Divergent word usages reflect differences among people. In this paper, we present a novel angle for studying word usage divergence -- word interpretations. We propose an approach that quantifies semantic differences in interpretations among different groups of people. The effectiveness of our approach is validated by quantitative evaluations. Experiment results indicate that divergences in word interpretations exist. We further apply the approach to two well studied types of differences between people -- gender and region. The detected words with divergent interpretations reveal the unique features of specific groups of people. For gender, we discover that certain different interests, social attitudes, and characters between males and females are reflected in their divergent interpretations of many words. For region, we find that specific interpretations of certain words reveal the geographical and cultural features of different regions.
Much work has focused on divergent word usages among people in numerous aspects, such as gender, age, occupation, and region. It is reported that typical male language uses more judgmental adjectives, elliptical sentences, directives, and I'' reference, while typical female language contains more intensive adverbs, references to emotions, uncertainty verbs @cite_9 . People in different age groups appear to choose words differently. reported that, comparing with younger people, older people talk more about family and work, and use less swear words in their language @cite_0 . Jobs affect the language patterns of people as well @cite_10 . In the paper, the authors listed the most used words of several occupations, and their results indicate clear divergences in word usages between people of different jobs. Similarly, different cities may have different preferences in words @cite_13 , which reflect regional differences between people.
@cite_0 @cite_9 @cite_10 @cite_13
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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 personality. We present a method that learns representations for word meanings from short video clips paired with sentences. Unlike prior work on learning language from symbolic input, our input consists of video of people interacting with multiple complex objects in outdoor environments. Unlike prior computer-vision approaches that learn from videos with verb labels or images with noun labels, our labels are sentences containing nouns, verbs, prepositions, adjectives, and adverbs. The correspondence between words and concepts in the video is learned in an unsupervised fashion, even when the video depicts simultaneous events described by multiple sentences or when different aspects of a single event are described with multiple sentences. The learned word meanings can be subsequently used to automatically generate description of new video. We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender. Two projects explored the links between language use and aging. In the first project, written or spoken text samples from disclosure studies from over 3,000 research participants from 45 different studies representing 21 laboratories in 3 countries were analyzed to determine how people change in their use of 14 text dimensions as a function of age. A separate project analyzed the collected works of 10 well-known novelists, playwrights, and poets who lived over the last 500 years. Both projects found that with increasing age, individuals use more positive and fewer negative affect words, use fewer self-references, use more future-tense and fewer past-tense verbs, arid demonstrate a general pattern of increasing cognitive complexity. Implications for using language as a marker of personality among current and historical texts are discussed.