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Berggren, M., Kaati, L., Pelzer, B., Stiff, H., Lundmark, L. & Akrami, N. (2024). The Generalizability of Machine Learning Models of Personality across Two Text Domains. Personality and Individual Differences, 217, Article ID 112465.
Open this publication in new window or tab >>The Generalizability of Machine Learning Models of Personality across Two Text Domains
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2024 (English)In: Personality and Individual Differences, ISSN 0191-8869, E-ISSN 1873-3549, Vol. 217, article id 112465Article in journal (Refereed) Published
Abstract [en]

Machine learning of high-dimensional models have received attention for their ability to predict psychological variables, such as personality. However, it has been less examined to what degree such models are capable of generalizing across domains. Across two text domains (Reddit message and personal essays), compared to low-dimensional- and theoretical models, atheoretical high-dimensional models provided superior predictive accuracy within but poor/non-significant predictive accuracy across domains. Thus, complex models depended more on the specifics of the trained domain. Further, when examining predictors of models, few survived across domains. We argue that theory remains important when conducting prediction-focused studies and that research on both high- and low-dimensional models benefit from establishing conditions under which they generalize.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
machine learning, big five, LIWC, text analysis
National Category
Natural Language Processing Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-496403 (URN)10.1016/j.paid.2023.112465 (DOI)001107259700001 ()
Funder
Riksbankens Jubileumsfond, P15-0603:1
Available from: 2023-02-12 Created: 2023-02-12 Last updated: 2025-02-01Bibliographically approved
Obaidi, M., Bergh, R., Akrami, N. & Dovidio, J. F. (2024). The personality of violent Jihadists: Examining violent and nonviolent defense of Muslims. Journal of personality, 92(4), 1172-1192
Open this publication in new window or tab >>The personality of violent Jihadists: Examining violent and nonviolent defense of Muslims
2024 (English)In: Journal of personality, ISSN 0022-3506, E-ISSN 1467-6494, Vol. 92, no 4, p. 1172-1192Article in journal (Refereed) Published
Abstract [en]

Objective: Although violent extremism is often attributed to clinical (dysfunctional) dispositions, it is also possible that violent Jihadists might be clinically "normal" but bear certain personality signatures. This alternative view has yet to be tested.

Method: In six studies, employing hard-to-reach Muslim samples, including one study of former Mujahideen, we investigated the relationship between basic personality traits and violent extremism. We further used a known group paradigm to validate the personality signatures of violent extremism, comparing a sample of former Mujahideen with another sample from Afghanistan.

Results: These studies and an internal meta-analysis revealed that Lower Openness to Experience, lower Emotionality, and lower Altruism were associated with more violent intentions to support Muslims. Higher Altruism was associated with higher levels of nonviolent intention to support Muslims. Supporting the validity of the nonviolent intention measure, similar associations were found in Study 3 with overt behavioral support of Muslims (donations). More important, compared to the nonMujahideen, the Mujahideen sample scored lower on average on, for instance, Openness, indicating that these results go beyond self-reported, findings.

Conclusion: We demonstrated that personality predicts violent and nonviolent defense of Muslims among four general populations of Muslims living in the West and in Asia (including the Middle East), and a sample of Mujahideen in Afghanistan.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
Keywords
altruism, cross-cultural violent extremism, HEXACO personality, individual differences, Jihadism, violent and nonviolent behavioral intentions
National Category
Clinical Medicine Psychology
Identifiers
urn:nbn:se:uu:diva-542415 (URN)10.1111/jopy.12880 (DOI)001065132400001 ()37650306 (PubMedID)
Funder
Riksbankens Jubileumsfond, P15- 0603:1Marianne and Marcus Wallenberg Foundation
Available from: 2024-11-12 Created: 2024-11-12 Last updated: 2024-11-12Bibliographically approved
Lindström, J., Bergh, R., Akrami, N., Obaidi, M. & Lindholm Öymyr, T. (2024). Who endorses group-based violence?. Group Processes & Intergroup Relations, 27(2), 217-238
Open this publication in new window or tab >>Who endorses group-based violence?
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2024 (English)In: Group Processes & Intergroup Relations, ISSN 1368-4302, E-ISSN 1461-7188, Vol. 27, no 2, p. 217-238Article in journal (Refereed) Published
Abstract [en]

