As the data-collection approach is twofold (Twitter data in addition to questionnaires with demographic information and personality profiles), the hypotheses fall into four categories: personality and linguistic features, Twitter measures in relationship to gender, gender effects and LIWC categories, and word-based measures as related to gender.
3.2.1 Effects of Personality on LIWC Categories
(1a) There will be a significant positive correlation between an extraverted personality (as measured by the score for extraversion in the Big Five factor model) and the percentage of positive emotion words.
(1b) There will be a significant positive correlation between an agreeable personality (as measured by the score for openness in the Big Five factor model) and the percentage of positive emotion words.
(1c) There will be a significant negative correlation between an agreeable personality (as measured by the score for openness in the Big Five factor model) and the percentage of swear words.
(1d) There will be a significant positive correlation between a neurotic personality (as measured by the score for openness in the Big Five factor model) and the percentage of words in the anxiety category.
(1e) There will be a significant prediction of the sentiment score (sentiment scores for emojis range from -1 to +1, with 0 being neutral (Novak et al., 2015b)) by extraversion (as measured by the score for extraversion in the Big Five factor model).
(1f) There will be a significant prediction of the sentiment score (sentiment scores for emojis range from -1 to +1, with 0 being neutral (Novak et al., 2015b)) by neuroticism (as measured by the score for neuroticism in the Big Five factor model).
3.1.2 Gender Effects and Twitter Measures
(2a) There will be a significant prediction of hashtag density (percentage of tweets containing hashtags) by gender.
(2b) There will be a significant prediction of hashtag type (tag vs. commentary – as measured by individual hashtag densities in the hashtag subset) by gender.
(2c) There will be a significant prediction of hashtag type (tag vs. commentary – as measured by individual hashtag densities in the hashtag subset) by language (German vs. English).
(2d) There will be a significant prediction of emoji density (as measured by the percentage of tweets that contain at least one emoji) by gender.
3.1.3 Gender Effects and LIWC Categories
(3a) There will be a significant prediction of positive emotion words (as measured by the percentage of words in the positive emotion word category) by gender.
(3b) There will be a significant prediction of positive feeling words (as measured by the percentage of words in the positive feeling word category) by gender.
(3c) There will be a significant prediction of negative emotion words (as measured by the percentage of words in the negative emotion word category) by gender.
(3d) There will be a significant prediction of swear words (as measured by the percentage of words in the swear word category) by gender.
(3e) There will be a significant prediction of tentative words (as measured by the
percentage of words in the tentative word category (see Appendix C, p. 258)) by gender. (3f) There will be a significant prediction of words related to social concerns (as
measured by the percentage of words in the social concerns category) by gender.
(3g) There will be a significant prediction of words related to family (as measured by the percentage of words in the family category) by gender.
(3h) There will be a significant prediction of percentage of words related to friends (as measured by the percentage of words in the friends category) by gender.
(3i) There will be a significant prediction of words related to occupation (as measured by the percentage of words in the occupation word category) by gender.
(3j) There will be a significant prediction of words related to job (as measured by the percentage of words in the job category) by gender.
(3k) There will be a significant prediction of words related to achievements (as measured by the percentage of words in the achievement category) by gender.
(3l) There will be a significant prediction of words related to money (as measured by the percentage of words in the money category) by gender.
(3m) There will be a significant prediction of words related to sports (as measured by the percentage of words in the sports category) by gender.
3.1.4 Gender Effects and Word-Based Measures
(4a) There will not be a significant difference between the lexical diversity of men and women as measured by Carroll’s CTTR.
(4b) There will not be a significant difference between the vocabulary richness of men and women as measured by Yule’s K.
(4c) German tweets will show a more ‘oral-like’ style despite Twitter being a hybrid, mostly written, genre (as measured by the percentages of the two conjunctions weil and denn ‘because,’ the former being used in a more informal genre and the latter almost exclusively being used in formal language (Wegener, 1999)).
To address the above hypotheses, different types of data are needed: (1) to investigate linguistic features, participants’ tweets are needed as they provide the natural language data, (2) to investigate participants’ personality, scores from the Big Five trait inventory are needed to provide measurements on extraversion, openness, agreeableness, conscientiousness, and neuroticism, and (3) participants’ demographic information is needed to test the hypotheses revolving around interactions between gender, language and personality.
3.2 Independent (Predictor/Covariates) and Dependent (Outcome) Variables: