48 research outputs found
Women are warmer but no less assertive than men: gender and language on Facebook
Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and—contrary to previous findings—slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale.This work was supported by the Templeton Religion Trust
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Living in the past, present, and future: measuring temporal orientation with language
OBJECTIVE: Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. METHOD: Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female, aged 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. RESULTS: Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. CONCLUSION: Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships. This article is protected by copyright. All rights reserved.Support for this article was provided by grant #63597 from the Robert Wood Johnson Foundation (M. E. P. Seligman, PI) and by a grant from the Templeton Religion Trust (M.E.P. Seligman, H. A. Schwartz, L. H. Ungar, co-PIs)
Personality, gender, and age in the language of social media: the open-vocabulary approach
We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or 'boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personalit
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The language of religious affiliation: social, emotional, and cognitive differences
Religious affiliation is an important identifying characteristic for many individuals and relates to numerous life outcomes including health, well-being, policy positions, and cognitive style. Using methods from computational linguistics, we examined language from 12,815 Facebook users in the United States and United Kingdom who indicated their religious affiliation. Religious individuals used more positive emotion words (β = .278, p < .0001) and social themes such as family (β = .242, p < .0001), while nonreligious people expressed more negative emotions like anger (β = −.427, p < .0001) and categories related to cognitive processes, like tentativeness (β = −.153, p < .0001). Nonreligious individuals also used more themes related to the body (β = −.265, p < .0001) and death (β = −.247, p < .0001). The findings offer directions for future research on religious affiliation, specifically in terms of social, emotional, and cognitive differences
Altered patterns of cortical activation in ALS patients during attention and cognitive response inhibition tasks
Since amyotrophic lateral sclerosis (ALS) can be accompanied by executive dysfunction, it is hypothesised that ALS patients will have impaired performance on tests of cognitive inhibition. We predicted that ALS patients would show patterns of abnormal activation in extramotor regions when performing tests requiring the inhibition of prepotent responses (the Stroop effect) and the inhibition of prior negatively primed responses (the negative priming effect) when compared to healthy controls. Functional magnetic resonance imaging was used to measure activation during a sparse sequence block design paradigm investigating the Stroop and negative priming effects in 14 ALS patients and 8 healthy age- and IQ-matched controls. Behavioural measures of performance were collected. Both groups’ reaction times (RTs) reflected the Stroop effect during scanning. The ALS and control groups did not differ significantly for any of the behavioural measures but did show significant differences in cerebral activation during both tasks. The ALS group showed increased activation predominantly in the left middle temporal gyrus (BA 20/21), left superior temporal gyrus (BA 22) and left anterior cingulate gyrus (BA 32). Neither group’s RT data showed clear evidence of a negative priming effect. However the ALS group showed decreased activation, relative to controls, particularly in the left cingulate gyrus (BA 23/24), left precentral gyrus (BA 4/6) and left medial frontal gyrus (BA 6). Greater cerebral activation in the ALS group accompanying the performance of the Stroop effect and areas of decreased activation during the negative priming comparison suggest altered inhibitory processing in ALS, consistent with other evidence of executive dysfunction in ALS. The current findings require further exploration in a larger study
The personal and contextual contributors to school belongingness among primary school students
School belongingness has gained currency among educators and school health professionals as an important determinant of adolescent health. The current cross-sectional study presents the 15 most significant personal and contextual factors that collectively explain 66.4% (two-thirds) of the variability in 12-year old students' perceptions of belongingness in primary school. The study is part of a larger longitudinal study investigating the factors associated with student adjustment in the transition from primary to secondary school. The study found that girls and students with disabilities had higher school belongingness scores than boys, and their typically developing counterparts respectively; and explained 2.5% of the variability in school belongingness. The majority (47.1% out of 66.4%) of the variability in school belongingness was explained by student personal factors, such as social acceptance, physical appearance competence, coping skills, and social affiliation motivation; followed by parental expectations (3% out of 66.4%), and school-based factors (13.9% out of 66.4%) such as, classroom involvement, task-goal structure, autonomy provision, cultural pluralism, and absence of bullying. Each of the identified contributors of primary school belongingness can be shaped through interventions, system changes, or policy reforms
Mio-Pliocene Faunal Exchanges and African Biogeography: The Record of Fossil Bovids
The development of the Ethiopian biogeographic realm since the late Miocene is here explored with the presentation and review of fossil evidence from eastern Africa. Prostrepsiceros cf. vinayaki and an unknown species of possible caprin affinity are described from the hominid-bearing Asa Koma and Kuseralee Members (∼5.7 and ∼5.2 Ma) of the Middle Awash, Ethiopia. The Middle Awash Prostrepsiceros cf. vinayaki constitutes the first record of this taxon from Africa, previously known from the Siwaliks and Arabia. The possible caprin joins a number of isolated records of caprin or caprin-like taxa recorded, but poorly understood, from the late Neogene of Africa. The identification of these two taxa from the Middle Awash prompts an overdue review of fossil bovids from the sub-Saharan African record that demonstrate Eurasian affinities, including the reduncin Kobus porrecticornis, and species of Tragoportax. The fossil bovid record provides evidence for greater biological continuity between Africa and Eurasia in the late Miocene and earliest Pliocene than is found later in time. In contrast, the early Pliocene (after 5 Ma) saw the loss of any significant proportions of Eurasian-related taxa, and the continental dominance of African-endemic taxa and lineages, a pattern that continues today
Association between levels of physical activity and use of medication among older women
Why are There Fewer Marsupials than Placentals? On the Relevance of Geography and Physiology to Evolutionary Patterns of Mammalian Diversity and Disparity
Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods
Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used