Collective action is often equated with progressive politics, but are there aspects of group mobilisations that generalise across contexts? We examine general social and personality psychological factors behind endorsement of group-based violence across different types of violent group mobilisation. Specifically, we focus on the endorsement of group-based violence amongst supporters of the Black Lives Matter (BLM) movement (N = 394), an immigration-critical group (N = 252), and soccer supporters (N = 445). Across three preregistered studies, we tested an integrative model including personality and social psychological factors. Several effects were consistent across all three contexts, with group-based relative deprivation positively, and honesty-humility negatively, predicting support for violence. Further, amongst BLM supporters and the immigration-critical group, emotionality negatively predicted support for violence, violent intentions, and self-reported aggression/violence. Overall, our results suggest that individuals who endorse violence in different contexts have some psychological factors in common.

Place, publisher, year, edition, pages
Sage Publications, 2024
National Category
Sociology (Excluding Social Work, Social Anthropology, Demography and Criminology)
Identifiers
urn:nbn:se:uu:diva-517778 (URN)10.1177/13684302231154412 (DOI)000937537200001 ()
Funder
Lars Hierta Memorial Foundation, FO2019-0005Marianne and Marcus Wallenberg Foundation, MMW 2016.0070
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2025-02-17Bibliographically approved
Kaati, L., Moshfegh, A., Linden, K., Shrestha, A. & Akrami, N. (2023). Harmful Communication Detection of Toxic Language and Threats on Swedish. In: B.A. Prakash;D. Wang;T. Weninger (Ed.), Proceedings of the 2023 IEEE/ACM International Conference on AdvancesiIn Social Networks Analysis and Mining, ASONAM 2023: . Paper presented at 15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM), November 06-09, 2023, Kusadasi, Turkey (pp. 624-630). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Harmful Communication Detection of Toxic Language and Threats on Swedish
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2023 (English)In: Proceedings of the 2023 IEEE/ACM International Conference on AdvancesiIn Social Networks Analysis and Mining, ASONAM 2023 / [ed] B.A. Prakash;D. Wang;T. Weninger, Association for Computing Machinery (ACM), 2023, p. 624-630Conference paper, Published paper (Refereed)
Abstract [en]

Harmful communication, such as toxic language and threats directed toward individuals or groups, is a common problem on most social media platforms and online spaces. While several approaches exist for detecting toxic language and threats in English, few attempts have detected such communication in Swedish. Thus, we used transfer learning and BERT to train two machine learning models: one that detects toxic language and one that detects threats in Swedish. We also examined the intersection between toxicity and threat. The models are trained on data from several different sources, with authentic social media posts and data translated from English. Our models perform well on test data with an F1-score above 0.94 for detecting toxic language and 0.86 for detecting threats. However, the models' performance decreases significantly when they are applied to new unseen social media data. Examining the intersection between toxic language and threats, we found that 20% of the threats on social media are not toxic, which means that they would not be detected using only methods for detecting toxic language. Our finding highlights the difficulties with harmful language and the need to use different methods to detect different kinds of harmful language.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Series
Proceedings of the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining, ISSN 2473-9928, E-ISSN 2473-991X
National Category
Natural Language Processing
Identifiers
urn:nbn:se:uu:diva-527133 (URN)10.1145/3625007.3627597 (DOI)001191293500099 ()9798400704093 (ISBN)
Conference
15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM), November 06-09, 2023, Kusadasi, Turkey
Funder
Riksbankens Jubileumsfond, P15-0603:1
Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2025-02-07Bibliographically approved
Kaati, L., Shrestha, A. & Akrami, N. (2023). Linguistic Alignments Detecting Similarities in Language Use in Written Communication. In: B.A. Prakash;D. Wang;T. Weninger (Ed.), Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023: . Paper presented at 15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM), November 06-09, 2023, Kusadasi, Turkey (pp. 619-623). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Linguistic Alignments Detecting Similarities in Language Use in Written Communication
2023 (English)In: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023 / [ed] B.A. Prakash;D. Wang;T. Weninger, Association for Computing Machinery (ACM), 2023, p. 619-623Conference paper, Published paper (Refereed)
Abstract [en]

