St partially due to a lack of talking about others’ partners.

St partially due to a lack of talking about others’ partners. Language of Age. Figure 4 shows the word cloud (center) and most discriminating topics (surrounding) for four age buckets chosen with regard to the distribution of ages in our sample (Facebook has many more young people). We see clear distinctions, such as use of slang, emoticons, and Internet speak in the youngest group (e.g. ‘:)’, `idk’, and a couple Internet speak topics) or work appearing in the 23 to 29 age group (e.g. `at work’, `new job’, as a job position topic). We also find subtle changes of topics progressing from one age group to the next. For example, we see a school related topic for 13 to 18 year olds (e.g. `school’, `homework’, `ugh’), while we see a college related topic for 19 to 22 year olds (e.g. `semester’, `college’, `register’). Additionally, consider the drunk topic (e.g. `drunk’, `hangover’, `wasted’) that appears for 19 to 22 year olds and a more reserved beer topic (e.g. `beer’, `drinking’, `ale’) for 23 to 29 year olds. In general, we find a progression of school, college, work, and family when looking at the predominant topics across all age groups. DLA may be valuable for the generation of hypotheses about life span developmental age differences. Figure 5A shows the relative frequency of the most discriminating topic for each age group as a function of age. Typical concerns peak at different ages, with the topic concerning relationships (e.g. `son’, `daughter’, `father’, `mother’) continuously increasing across life span. On a similar note, Figure 5C shows `we’ increases approximately linearly after the age of 22, whereas `I’ monotonically decreases. We take this as a proxy for social integration [19], suggesting the increasing importance of friendships and relationships as people age. Figure 5B reinforces this hypothesis by presenting a similar pattern based on other social topics. One limitation of our dataset is the rarity of older individuals using social media; we look forward to a time in which we can track fine-grained language differences across the entire lifespan. Language of Personality. We created age and genderadjusted word clouds for each personality factor based on around 72 thousand participants with at least 1,000 words across their Facebook status updates, who took a Big Five questionnaire [91]. Figure 6 shows word clouds for extraversion and neuroticism. (See Figure S2 for openness, conscientiousness, and agreeableness.) The dominant words in each cluster were consistent with prior lexical and questionnaire work [14]. For example, extraverts were more likely to mention social words such as `party’, `love you’, `boys’, and `ladies’, whereas introverts were more likely to mention words related to solitary activities such as `computer’, `Internet’, and `reading’. In the openness cloud, words such as `music’, `art’, and `writing’ (i.e., creativity), and `dream’, `universe’, and `soul’ (i.e., imagination) were discriminating [85]. Topics were also found reflecting similar concepts as the words, some of which would not have been captured with LIWC. For example, get AZD4547 although LIWC has socially related categories, it does not contain a party topic, which emerges as a key distinguishing feature for extraverts. Topics related to other types of social events are PemafibrateMedChemExpress Pemafibrate listed elsewhere, such as a sports topic for low neuroticismPersonality, Gender, Age in Social Media LanguageFigure 4. Words, phrases, and topics most distinguishing subjects aged 13 t.St partially due to a lack of talking about others’ partners. Language of Age. Figure 4 shows the word cloud (center) and most discriminating topics (surrounding) for four age buckets chosen with regard to the distribution of ages in our sample (Facebook has many more young people). We see clear distinctions, such as use of slang, emoticons, and Internet speak in the youngest group (e.g. ‘:)’, `idk’, and a couple Internet speak topics) or work appearing in the 23 to 29 age group (e.g. `at work’, `new job’, as a job position topic). We also find subtle changes of topics progressing from one age group to the next. For example, we see a school related topic for 13 to 18 year olds (e.g. `school’, `homework’, `ugh’), while we see a college related topic for 19 to 22 year olds (e.g. `semester’, `college’, `register’). Additionally, consider the drunk topic (e.g. `drunk’, `hangover’, `wasted’) that appears for 19 to 22 year olds and a more reserved beer topic (e.g. `beer’, `drinking’, `ale’) for 23 to 29 year olds. In general, we find a progression of school, college, work, and family when looking at the predominant topics across all age groups. DLA may be valuable for the generation of hypotheses about life span developmental age differences. Figure 5A shows the relative frequency of the most discriminating topic for each age group as a function of age. Typical concerns peak at different ages, with the topic concerning relationships (e.g. `son’, `daughter’, `father’, `mother’) continuously increasing across life span. On a similar note, Figure 5C shows `we’ increases approximately linearly after the age of 22, whereas `I’ monotonically decreases. We take this as a proxy for social integration [19], suggesting the increasing importance of friendships and relationships as people age. Figure 5B reinforces this hypothesis by presenting a similar pattern based on other social topics. One limitation of our dataset is the rarity of older individuals using social media; we look forward to a time in which we can track fine-grained language differences across the entire lifespan. Language of Personality. We created age and genderadjusted word clouds for each personality factor based on around 72 thousand participants with at least 1,000 words across their Facebook status updates, who took a Big Five questionnaire [91]. Figure 6 shows word clouds for extraversion and neuroticism. (See Figure S2 for openness, conscientiousness, and agreeableness.) The dominant words in each cluster were consistent with prior lexical and questionnaire work [14]. For example, extraverts were more likely to mention social words such as `party’, `love you’, `boys’, and `ladies’, whereas introverts were more likely to mention words related to solitary activities such as `computer’, `Internet’, and `reading’. In the openness cloud, words such as `music’, `art’, and `writing’ (i.e., creativity), and `dream’, `universe’, and `soul’ (i.e., imagination) were discriminating [85]. Topics were also found reflecting similar concepts as the words, some of which would not have been captured with LIWC. For example, although LIWC has socially related categories, it does not contain a party topic, which emerges as a key distinguishing feature for extraverts. Topics related to other types of social events are listed elsewhere, such as a sports topic for low neuroticismPersonality, Gender, Age in Social Media LanguageFigure 4. Words, phrases, and topics most distinguishing subjects aged 13 t.