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Communication Methods and Measures最新文献

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Bootstrapping public entities. Domain-specific NER for public speakers 引导公共实体。针对公共发言人的特定领域 NER
IF 11.4 1区 文学 Q1 COMMUNICATION Pub Date : 2024-08-13 DOI: 10.1080/19312458.2024.2388098
Sami Nenno
Named Entity Recognition (NER) is a supervised machine learning task that finds various applications in automated content analysis, as the identification of entities is vital for understanding publ...
命名实体识别(NER)是一项有监督的机器学习任务,在自动内容分析中有着多种应用,因为实体的识别对于理解公共内容至关重要。
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引用次数: 0
On Measurement Validity and Language Models: Increasing Validity and Decreasing Bias with Instructions 关于测量有效性和语言模型:用说明提高有效性和减少偏差
IF 11.4 1区 文学 Q1 COMMUNICATION Pub Date : 2024-07-29 DOI: 10.1080/19312458.2024.2378690
Moritz Laurer, Wouter van Atteveldt, Andreu Casas, Kasper Welbers
Language models like BERT or GPT are becoming increasingly popular measurement tools, but are the measurements they produce valid? Literature suggests that there is still a relevant gap between the...
BERT 或 GPT 等语言模型正日益成为流行的测量工具,但它们所产生的测量结果是否有效?文献表明,在语言模型和语言测量之间仍然存在着相关的差距。
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引用次数: 0
Googling Politics? Comparing Five Computational Methods to Identify Political and News-related Searches from Web Browser Histories 上网搜索政治?比较从网页浏览器历史记录中识别政治和新闻相关搜索的五种计算方法
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-06-18 DOI: 10.1080/19312458.2024.2363776
Marieke van Hoof, Damian Trilling, Corine Meppelink, Judith Möller, Felicia Loecherbach
Search engines play a crucial role in today’s information environment. Yet, political and news-related (PNR) search engine use remains understudied, mainly due to the lack of suitable measurement m...
搜索引擎在当今的信息环境中发挥着至关重要的作用。然而,对政治和新闻相关(PNR)搜索引擎使用情况的研究仍然不足,主要原因是缺乏合适的测量方法。
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引用次数: 0
Message-level Claims Require Message-level Data Analyses: Aligning Claims and Evidence in Communication Research 信息层面的主张需要信息层面的数据分析:使传播研究中的主张与证据相一致
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-05-04 DOI: 10.1080/19312458.2024.2349987
Daniel J. O’Keefe
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引用次数: 0
Automatically Finding Actors in Texts: A Performance Review of Multilingual Named Entity Recognition Tools 自动查找文本中的行为者:多语言命名实体识别工具性能评测
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-03-19 DOI: 10.1080/19312458.2024.2324789
Paul Balluff, Hajo G. Boomgaarden, Annie Waldherr
Named Entity Recognition (NER) is a crucial task in natural language processing and has a wide range of applications in communication science. However, there is a lack of systematic evaluations of ...
命名实体识别(NER)是自然语言处理中的一项重要任务,在通信科学中有着广泛的应用。然而,目前还缺乏对 NER 的系统评估。
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引用次数: 0
Taking communication science and research methodology seriously 认真对待传播科学和研究方法
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-02-20 DOI: 10.1080/19312458.2024.2308369
Lijiang Shen, Ye Sun, Pascal Jürgens, Baohua Zhou, Marko Bachl
Published in Communication Methods and Measures (Vol. 18, No. 1, 2024)
发表于《传播方法与措施》(第 18 卷第 1 期,2024 年)
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引用次数: 0
Coefficient alpha and reliability of communication science measurement scales: A note on Hayes and Coutts 传播科学测量量表的α系数和可靠性:关于 Hayes 和 Coutts 的说明
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-02-12 DOI: 10.1080/19312458.2024.2302125
Tenko Raykov, George A. Marcoulides, Natalja Menold
This note intends to complement the recent discussion in Hayes and Coutts (2020) by focusing on (i) the loading equality condition for the population identity of coefficient alpha and reliability o...
本说明旨在对 Hayes 和 Coutts(2020 年)最近的讨论进行补充,重点是:(i) 系数α和信度的人口特征的载荷相等条件;(ii) 系数α和信度的人口特征的载荷相等条件;(iii) 系数α和信度的载荷相等条件。
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引用次数: 0
What’s in a name? The effect of named entities on topic modelling interpretability 名称中有什么?命名实体对主题建模可解释性的影响
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-01-29 DOI: 10.1080/19312458.2024.2302120
Petro Tolochko, Paul Balluff, Jana Bernhard, Sebastian Galyga, Noëlle S. Lebernegg, Hajo G. Boomgaarden
Topic Modelling has established itself as one of the major text-as-data methodologies within the social sciences in general, and in communications science, in particular. The core strength of TM ap...
主题建模(Topic Modelling)已成为社会科学领域,尤其是传播科学领域的主要文本即数据方法之一。主题建模的核心优势在于...
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引用次数: 0
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can! 自动内容分析中的分类错误会导致回归偏差。我们能解决这个问题吗?是的,我们能!
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2024-01-16 DOI: 10.1080/19312458.2023.2293713
Nathan TeBlunthuis, Valerie Hase, Chung-Hong Chan
Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become ...
自动分类器(AC)通常是通过有监督机器学习(SML)建立的,可以对从文本到图像和视频的大量统计功能强大的数据样本进行分类。它们已成为 ...
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引用次数: 0
Beyond sentiment: an algorithmic strategy for identifying evaluations within large text corpora 超越情感:在大型文本库中识别评价的算法策略
IF 11.4 1区 文学 Q1 Social Sciences Pub Date : 2023-12-07 DOI: 10.1080/19312458.2023.2285783
Maximilian Overbeck, Christian Baden, Tali Aharoni, Eedan Amit-Danhi, Keren Tenenboim-Weinblatt
In this paper, we propose a new strategy for classifying evaluations in large text corpora, using supervised machine learning (SML). Departing from a conceptual and methodological critique of the u...
在本文中,我们提出了一种使用监督机器学习(SML)对大型文本库中的评价进行分类的新策略。我们从概念和方法论角度出发,对使用监督机器学习(SML)的评价方法进行了批判。
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引用次数: 1
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Communication Methods and Measures
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