Human language has many functions. Our communication on social media carries information about how we relate to ourselves and others, that is our identity, and we adjust our language to become more similar to our community - in the same way as we dress and style and act to show our commitment to the groups we belong to. Within a community, members adopt the community's language, and the common language becomes a unifying factor. In this paper, we explore the possibilities of identifying linguistic alignment - that individuals adjust their language to become more similar to their conversation partners in a community. We use machine learning to detect linguistic alignment to a number of different ideologies, communities, and subcultures. We use two different approaches: transfer learning with RoBERTa and traditional machine learning using Random forest and feature selection.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Series
Proceedings of the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining, ISSN 2473-9928, E-ISSN 2473-991X
National Category
Natural Language Processing Specific Languages General Language Studies and Linguistics
Identifiers
urn:nbn:se:uu:diva-527135 (URN)10.1145/3625007.3627594 (DOI)001191293500098 ()979-8-4007-0409-3 (ISBN)
Conference
15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM), November 06-09, 2023, Kusadasi, Turkey
Funder
Riksbankens Jubileumsfond, P15-0603:1
Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2025-02-01Bibliographically approved
Lindström, J., Bergh, R. & Akrami, N. (2023). Low modesty linked to feeling deprived within advantaged (but not disadvantaged) groups. Journal of Research in Personality, 103, Article ID 104356.
Open this publication in new window or tab >>Low modesty linked to feeling deprived within advantaged (but not disadvantaged) groups
2023 (English)In: Journal of Research in Personality, ISSN 0092-6566, E-ISSN 1095-7251, Vol. 103, article id 104356Article in journal (Refereed) Published
Abstract [en]

There is growing recognition that members of structurally advantaged groups experience group-based relative deprivation. We consider the idea that personality may explain these "entitlement-based" feelings of deprivation. Specifically, we predicted that modesty would be negatively associated with group-based relative deprivation among members of advantaged groups, but not amongst disadvantaged groups. Two studies focusing on White and Black Americans (N = 334), and Men and Women (N = 309) showed that modesty interacted with group membership. Modesty was negatively related to group-based relative deprivation amongst White Americans and men, but not amongst Black Americans and women. The findings help explain why some individuals espouse rhetoric that their group is being disfavored, even when group statistics and history suggest otherwise.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Relative deprivation, Structurally advantaged groups, Personality, Modesty
National Category
Sociology (Excluding Social Work, Social Anthropology, Demography and Criminology)
Identifiers
urn:nbn:se:uu:diva-499889 (URN)10.1016/j.jrp.2023.104356 (DOI)000944429600001 ()
Funder
Riksbankens Jubileumsfond, P15-0603:1Marianne and Marcus Wallenberg Foundation, MMW 2016.0070
Available from: 2023-04-11 Created: 2023-04-11 Last updated: 2025-02-17Bibliographically approved
Shrestha, A., Akrami, N., Kaati, L., Kupper, J. & Schumacher, M. R. (2021). Words of Suicide: Identifying Suicidal Risk in Written Communications. In: Chen, Y Ludwig, H Tu, Y Fayyad, U Zhu, X Hu, X Byna, S Liu, X Zhang, J Pan, S Papalexakis, V Wang, J Cuzzocrea, A Ordonez, C (Ed.), 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA): . Paper presented at 9th IEEE International Conference on Big Data (IEEE BigData), DEC 15-18, 2021, ELECTR NETWORK (pp. 2144-2150). Institute of Electrical and Electronics Engineers (IEEE) Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Words of Suicide: Identifying Suicidal Risk in Written Communications
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2021 (English)In: 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) / [ed] Chen, Y Ludwig, H Tu, Y Fayyad, U Zhu, X Hu, X Byna, S Liu, X Zhang, J Pan, S Papalexakis, V Wang, J Cuzzocrea, A Ordonez, C, Institute of Electrical and Electronics Engineers (IEEE) Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 2144-2150Conference paper, Published paper (Refereed)
Abstract [en]

