Effects of Frame Repetition Through Cues in the Online Environment

Liu, Jiawei; Lee, ByungGu; McLeod, Douglas M.; Choung, Hyesun

doi:10.1080/15205436.2018.1560475

Cited by 10 publications

(10 citation statements)

References 44 publications

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“…The sentiment of headlines, however, is interesting in its own right as the headline is the first (and sometimes the only) part of an article that people read, and headlines shape the context in which people read the article. In social science research, headlines are seen as an important framing device (e.g., Liu et al, 2019;Tankard et al, 2001), a signal of news values (e.g., Ng & Zhao, 2020) and as an important predictor of effects on polarization, political attitudes, and consumer confidence (e.g., Blood & Phillips, 1995;Munger et al, 2020;Narayan & Narayan, 2017). Moreover, many other forms of communication that are studied in our field (and with automated content analysis), such as tweets, chat messages, and online comments, are also relatively short.…”

mentioning

confidence: 99%

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

Atteveldt

Velden

Boukes

2021

Communication Methods and Measures

197

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Sentiment is central to many studies of communication science, from negativity and polarization in political communication to analyzing product reviews and social media comments in other sub-fields. This study provides an exhaustive comparison of sentiment analysis methods, using a validation set of Dutch economic headlines to compare the performance of manual annotation, crowd coding, numerous dictionaries and machine learning using both traditional and deep learning algorithms. The three main conclusions of this article are that: (1) The best performance is still attained with trained human or crowd coding; (2) None of the used dictionaries come close to acceptable levels of validity; and (3) machine learning, especially deep learning, substantially outperforms dictionary-based methods but falls short of human performance. From these findings, we stress the importance of always validating automatic text analysis methods before usage. Moreover, we provide a recommended step-bystep approach for (automated) text analysis projects to ensure both efficiency and validity.

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mentioning

confidence: 99%

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

Atteveldt

Velden

Boukes

2021

Communication Methods and Measures

197

View full text Add to dashboard Cite

show abstract

“…To estimate the effect of a frame through an experiment, researchers typically manipulate the frame employed in the message and observe the effects. This message could take a variety of forms, including news articles (Su et al, 2019), headlines (Liu et al, 2019b), editorials and reader comments (Holton et al, 2014;. Regardless of the form, one of the biggest challenges of using narrative frames is to be able to confidently identify the frame itself, which we have discussed in the Enduring Issues and Semantic Architecture Model sections.…”

Section: Narrative Framesmentioning

confidence: 99%

“…Issue involvement and personal relevance may moderate not only the main effects of frames, but also the nature of frame interaction effects. For example, Liu et al (2019b) found that cue interactions were moderated by personal relevance. Cues embedded in recommended headlines that repeat an editorial's frame reinforce the original frame's effects.…”

Section: Issue Involvement and Issue Relevancementioning

confidence: 99%

Navigating a Diverse Paradigm: A Conceptual Framework for Experimental Framing Effects Research

McLeod

Choung

et al. 2022

rev commun res

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This review introduces a conceptual framework with three elements to highlight the richness of the framing effects literature, while providing structure to address its fragmented nature. Our first element identifies and discusses the Enduring Issues that confront framing effects researchers. Second, we introduce the Semantic Architecture Model (SAM), which builds on the premise that meaning can be framed at different textual units within a text, which can form the basis of frame manipulations in framing effects experiments. Third, we provide an Inventory of Framing Effects Research Components used in framing effects research illustrated with salient examples from the framing effects literature. By offering this conceptual framework, we make the case for revitalizing framing effects research.

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“…Эффект фрейминга (Tversky, Kahneman, 1986), который заключается в • том, что результат выбора зависит от представления выбора как вопроса о выигрыше или о проигрыше (Liu et al, 2019). Избегание потерь (Tversky, Kahneman, 1991), которое заключается в том,…”

Section: когнитивные искажения в цифровой средеunclassified

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2020

psychology

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Учре ди тель Национальный исследовательский уни вер си тет «Вы сшая шко ла эко но ми ки» Главный редактор В.А. Петровский (НИУ ВШЭ) Редакционная коллегия Дж. Берри (Университет Куинс, Кана да) Г.М. Бреслав (Балтийская международная академия, Латвия) Я. Вальсинер (Ольборгский университет, Дания) Е.Л. Григоренко (МГУ им. М.В. Ло мо но сова и Центр ребенка Йельского университета, США) В.А. Ключарев (НИУ ВШЭ) Д.А. Леонтьев (НИУ ВШЭ и МГУ им. М.В. Ломоносова) В.Е. Лепский (ИФ РАН) М. Линч (Рочестерский университет, США) Д.В. Люсин (НИУ ВШЭ и ИП РАН) Е.Н. Осин (НИУ ВШЭ) А.Н. Поддьяков (НИУ ВШЭ) Е.Б. Старовойтенко (НИУ ВШЭ) Д.В. Ушаков (зам. глав. ред.) (ИП РАН) М.В. Фаликман (НИУ ВШЭ) А.В. Хархурин (НИУ ВШЭ) В.Д. Шадриков (зам. глав. ред.) (НИУ ВШЭ) С.Р. Яголковский (зам. глав. ред.) (НИУ ВШЭ) Экспертный совет К.А. Абульханова-Славская (НИУ ВШЭ и ИП РАН) Н.А. Алмаев (ИП РАН) В.А. Барабанщиков (ИП РАН и МГППУ) Т.Ю. Базаров (НИУ ВШЭ и МГУ им. М.В. Ломоносова) А.К. Болотова (НИУ ВШЭ) А.Н. Гусев (МГУ им. М.В. Ломоносова) А.Л. Журавлев (ИП РАН) А.В. Карпов (Ярославский государст венный университет им. П.Г. Демидова) П. Лучисано (Римский университет Ла Сапиенца, Италия) А. Лэнгле (НИУ ВШЭ) А.Б. Орлов (НИУ ВШЭ) В.Ф. Петренко (МГУ им. М.В. Ломо но сова) В.М. Розин (ИФ РАН) И.H. Семенов (НИУ ВШЭ) Е.А. Сергиенко (ИП РАН) Т.Н. Ушакова (ИП РАН) А.М. Черноризов (МГУ им. М.В. Ломо но со ва) А.Г. Шмелев (МГУ им. М.В. Ломо но сова) П. Шмидт (НИУ ВШЭ и Гиссенский университет, Германия)

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Effects of Frame Repetition Through Cues in the Online Environment

Cited by 10 publications

References 44 publications

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

Navigating a Diverse Paradigm: A Conceptual Framework for Experimental Framing Effects Research

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