A method for human-computer collaborative optimization of journal positioning

Abstract

A method for optimizing journal positioning through human-computer collaboration includes: obtaining a user-preset journal domain, custom_domain; performing a multi-dimensional analysis of the literature and associated information corresponding to custom_domain; and, based on the dimensional analysis results, determining whether custom_domain is suitable for new journal publication indicators. This method can help users fully understand the full scope of user-set domain information when performing journal positioning, avoiding a series of subsequent issues arising from poor journal positioning. This method forms a closed loop of human-computer collaboration and assists users in finding appropriate journal content positioning.

Description

Technical Field

[0001] The present invention belongs to the technical field of periodical optimization, and in particular relates to a method for human-machine collaborative optimization of periodical positioning. Background Art

[0002] Journal positioning is the result of market research and discussions with senior experts by pre-publishers. It is an essential step in the publication process. Journal positioning encompasses the journal's purpose, target readers, target authors, main columns, main content, and distribution methods, among other aspects. "Main content" is the primary aspect of journal positioning and defines the scope of pre-publication. However, in the era of big data and information explosion, data (volume and content) is constantly changing, making it impossible for market researchers or experts to fully understand the information. Relying on feedback from a specific demographic and the personal experience of experts to position pre-publication content can lead to information positioning bias and poor journal positioning. For example:

[0003] 1. There is a lack of submitted literature or authors in the pre-publication field, and a lack of literature support;

[0004] 2. The number of journals with the same positioning in the pre-publication field is already saturated or in oversupply, which is not friendly to the establishment of new journals.

[0005] To address these issues, this paper proposes a method for optimizing journal positioning through human-computer collaboration. First, the user's pre-defined journal domain (consisting of one or more topics) is obtained. This domain is then analyzed across multiple dimensions based on the literature. Finally, an evaluation index is generated to determine whether the pre-defined domain is suitable for a new journal. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for human-computer collaborative optimization of journal positioning. First, the user's preset journal field (consisting of one or more topics) is obtained, and then a multi-dimensional analysis of the field is performed based on the literature. Finally, an evaluation index is given to determine whether the preset field is suitable for publishing a new journal.

[0007] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0008] A method for optimizing journal positioning through human-computer collaboration, comprising:

[0009] S1 obtains the user's preset journal domain custom_domain;

[0010] S2 performs multi-dimensional analysis on the literature and belonging information corresponding to custom_domain;

[0011] S3 provides indicators of whether custom_domain is suitable for publishing a new journal based on the results of multi-dimensional analysis.

[0012] The present invention has the following advantages:

[0013] It can help users fully understand the overall picture of the field information set by users when positioning journals, avoid a series of subsequent problems caused by poor journal positioning, realize a closed loop of human-computer collaboration, and assist users in finding suitable journal content positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart of the method for optimizing journal positioning for human-machine collaboration. DETAILED DESCRIPTION

[0015] In order to better understand the purpose, structure and function of the present invention, the following is a further detailed description of a method for human-computer collaborative optimization of journal positioning of the present invention in conjunction with the accompanying drawings.

[0016] like Figure 1 As shown in the figure, the method process of human-machine collaborative optimization of journal positioning includes:

[0017] Get the user's preset journal domain custom_domain; search the Topic service topic_service based on the user's preset keywords topic_keywords, return topic_list in descending order of relevance, and then collaboratively select n topics as the user's preset journal domain. custom_domain = [topic_id_7, topic_id_300, ..., topic_id_n].

[0018] Conduct multi-dimensional analysis of the documents and information corresponding to custom_domain;

[0019] According to custom_domain, the literature of corresponding topics is retrieved from the Topic database as the data set custom_domain_corpus in this field, and the indicators analysis_custom_domain_corpus_re of each dimension of custom_domain_corpus are analyzed.

[0020] The indicators of each dimension include document doc_info, published author author_info and journal existing_journals_info. Among them, doc_info and author_info are used to analyze whether the content in the preset field can support and is suitable for publishing a journal, and existing_journals_info is used to analyze the value of publishing a journal in the preset field.

[0021] Analyze the custom_domain data set and its associated information from three dimensions: doc_info, author_info, and existing_journals_info. The quantities and trends of each dimension, i.e., the indicators of analysis_custom_domain_corpus_re, are given.

