, movie reviews and other review resources, but in fact it also provides users with a variety of services such as book and video recommendations, interest groups, offline activities and other services.

Various recommended lists on reading platforms include New Book Express, Most Attention Book List, Best Selling Book List (Jingdong, Amazon and Dangdang), “Douban Book 250” and so on. Among them, “Douban Books 250” is an authoritative list formed by comprehensively calculating and analyzing the number of people who have read each book and the evaluation of the book (five grades between “very poor” and “highly recommended”) and other related data through the background program algorithm of the whole network.

In order to carry out the research, the top 25 books were selected from the “Book 250” list, and then the top 5 books were selected from the “2023 High-scoring Books”, “2023 Most Watched Books” and “2023 World Literature” of the “2023 List”, and then the five most commonly used tags were extracted from these book resource tags to form a sampling dataset with 200 tags, which has a certain representativeness and validity. The results are summarized in Table 1.

The data collection of the bean flap book label

Book name Label 1 Label 2 Label 3 Label 4 Label 5
Little prince Fairy tale France Classic Foreign literature Novel
The man who pursued the zither Novel Afghanistan Human nature Redemption Foreign literature
Siege Qian zhongshu Novel Chinese literature Classic Literature
Live Yuhua Live Literature Chinese literature Novel
The light of life that cannot be borne Foreign literature Novel Philosophy Czech republic Literature
Miserable world The UK Novel Foreign literature Classic Famous work
Da Vinci code Suspense Novel Religion Reasoning Foreign literature
Jianai Foreign literature Classic Love Novel Famous work
Harry Potter and the stone Harry Potter Magic Novel The UK Foreign literature
Pride and prejudice Foreign literature Love Classic Novel The UK
Martian rescue Science fiction Science fiction novel The United States Foreign literature Novel
Centennial loneliness Centennial loneliness Latin American literature Novel Classic Foreign literature
.......

The most commonly used 200 labels in the above table are “de-emphasized” to streamline the data into a data set with 70 different labels, and in accordance with the frequency of use of the label will be arranged in descending order, the results are shown in Table 2. In order to simplify the experimental process, this paper according to the frequency of book label sampling data in the table, select the use of frequency “greater than or equal to 4” labels for this experiment. Among them, the 10 labels “novel” (30), “foreign literature” (27), “literature” (26), “British and American literature” (21), “classic” (17), “love” (15), “English” (5), “poetry” (13), “essay” (11) and “poetry” (10) are used more frequently than other labels, and they have both complex and simple words, which are representative.

The data of the bean flap book label is sorted by frequency

Tags Frequency Tags Frequency
Novel 30 Suspense 5
Foreign literature 27 The UK 5
Literature 26 Afghanistan 5
Anglo-American literature 21 Centennial loneliness 4
Classic 17 Live 4
Love 15 Czech republic 3
Chinese 14 Redemption 3
Poetry 13 Latin American literature 3
Essays 11 Harry potter 2
Poem 10 Classical literature 1
Science fiction 9 Philosophy 1
Science fiction novel 9 Religion 1
The United States 7 Fairy tale 1
The United States literature 7 ......
Status of dissemination of literary information

From the perspective of communication platforms, microblogging is the absolute main body of the dissemination of English and American literature, followed by WeChat and all kinds of APP, as a relatively specialized content, Q&A platforms including Zhihu and all kinds of forums also contribute to the dissemination of English and American literature, and the self-media also produce a certain amount of relevant content under certain circumstances. However, since English and American literature is not a social hotspot at present, the contribution of traditional media such as radio, newspaper, and television to the dissemination of English and American literature is relatively small. Although APPs other than today’s headlines also contribute a certain amount of information, these contents come from a huge APP base, and their contribution is not significant from the perspective of individual APPs. The information dissemination of each platform is specifically shown in Table 3. Taking “literature” as the keyword, we captured relevant data from all over the internet, and obtained a total of 30,191 pieces of relevant information, among which, microblog is the main source of information, with a total of 21,422 pieces of relevant information, accounting for more than 69.2%, followed by various types of APPs, with a proportion of 6.53%. The highest and second highest peaks of information dissemination occurred on July 25, 2023 and August 21, 2023, respectively.