Suicide is a global health problem with more than 700,000 individuals dying by self-destruction each year, yet it is classified as a low base rate behavior that is difficult to prognosticate. Aiming to advance suicide prediction and prevention, we examined the potential use of machine learning and text analyses models to predict suicide risk based on written communications. Specifically, we used a dataset consisting of more than 27,000 general writings unrelated to suicide, 193 genuine suicide notes from individuals who committed suicide, and an additional 89 suicide posts shared on sub-Reddits for an in-the-wild test to examine the prediction accuracy of two machine learning models (SVM & RoBERTa) and a linguistic marker model. Our tests showed that the machine learning models performed better than the linguistic marker model when examined on the test data. However, the linguistic marker model achieved higher results in the wild, correctly classifying 88% of written communications as a "high risk of suicide" versus 56% and 70% of the machine learning models. The best in-the-wild performing model was adopted in an online suicide risk assessment tool called Edwin to honor Edwin Shneidman for his numerous contributions to the field of suicidology. Finally, discrepancies between training and real-world data, vocabulary variation across domains, and the limited number of benchmarks constitute limitations that need to be addressed in future research.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE)Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
IEEE International Conference on Big Data, ISSN 2639-1589
Keywords
Suicide, machine learning, linguistic marker, RoBERTa, SVM
National Category
Psychiatry Psychology Computer Engineering
Identifiers
urn:nbn:se:uu:diva-480114 (URN)10.1109/BigData52589.2021.9671472 (DOI)000800559502033 ()978-1-6654-3902-2 (ISBN)
Conference
9th IEEE International Conference on Big Data (IEEE BigData), DEC 15-18, 2021, ELECTR NETWORK
Funder
Swedish Research Council, 2018-05973
Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2024-01-15Bibliographically approved
Shrestha, A., Akrami, N. & Kaati, L. (2020). Introducing Digital-7 Threat Assessment of Individuals in Digital Environments. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): . Paper presented at 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 720-726). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Introducing Digital-7 Threat Assessment of Individuals in Digital Environments
2020 (English)In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 720-726Conference paper, Published paper (Refereed)
Abstract [en]

One of the most challenging threats towards the security of the society is attacks from violent lone offenders, individuals that act alone or with minimal help from others without any economic gains or direct orders from organizations. Over the past few years, several terror attacks have been accompanied by manifestos published on social media platforms that outline ideology, motivation, and in some cases tactical choices. The trend in publishing manifestos and other communication on social media sites before committing an attack has increased the need for threat assessment in digital environments. Most existing methods for threat assessment are developed to be used in offline settings where information about an individual is accessible and cases where the individual is present and can answer questions. In this paper, we present seven indicators that can be used to assess the potential threat of violence based on digital communication only. The seven indicators are designed to be used when analyzing texts and can be seen as a complement to other risk assessment protocols.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Series
International Conference on Advances in Social Network Analysis and Mining, ASONAM, E-ISSN 2473-991X
National Category
Applied Psychology Computer Sciences
Identifiers
urn:nbn:se:uu:diva-444438 (URN)10.1109/ASONAM49781.2020.9381387 (DOI)000678816900114 ()978-1-7281-1056-1 (ISBN)
Conference
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Available from: 2021-06-07 Created: 2021-06-07 Last updated: 2021-08-30Bibliographically approved
Thielmann, I., Akrami, N., Babarovic, T., Belloch, A., Bergh, R., Chirumbolo, A., . . . Lee, K. (2020). The HEXACO-100 Across 16 Languages: A Large-Scale Test of Measurement Invariance. Journal of Personality Assessment, 102(5), 714-726
Open this publication in new window or tab >>The HEXACO-100 Across 16 Languages: A Large-Scale Test of Measurement Invariance
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2020 (English)In: Journal of Personality Assessment, ISSN 0022-3891, E-ISSN 1532-7752, Vol. 102, no 5, p. 714-726Article in journal (Refereed) Published
Abstract [en]