[0022] doc_info:

[0023] Document volume docs_counts: the number of documents in the past n years, where n is 5 years. According to statistics, the minimum number of documents for a journal is 100. If docs_counts is less than 100, it is not suitable for publishing, otherwise it is suitable.

[0024] Document growth trend by year docs_grow_trend_by_year:

[0025] Literature growth rate: (number of documents in the last year - number of documents in the first year) / (number of documents in the first year * (n-1)). If only 5 years of documents are taken, then n = 5 years. The values are as follows:

[0026] Positive: Literature growth rate >= 0.05;

[0027] neutral: the literature growth rate is greater than -0.05 and the literature growth rate is less than 0.05;

[0028] negative: literature growth rate <= -0.05;

[0029] author_info:

[0030] Author count authors_counts: The total number of authors included in the document collection. According to statistics, the average number of authors in a journal is 100. If authors_counts is less than 100, it is not suitable for publishing a journal, otherwise it is suitable.

[0031] Authors' annual growth trend authors_grow_trend_by_year:

[0032] Author growth rate: (number of authors in the last year of the paper - number of authors in the first year of the paper) / (number of authors in the first year of the paper * (n-1)). If only 5 years of papers are used, then n = 5 years. The values are as follows:

[0033] Positive: author growth rate >= 0.05;

[0034] neutral: author growth rate > -0.05 and author growth rate < 0.05;

[0035] negative: author growth rate <= -0.05;

[0036] existing_journals_info:

[0037] existing_journals_grow_trend_by_year: (A positive value indicates that the field is worth publishing)

[0038] Periodical growth rate: (number of periodicals in the last year - number of periodicals in the first year) / (number of periodicals in the first year * (n-1)). If only 5 years of periodicals are used, then n = 5 years. The values are as follows:

[0039] Positive: journal growth rate >= 0.05;

[0040] neutral: journal growth rate > -0.05 and journal growth rate < 0.05;

[0041] negative: journal growth rate <= -0.05;

[0042] The module returns the result: analysis_custom_domain_corpus_re={

[0043] doc_info:{docs_counts:value_int,

[0044] docs_grow_trend_by_year:{positive|neutral|negative}},

[0045] author_info:{authors_counts:value_int,

[0046] authors_grow_trend_by_year:{positive|neutral|negative}},

[0047] existing_journals_info:{existing_journals_grow_trend_by_year:{positive|neutral|negative}}

[0048] }.

[0049] Based on the dimensional analysis results, determine whether custom_domain is suitable for publishing a new journal. Based on the comprehensive results of each dimension in analysis_custom_domain_corpus_re, determine whether custom_domain's content positioning is suitable for publishing a journal, i.e., recommended_value = {yes|neutral|no}, where yes: recommends publishing a journal, no: does not recommend publishing a journal, and neutral: does not provide any guiding suggestions. The workflow is as follows:

[0050] a) If

[0051] existing_journals_info.existing_journals_grow_trend_by_year={negative}, recommended_value={no};

[0052] b) If

[0053] existing_journals_info.existing_journals_grow_trend_by_year={neutral},

[0054] recommended_value={neutral};

[0055] c) If

[0056] existing_journals_info.existing_journals_grow_trend_by_year={positive}:

[0057] i.If doc_info.docs_grow_trend_by_year={negative}or

[0058] author_info.authors_grow_trend_by_year={negative},

[0059] recommended_value={no};

[0060] ii.If doc_info.docs_grow_trend_by_year={neutral}and

[0061] author_info.authors_grow_trend_by_year={neutral},

[0062] recommended_value={neutral};

[0063] iii. If docs_info.doc_counts<100or author_info.authors_counts<100, recommended_value={no};

[0064] iv. Otherwise, recommended_value = {yes};

[0065] The specific embodiments are as follows:

[0066] 1) User input keywords: keywords = ["COVID-19", "new coronavirus", "epidemic"] The processing flow is as follows:

