Moralization in social networks and the emergence of violence during protests

Mooijman, Marlon; Hoover, Joe; Lin, Ying; Ji, Heng; Dehghani, Morteza

doi:10.1038/s41562-018-0353-0

Cited by 162 publications

(166 citation statements)

References 42 publications

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“…In a study more similar to ours, attempting to predict moral values involved in Twitter posts, Lin et al [40] proposed a method that automatically acquires background knowledge to improve the moral value prediction, pointing out the difficulty of the task also for human experts. Based on the work of [40] and [10], [9] predicted the moral sentiment of the tweets. Their model consists of three layers, an embedding (lookup) layer, a recurrent neural network (RNN) with long short-term memory (LSTM) [41] and an output layer.…”

Section: Related Literaturementioning

confidence: 99%

“…An older version of this dataset exists which contains a more significant number of instances. Nevertheless, since the annotations of this older version are obtained by automated means [9], we have decided to use the newer version, which has manual annotations.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…They are also closely related to our political views [5] and the opinion formation mechanisms regarding immigration [6], political extremism [7,1], and poverty [8]. Recently, scientists also showed that moral values could be employed to detect violent protests [9] based on user-generated text.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

Araque

Gatti

Kalimeri

2020

Knowledge-Based Systems

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Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on Word-Net synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value< 0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues.

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Section: Related Literaturementioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

Araque

Gatti

Kalimeri

2020

Knowledge-Based Systems

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“…Third, the proposed dictionaries and LIWC software enable analyses of large quantities of data, where manual coding would be highly time-consuming (Neuendorf, 2011;Rudy, Popova, & Linz, 2010). Recently, text analysis started to play a significant role in psychological research (e.g., Eichstaedt et al, 2015;Gustafsson Send en et al, 2014;Mooijman, Hoover, Lin, Ji, & Dehghani, 2018;Van Swol & Kane, 2018), for instance, in reliably evaluating an individual's personality, status, or mood just based on the use of pronouns (see Campbell & Pennebaker, 2003;Pennebaker, 2011). Thus, analyzing 400,000 articles (a dataset released by Reuters for research and the development of natural language processing) would take 400,000 hours, which would require 200 full-time coders working for a year if every article were analyzed for an hour (based on 2,000 working hours per year; for details see Gustafsson Send en, Sikstr€ om, & Lindholm, 2015).…”

Section: Future Researchmentioning

confidence: 99%

The big two dictionaries: Capturing agency and communion in natural language

Pietraszkiewicz

Formanowicz

Sendén

et al. 2019

Euro J Social Psych

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Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency‐based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male‐dominated jobs were advertised with more agentic than communal words, and female‐dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion.

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“…He specifically wrote that: “But not to perish of internal distress and uncertainty when one inflicts great suffering and hears the cry of this suffering—that is great, that belongs to greatness” (Nietzsche, , p. 325). These factors could also help group members more easily moralise and therefore justify violent intergroup behaviour (see Mooijman, Hoover, Lin, Ji, & Dehghani, , for an example in the context of crowd protests).…”

Section: Towards An Integrative Model Of the Internalisation Of Intermentioning

confidence: 99%

Bringing together humanistic and intergroup perspectives to build a model of internalisation of normative social harmdoing

Amiot

Lizzio‐Wilson

Thomas

et al. 2020

Euro J Social Psych

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This article introduces a model of the internalisation of normative social harmdoing: the MINSOH. This model seeks to explain how group members internalise harmful social norms such that they personally endorse their groups' normative actions. To this aim, the MINSOH integrates two divergent yet complementary theoretical perspectives: self‐determination theory and the social identity approach. These perspectives differ in their basic assumptions about the possibility for harm to become internalised, yet when integrated, they provide a powerful account of how harmdoing can become internalised. The MINSOH proposes specific conditions under which harmful normative actions become accepted by group members. This article outlines multiple self‐determined motivations for harmdoing and discrete group processes that enable harmdoing to be internalised and autonomously enacted, and reviews factors that facilitate (i.e., strong/unique/comparative social identification; endorsement of ideological justifications) and block the internalisation process (presence of multiple identities/diverging norms; inclusive superordinate identity). Directions for future research are then discussed.

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Moralization in social networks and the emergence of violence during protests

Cited by 162 publications

References 42 publications

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

The big two dictionaries: Capturing agency and communion in natural language

Bringing together humanistic and intergroup perspectives to build a model of internalisation of normative social harmdoing

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