The HEXACO Personality Inventory-Revised (HEXACO-PI-R) has become one of the most heavily applied measurement tools for the assessment of basic personality traits. Correspondingly, the inventory has been translated to many languages for use in cross-cultural research. However, formal tests examining whether the different language versions of the HEXACO-PI-R provide equivalent measures of the 6 personality dimensions are missing. We provide a large-scale test of measurement invariance of the 100-item version of the HEXACO-PI-R across 16 languages spoken in European and Asian countries (N = 30,484). Multigroup exploratory structural equation modeling and confirmatory factor analyses revealed consistent support for configural and metric invariance, thus implying that the factor structure of the HEXACO dimensions as well as the meaning of the latent HEXACO factors is comparable across languages. However, analyses did not show overall support for scalar invariance; that is, equivalence of facet intercepts. A complementary alignment analysis supported this pattern, but also revealed substantial heterogeneity in the level of (non)invariance across facets and factors. Overall, results imply that the HEXACO-PI-R provides largely comparable measurement of the HEXACO dimensions, although the lack of scalar invariance highlights the necessity for future research clarifying the interpretation of mean-level trait differences across countries.

Place, publisher, year, edition, pages
Informa UK Limited, 2020
National Category
Psychology (excluding Applied Psychology)
Identifiers
urn:nbn:se:uu:diva-421230 (URN)10.1080/00223891.2019.1614011 (DOI)000475170000001 ()31184949 (PubMedID)
Available from: 2020-10-07 Created: 2020-10-07 Last updated: 2021-04-06Bibliographically approved
Bayat, J. T., Huggare, J. & Akrami, N. (2019). Distinguishing between global and dental self-esteem in evaluating malocclusions. Acta Odontologica Scandinavica, 77(6), 452-456
Open this publication in new window or tab >>Distinguishing between global and dental self-esteem in evaluating malocclusions
2019 (English)In: Acta Odontologica Scandinavica, ISSN 0001-6357, E-ISSN 1502-3850, Vol. 77, no 6, p. 452-456Article in journal (Refereed) Published
Abstract [en]

Objective: When dealing with the impact of malocclusion on self-esteem, the terms global and dental self-esteem are sometimes used. Although these terms are related to one another, they do not depict the same concept. The aims of this paper were to explore if the two forms of self-esteem are distinguishable, to find out if they represent different factors, and to investigate how they are related to malocclusion. Materials and methods: A sample consisting of 150 adolescents, aged 13 years, completed self-assessed measures of Dental and Global Self-Esteem. Orthodontic treatment need for each individual was assessed by the Dental Health Component of the Index of Orthodontic Treatment Need (IOTN-DHC). Data were analysed by factor analyses and a 5 (IOTN-DHC grades) by 2 (global vs. dental self-esteem) ANOVA, with the IOTN-DHC grades as the independent and self-esteem (repeated measure) as the dependent variables. Results: The factor analyses showed that the two forms of self-esteem, based on the measures, are distinguishable. More importantly, the results of the ANOVA revealed that Dental and Global Self-Esteem are differentially related to IOTN-DHC. Specifically, Dental Self-Esteem varied across IOTN-DHC scale while Global Self-Esteem did not. There was no effect of gender. Conclusions: Dental self-esteem is related to malocclusion while global self-esteem is not. These findings have implications in areas where the predictive power of dental self-esteem needs to be considered.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS LTD, 2019
Keywords
Adolescent, dental self-esteem, global self-esteem, malocclusion, self-assessed measures
National Category
Dentistry
Identifiers
urn:nbn:se:uu:diva-396080 (URN)10.1080/00016357.2019.1588371 (DOI)000463512900001 ()30905235 (PubMedID)
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved
Projects
The Implicit Association Test: Generalized Implicit Prejudice, Personality and Discrimination [2008-02319_VR]; Uppsala UniversityPersonlighet, Fördomsfullhet och Diskriminering: Teoretiska och Metodologiska Konsekvenser av en Explicit-Implicit Distinktion [2011-01891_VR]; Uppsala UniversityOn radicalization and extremism: What brings ordinary people into extreme situations? [P15-0603:1_RJ]; Uppsala University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9641-6275

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