[0067] a. Get the user's preset journal domain custom_domain: Based on the keyword query topic_service and user collaborative selection, custom_domain = ["T.16_Novel Coronavirus; COVID-19; SARS-CoV-2; Pneumonia; Nucleic Acid Testing; 2019-nCoV","T.1618_Nurse; Qualitative Research; Coronavirus; Stress, Psychology; Clinical Frontline; Stress Tolerance","T.1025_Network Pharmacology; Novel Coronavirus Pneumonia (COVID-19); Potential Mechanism of Action; 3Cl Hydrolase; Antiviral; Stigmasterol"];

[0068] b. Perform multi-dimensional analysis on the literature and information corresponding to custom_domain:

[0069] Document growth rate = 68.87 Author growth rate = 80.7 Journal growth rate = 10.125

[0070] analysis_custom_domain_corpus_re={

[0071] doc_info:{docs_counts:3904,docs_grow_trend_by_year:{positive}},

[0072] author_info:{authors_counts:19119,

[0073] authors_trend_rate_by_year:{positive}},

[0074] existing_journals_info:{existing_journals_grow_trend_by_year:{positive}}

[0075] }.

[0076] c. Provide an indicator of whether custom_domain is suitable for publishing a new journal: Based on the value in analysis_custom_domain_corpus_re and steps c)->iv in the workflow of this module, recommended_value = {yes}.

[0077] 2) User enters keywords: keywords = [“intelligent manufacturing”]

[0078] The processing flow is as follows:

[0079] a) Obtain the user-preset journal domain custom_domain: Based on the keyword query topic_service and user collaborative selection, custom_domain = ["T.1095_Intelligent Manufacturing; Model-Based Definition; Maturity Model; Product Breakdown Structure; Single Data Source; Aircraft Final Assembly Pulse Production","T.755_Intelligent Manufacturing; Industry 4.0; Social Cyber-Physical Systems; Made in China 2025; Integrated Development; Human-Machine Symbiosis"];

[0080] b) Perform multi-dimensional analysis on the literature and information corresponding to custom_domain:

[0081] Document growth rate = 0.136 Author growth rate = 0.197 Journal growth rate = 0.875

[0082] analysis_custom_domain_corpus_re={

[0083] doc_info:{docs_counts:278,docs_grow_trend_by_year:{positive}},

[0084] author_info:{authors_counts:910,authors_trend_rate_by_year:{positive}},

[0085] existing_journals_info:{existing_journals_grow_trend_by_year:{positive}}

[0086] }.

[0087] c) Provide an indicator of whether custom_domain is suitable for publishing a new journal: Based on the value in analysis_custom_domain_corpus_re and steps c)->iv in the workflow of this module, recommended_value = {yes}.

[0088] 3) User enters keywords: keywords = [“non-small cell lung cancer”]

[0089] The processing flow is as follows:

[0090] a) Obtain user-preset journal domain custom_domain: Based on keyword query topic_service and user collaborative selection, custom_domain = ["T.1342_Non-small cell lung cancer; e-cadherin; cdo1; wild-type p53-induced phosphatase 1; zinc finger protein 217; hey1","T.65_Non-small cell lung cancer; pemetrexed; chemotherapy; thoracoscopic surgery; lobectomy; radiation pneumonitis","T.668_Non-small cell lung cancer; a549 cells; migration; transwell; epithelial-mesenchymal transition; NSCLC"];

[0091] b) Perform multi-dimensional analysis on the literature and information corresponding to custom_domain:

[0092] Document growth rate = -0.02 Author growth rate = -0.03 Journal growth rate = 0.125

[0093] analysis_custom_domain_corpus_re={

[0094] doc_info:{docs_counts:2057,docs_grow_trend_by_year:{neutral}},

[0095] author_info:{authors_counts:910,authors_trend_rate_by_year:{neutral}},

[0096] existing_journals_info:{existing_journals_grow_trend_by_year:{positive}}

[0097] }.

[0098] c) Provide an indicator of whether custom_domain is suitable for publishing a new journal: Based on the value in analysis_custom_domain_corpus_re and step c)->ii in the workflow of this module, recommended_value = {neutral}.