Information dissemination of various platforms

Order Platform Proportion
1 Micro blog 69.2%
2 APP 6.53%
3 BBS 1.80%
4 Newspapers 0.16%
5 Video 0.14%
6 Headline 1.64%
7 Sohu 0.52%
8 Q&a 1.60%
9 Comment 0.03%
10 Broadcast television 0%
11 Other types 1.4%
12 Web page 4.42%
13 Wechat 12.56%

From the geographical point of view of the release of content related to English and American literature, the top 10 provinces and cities with the most information released during the monitoring period are: Beijing 2478, Guangdong 1942, Shanghai 1054, Jiangsu 947, Zhejiang 702, Sichuan 573, Shandong 540, Hubei 411, Chongqing 365, Guangxi 348, as shown in Figure 1:

Figure 1.

The geographical distribution of literary information publishers

It can be seen that the provinces that pay more attention to British and American literature are basically developed coastal provinces, and this ranking is basically consistent with the ranking of the number of undergraduate universities in various provinces in China, which can show to a certain extent that British and American literature is an improved information demand for the current public, and the audience of British and American literature is concentrated in the audience groups with better economic conditions and higher education level, which is a kind of niche communication content of “spring and snow”.

From the perspective of communication content, users highly recognize the status of English and American literature in the history of world literature, and often use adjectives such as “worldwide” and “giants” to describe English and American literature and its related characters. At the same time, users generally believe that British and American literature is a kind of “utopian” and “ideal” literature, and readers may have more resonance with the authors in terms of idealism in the process of reading British and American literature. However, at present, the most concerned British and American writers among Chinese readers are still writers such as Charlotte, which also proves that the dissemination of modern British and American literature in China in recent years is indeed unsatisfactory. According to all the text content captured during the monitoring period, after eliminating the subject words themselves and other meaningless words, a word cloud map can be formed as shown in Figure 2, which can reflect the content dissemination of British and American literature in China to a certain extent. And from the point of view of content dissemination, Charlotte’s works of Jane Eyre are still one of the most popular works among domestic readers, which are not only known and concerned by Chinese readers, but also provide quite a lot of inspirations and creative ideas for today’s Chinese literature and art works and variety shows, so it can be said that the influence of these works on Chinese readers has been subtle and ubiquitous.

Figure 2.

A cloud of content words

The word cloud map of the information related to the works of the six authors is shown in Figure 3, from which we can see the great influence of the golden age of English and American literature on today’s Chinese literary creation.

Figure 3.

Relevant information about six writers’ works

Mechanisms of reader interpretation affect the reception and interpretation of English and American literary works
The essence of textual interpretation

Let’s also start with the reference and denotation of language. Imagine if a text is written in a language that the reader does not recognize at all. Such a text naturally lacks its function of expression and communication. This phenomenon is so common that we may seldom think about it: what is the “text” that can be seen with the eyes and printed on the pages of a book? It is not just a set of symbols after a set of strings. In fact, symbolic reference is an indispensable aspect of the mutually prescriptive relationship established by the interaction of referential sound imagery with relevant experience fragments under the motivation of communication.

According to a great deal of existing research, especially in the hermeneutics of Heidegger, Gadamer and their aftermath, meaning is not only in the author, nor in the written word, nor in the absolute hegemony of the reader’s interpretation. Historically, meaning should be the result of the interaction between the author, text, and reader, co-constructed through the reader (including the analytic interpreter). Specifically, the text is the bridge and intermediary between the author and the reader. The author sets the direction of interpretation. The reader is the current subject who creates this linking function and enables the “author-text-reader” to jointly achieve meaning.

Interaction of readers and texts and authors

Jacobson’s six-element diagram of verbal communication is shown in Figure 4. Rhetorical theory reduces the traditional view of narrative to a structured symbolic system that exhibits the interconnectedness of a series of events, specifically, communication with multiple purposes from the narrator to the receiver. This communication takes the form of symbolic communication, which in the classical sense is described as “the exchange of meaning represented by symbols”: the communicator, as the sender of the symbol, “encodes its meaning into a specific sequence of symbols (such as a sequence of sounds, a mark on a piece of paper, or a visible gesture)”, while the receiver “decodes such meaning from the sequence of symbols it receives”. Breaking down the communicative process of verbal communication according to a system, the diagrams he lists contain six elements with different functions, namely, the sender (the sender or encoder of the message), the receiver (the recipient or decoder of the message), the message itself, the symbols (the meanings that the message expresses), the context (or the objects referred to by the discourse that the message relates to), and the linkage (the linkage between the sender and the receiver).

Figure 4.

