Constructing personality and social reality through music: Individual differences among fans of punk and heavy metal music

Hansen, Christine H.; Hansen, Ranald D.

doi:10.1080/08838159109364129

Cited by 85 publications

(89 citation statements)

References 31 publications

(38 reference statements)

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“…Results showing correlations between personality and music taste are well established in the literature [40,11,18,39]. Inferring personality traits from Facebook profiles may allow recommender systems to improve their accuracy by recommending music, and possibly other items, that are tailored to the user's personality profile.…”

Section: Discussionmentioning

confidence: 65%

Predicting personality with social media

Golbeck

Robles

Turner

2011

CHI '11 Extended Abstracts on Human Factors in Computing Systems

462

277

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Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately guage users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.

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Section: Discussionmentioning

confidence: 65%

Predicting personality with social media

Golbeck

Robles

Turner

2011

CHI '11 Extended Abstracts on Human Factors in Computing Systems

462

277

View full text Add to dashboard Cite

show abstract

“…Significant correlations were also found between personality and preferences. Studies showing connections between personality and music taste are well established in literature [11][12][13][14], while Jost et al [15] found that knowing the personality traits of an individual allowed to predict whether they would be more likely to vote for McCain or Obama in the presidential election of 2008 in the United States. Cantador et al [16] presented a preliminary study in which they analyzed preferences of roughly 50,000 Facebook users who expressed their interests about sixteen genres in each of these domains: movies, TV shows, music and books.…”

Section: Related Workmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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Abstract:We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey.

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“…There is a great deal of literature, both research-based clinical work (often using clinical samples of young people who experience self harm) and more generic literature, which purports to show these relationships, so we present only a sampler of the views and findings here, which will show the tenor of this work. Hansen and Hansen (1991) describe heavy metal fans as being more Machiavellian, less needful of cognition, more likely to show machismo, and likely to make higher estimates of antisocial behaviour among the population as a whole than non-fans. Roberts et al, agree that heavy metal music preference in adolescents, when combined with higher levels of negative emotions, can result in increased risk-taking behaviour.…”

mentioning

confidence: 98%

Suicide, Self-Harm and Survival Strategies in Contemporary Heavy Metal Music: A Cultural and Literary Analysis

Baker

Brown

2014

J Med Humanit

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This paper seeks to think creatively about the body of research which claims there is a link between heavy metal music and adolescent alienation, self-destructive behaviours, selfharm and suicide. Such research has been criticised, often by people who belong to heavy metal subcultures, as systematically neglecting to explore, in a meaningful manner, the psychosocial benefits for individuals who both listen to contemporary heavy metal music and socialize in associated groups. We argue that notions of survival, strength, community, and rebellion are key themes in contemporary heavy metal music. Through literary-lyrical analysis of a selection of heavy metal tracks, this paper aims to redress the balance of risk and benefit. We argue that listening to this type of music, the accompanying social relationships, sense of solidarity and even the type of dancing can ameliorate tumultuous and difficult emotions. Songs which could be read as negative can induce feelings of relief through the sense that someone else has felt a particular way and recovered enough to transform these emotions into a creative outlet. This genre of music may therefore not increase the risk of untoward outcomes in any simple sense but rather represent a valuable resource for young people in difficulty.Keywords Self-harm . Suicide . Heavy metal music . Subcultures . Adolescence . Alienation This paper sets out to address the oft-cited relationship between heavy metal music and adolescent self-harm. Whilst frequently assumed, this alleged relationship originates in stereotypes held by parents, educators, policymakers and researchers and is often only tenuously supported by study of young people themselves. As Martin Barker (1984) has pointed out, each generation of new media has been interpreted by many social commentators as being potentially harmful and apt to have a particularly pernicious effect on children. The rise of mass literacy in the 19 th century was accompanied by concerns about the effects of 'penny dreadfuls', cinema yielded concerns about potential delinquency and deteriorations in young J Med Humanit

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Constructing personality and social reality through music: Individual differences among fans of punk and heavy metal music

Cited by 85 publications

References 31 publications

Predicting personality with social media

Predicting personality with social media

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

Suicide, Self-Harm and Survival Strategies in Contemporary Heavy Metal Music: A Cultural and Literary Analysis

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