[0099] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A method for optimizing journal positioning through human-computer collaboration, characterized in that: include: S1 obtains the user's preset journal domain custom_domain; S2 performs multi-dimensional analysis on the literature and belonging information corresponding to custom_domain; S3 gives an indicator of whether custom_domain is suitable for publishing a new journal based on the results of multi-dimensional analysis; Said S1 is to search the Topic service topic_service according to a series of keywords topic_keywords preset by the user, return topic_list in descending order of relevance, and then collaboratively select n topics as the journal field preset by the user; S2 is to extract the literature of corresponding topics from the Topic database according to custom_domain as the data set custom_domain_corpus in this field, and analyze the indicators analysis_custom_domain_corpus_re of each dimension of custom_domain_corpus; The indicators of each dimension include document doc_info, author author_info and journal existing_journals_info. Among them, doc_info and author_info are used to analyze whether the content in the preset field can support and is suitable for publishing a journal, and existing_journals_info is used to analyze the value of publishing a journal in the preset field. Analyze the custom_domain data set and its associated information from the three dimensions of doc_info, author_info, and existing_journals_info, and present the quantity and trend of each dimension, i.e., the various indicators of analysis_custom_domain_corpus_re; doc_info: Document count docs_counts: Document count in the past n years, where n is 5 years. According to statistics, the minimum document count for a journal is 100. If docs_counts is less than 100, the journal is not suitable for publication; otherwise, it is suitable. Document growth trend by year docs_grow_trend_by_year: Literature growth rate: (number of documents in the last year - number of documents in the first year) / (number of documents in the first year * (n-1)). If only 5 years of documents are taken, then n = 5 years. The values are as follows: Positive: Literature growth rate >= 0.05; neutral: the literature growth rate is greater than -0.05 and the literature growth rate is less than 0.05; negative: literature growth rate <= -0.05; author_info: Author count authors_counts: The total number of authors included in the document collection. According to statistics, the average number of authors in a journal is 100. If authors_counts is less than 100, it is not suitable for publishing a journal, otherwise it is suitable; Authors' annual growth trend authors_grow_trend_by_year: Author growth rate: (number of authors in the last year of the paper - number of authors in the first year of the paper) / (number of authors in the first year of the paper * (n-1)). If only 5 years of papers are used, then n = 5 years. The values are as follows: Positive: author growth rate >= 0.05; neutral: author growth rate > -0.05 and author growth rate < 0.05; negative: author growth rate <= -0.05; existing_journals_info: The annual growth trend of the number of journals existing_journals_grow_trend_by_year: A positive value reflects that the field is worth publishing journals; Periodical growth rate: (number of periodicals in the last year - number of periodicals in the first year) / (number of periodicals in the first year * (n-1)). If only 5 years of periodicals are used, then n = 5 years. The values are as follows: Positive: journal growth rate >= 0.05; neutral: journal growth rate > -0.05 and journal growth rate < 0.05; negative: journal growth rate <= -0.05; The module returns the result: analysis_custom_domain_corpus_re={ doc_info:{docs_counts:value_int, docs_grow_trend_by_year:{positive|neutral|negative}}, author_info:{authors_counts:value_int, authors_grow_trend_by_year:{positive|neutral|negative}}, existing_journals_info:{existing_journals_grow_trend_by_year:{positive|neutral|negative}} }； Based on the comprehensive results of each dimension in analysis_custom_domain_corpus_re, the content positioning of custom_domain is determined to be suitable for publication, that is, recommended_value = {yes|neutral|no}, where yes: recommends publication, no: does not recommend publication, and neutral: does not provide any guiding suggestions; specifically, it includes: a) If existing_journals_info.existing_journals_grow_trend_by_year={negative}, recommended_value={no}; b) If existing_journals_info.existing_journals_grow_trend_by_year={neutral }， recommended_value = {neutral}; c) If existing_journals_info.existing_journals_grow_trend_by_year = {positive}: i. If doc_info.docs_grow_trend_by_year = {negative} or author_info.authors_grow_trend_by_year = {negative}, recommended_value = {no}; ii. If doc_info.docs_grow_trend_by_year = {neutral} and author_info.authors_grow_trend_by_year = {neutral}, recommended_value = {neutral}; iii. If docs_info.doc_counts < 100 or author_info.authors_counts < 100, recommended_value = {no}; Otherwise, recommended_value = {yes}.

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