Six elements of speech communication

Jacobson’s diagram can then be re-presented as shown in Figure 5. The sender sends the message to the receiver, and for the message to work it needs to be associated with some kind of context, and for the receiver to capture this context, whether it is linguistic or translatable into language, it needs to be expressed in the form of a symbol code, which is usually a symbolic form that is familiar to both the sender of the message and the receiver of the message, and which can appear as a linguistic symbol or as some other symbol. Finally there needs to be some kind of linkage, a physical channel and a psychological connection that remains open between the sender and the receiver, which allows both to enter and maintain this process of communicative exchange. On the basis of Jacobson’s model of symbolic communication, we can omit the “link” from our discussion of literary works, since it is usually expressed through written language.

Figure 5.

Communication of literary works

Audience Response Survey of Chinese Readers

Chinese readers are exposed to many foreign literary works, which include many excellent writings representing different cultural backgrounds such as Western culture, African culture, Latin American culture, and so on. However, cognitive patterns in different cultural backgrounds may cause certain biases in the reception and interpretation of literary works, and may lead to misinterpretation, misinterpretation, or peculiar interpretations by Chinese readers in the reading process.

In this study, an online questionnaire was designed for Chinese readers. 500 questionnaires were distributed and 450 were valid to ensure the representativeness of the sample and the reliability of the statistical analysis. The questionnaire mainly investigated the evaluation and opinion of several literary works using the “author-text-reader” interpretation mechanism, as well as the degree of understanding and recognition of the cultural and emotional elements in the works. The survey results are shown in Table 4.

The Catcher in the Rye

59% of the respondents thought that the work deeply reflected the struggles and confusion of teenagers in their growing up process and triggered them to think about their own values. 28% of the respondents felt that the storyline was not compelling enough to empathize with the main character. 13% of the respondents were unfamiliar with the western cultural background of the work, which made it difficult to understand some of the plot points. 72% of the respondents were able to understand the Western cultural context of the work and agreed with the ideas and values embedded in it. 24% of the respondents felt that the cultural elements in the works differed from traditional Chinese values, but were still able to understand and accept them. 4% of the respondents were unfamiliar with the cultural elements in the works and had a low level of understanding.

The Great Gatsby

44% of the respondents were attracted by the work’s insightful exploration of wealth, vanity and human nature, and considered the work to be of universal value and significance. 38% of the respondents found the plot of the novel too complicated and difficult to comprehend, which affected their overall feeling of the work. 18% of the respondents did not have enough knowledge of the social background of the early 20th century in the United States, which resulted in a limited understanding of the cultural elements in the work.

58% of the respondents were able to understand the social background of the early 20th century in the United States as depicted in the work, and agreed with the cultural elements in it. 24% of the respondents felt that the cultural elements in the work were at a certain distance from the reality of Chinese society, but they were still able to understand and accept them. 18% of the respondents were unfamiliar with the cultural elements in the work, and understood them to a lesser extent.

The reader’s interpretation mechanism affects the acceptance of the work

“The catcher in the rye” The struggle and confusion that reflect the growth of teenagers have led to their thinking about their values The story is not attractive enough to resonate with the hero The western culture of the work is unfamiliar and causes some of the trouble to understand
59% 28% 13%
Be able to understand the western cultural background in the work and have a certain recognition of the ideas and values that are contained in it Cultural elements in the work differ from traditional Chinese values, but they can still be understood and accepted The cultural elements in the work are unfamiliar and less understood
72% 24% 4%
“Great gatsby” The profound discussion of wealth, vanity and human nature is attracted The plot is too complex to understand The cultural elements in the work are limited
44% 38% 18%
Cultural elements agree Cultural elements are certain distance from Chinese social reality, but they can still be understood and accepted Cultural elements are unfamiliar and less understood.
58% 24% 18%
Conclusion

This paper focuses on the impact of information flow transfer on English and American literature by enhancing the semantic Web mining algorithm to thoroughly mine text information on Web 2.0 websites. Then, a multi-perspective interpretation mechanism of “author-text-reader” is proposed, and a questionnaire survey is conducted to investigate the impact of this interpretation mechanism on readers. The conclusions are as follows:

Among the online platforms, “novels” and “foreign literature” have the highest frequency. The main source of information is microblogging, accounting for more than 69.2%, followed by various types of apps. Coastal developed regions pay more attention to British and American literature. British and American literature have had a profound influence on Chinese readers and literature.

Although the background of Western culture is quite different from that of Chinese culture, most of the interviewees believe that they can still deeply understand its meaning by utilizing the “author-text-reader” interpretation mechanism.

Language:
English