| 1. |
- Rioux, John D., et al.
(författare)
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Genetic variation in the 5q31 cytokine gene cluster confers susceptibility to Crohn disease
- 2001
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 29:2, s. 223-228
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Tidskriftsartikel (refereegranskat)abstract
- Linkage disequilibrium (LD) mapping provides a powerful method for fine-structure localization of rare disease genes, but has not yet been widely applied to common disease1. We sought to design a systematic approach for LD mapping and apply it to the localization of a gene (IBD5) conferring susceptibility to Crohn disease. The key issues are: (i) to detect a significant LD signal (ii) to rigorously bound the critical region and (iii) to identify the causal genetic variant within this region. We previously mapped the IBD5 locus to a large region spanning 18 cM of chromosome 5q31 (P<10−4). Using dense genetic maps of microsatellite markers and single-nucleotide polymorphisms (SNPs) across the entire region, we found strong evidence of LD. We bound the region to a common haplotype spanning 250 kb that shows strong association with the disease (P<2×10−7) and contains the cytokine gene cluster. This finding provides overwhelming evidence that a specific common haplotype of the cytokine region in 5q31 confers susceptibility to Crohn disease. However, genetic evidence alone is not sufficient to identify the causal mutation within this region, as strong LD across the region results in multiple SNPs having equivalent genetic evidence—each consistent with the expected properties of the IBD5 locus. These results have important implications for Crohn disease in particular and LD mapping in general.
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| 2. |
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| 3. |
- Cohen, Katie, et al.
(författare)
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Linguistic markers of a radicalized mind-set among extreme adopters
- 2017
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Ingår i: Proc. 10th ACM International Conference on Web Search and Data Mining. - New York : ACM Press. - 9781450346757 ; , s. 823-824
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Konferensbidrag (refereegranskat)abstract
- The words that we use when communicating in social media can reveal how we relate to ourselves and to others. For instance, within many online communities, the degree of adaptation to a community-specific jargon can serve as a marker of identification with the community. In this paper we single out a group of so called extreme adopters of community-specific jargon from the whole group of users of a Swedish discussion forum devoted to the topics immigration and integration. The forum is characterized by a certain xenophobic jargon, and we hypothesize that extreme adopters of this jargon also exhibit certain linguistic features that we view as markers of a radicalized mind-set. We use a Swedish translation of LIWC (linguistic inquiry word count) and find that the group of extreme adopters differs significantly from the whole group of forum users regarding six out of seven linguistic markers of a radicalized mind-set.
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| 4. |
- Isbister, Tim, et al.
(författare)
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Gender Classification with Data Independent Features in Multiple Languages
- 2017
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Ingår i: 2017 European Intelligence and Security Informatics Conference (EISIC). - : IEEE. - 9781538623855 ; , s. 54-60
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Konferensbidrag (refereegranskat)abstract
- Gender classification is a well-researched problem, and state-of-the-art implementations achieve an accuracy of over 85%. However, most previous work has focused on gender classification of texts written in the English language, and in many cases, the results cannot be transferred to different datasets since the features used to train the machine learning models are dependent on the data. In this work, we investigate the possibilities to classify the gender of an author on five different languages: English, Swedish, French, Spanish, and Russian. We use features of the word counting program Linguistic Inquiry and Word Count (LIWC) with the benefit that these features are independent of the dataset. Our results show that by using machine learning with features from LIWC, we can obtain an accuracy of 79% and 73% depending on the language. We also, show some interesting differences between the uses of certain categories among the genders in different languages.
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| 5. |
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| 6. |
- Kaati, Lisa, et al.
(författare)
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En studie i fördom : Om rasistiska stereotyper i digitala miljöer
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- I denna rapport studeras uttryck för rasism i digitala miljöer, i första hand fördomar – det vill säga ogrundade negativa värderingar av individer baserade på deras grupptillhörighet – mot de minoritetsgrupper som i Regeringens plan mot rasism, liknande former av fientlighet och hatbrott angivits som speciellt utsatta. Bland de inlägg som nämnt etniska minoriteter och publicerats på diskussionsforumet Reddit från januari till juli 2022, innehöll 16 % fördomar och ytterligare 3% andra former av negativa attityder mot etniska minoriteter. De fördomar som uttrycktes kunde indelas i fyra kategorier: Föreställningar om grupper som (1) skapar oro eller oreda i samhället , (2) har odemokratiska eller föråldrade värderingar, (3) inte kan integreras eller (4) väljer att försörja sig på bidrag eller ekonomisk brottslighet framför arbete. Vår undersökning visar att muslimer och andra personer med ursprung i Mellanöstern och Nordafrika är särskilt utsatta för andras fördomar. Fördomsfulla inlägg innehåller ofta okunskap om och sammanblandning av olika etniska och religiösa minoriteter. Eftersom undersökningen gjordes våren 2022 har vi testat resultaten mot mediebevakade händelser under tidsperioden. En kraftig ökning av islamofobiska uttryck sammanföll med rapporteringen om de så kallade påskupploppen i april 2022.
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| 9. |
- Shrestha, Amendra, et al.
(författare)
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A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities
- 2017
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Ingår i: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). - : IEEE. - 9781538610510 ; , s. 1-5
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this study we try to identify extreme adopters on a discussion forum using machine learning. An extreme adopter is a user that has adopted a high level of a community-specific jargon and therefore can be seen as a user that has a high degree of identification with the community. The dataset that we consider consists of a Swedish xenophobic discussion forum where we use a machine learning approach to identify extreme adopters using a number of linguistic features that are independent on the dataset and the community. The results indicates that it is possible to separate these extreme adopters from the rest of the discussants on the discussion forum with more than 80% accuracy. Since the linguistic features that we use are highly domain independent, the results indicates that there is a possibility to use this kind of techniques to identify extreme adopters within other communities as well.
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| 10. |
- Shrestha, Amendra, 1986-, et al.
(författare)
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Extreme adopters in digital communities.
- 2020
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Ingår i: Journal of Threat Assessment and Management. - : American Psychological Association (APA). - 2169-4850 .- 2169-4842. ; 7, s. 72-84
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Tidskriftsartikel (refereegranskat)abstract
- The words that we use when communicating on social media carry information about how we relate to ourselves and others. For instance, within many online communities, the degree of adaptation to a community-specific jargon can serve as a marker of identification with the community. In this work, we define a set of linguistic features that we view as markers of a radicalized mindset, defined as a way of understanding and relating to the world that has often been observed among violent extremists. We single out a group of so-called extreme adopters of community-specific jargon from the whole group of users of a Swedish discussion forum devoted to topics related to immigration and integration. The forum is characterized by a xenophobic jargon, and we hypothesized that forum users that exhibit this particular jargon also exhibit certain other linguistic features that we regard as markers of a radicalized mindset. Using a Swedish translation of Linguistic Inquiry and Word Count to measure these linguistic features, we found that the group of extreme adopters differed significantly from the whole group of forum users regarding six out of seven linguistic markers of a radicalized mindset. We also used a machine learning approach to identify forum users with a radicalized mindset. The results indicate that it is possible to separate these individuals from the rest of the discussants on the discussion forum with more than 85% accuracy. Since the linguistic features that we used are domain independent, the results indicate a possibility to use this kind of technique to identify individuals with a radicalized mindset within other digital communities as well. These findings are relevant in threat assessment of digital communities, where computerized techniques are crucial to ease the burden of analysts by minimizing the number of individuals that need to be assessed manually. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
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