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Found 27 papers, 9 papers with code
Characterizing narrative time in books through fluctuations in power and danger arcs
While recent studies have focused on quantifying word usage to find the overall shapes of narrative emotional arcs, certain features of narratives within narratives remain to be explored.
Sentiment and structure in word co-occurrence networks on Twitter
We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur.
Augmenting semantic lexicons using word embeddings and transfer learning
Sentiment-aware intelligent systems are essential to a wide array of applications.
Quantifying language changes surrounding mental health on Twitter
Mental health challenges are thought to afflict around 10% of the global population each year, with many going untreated due to stigma and limited access to services.
The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community
Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of `incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships.
Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance
Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements.
Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts
A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content.
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
In real-time, social media data strongly imprints world events, popular culture, and day-to-day conversations by millions of ordinary people at a scale that is scarcely conventionalized and recorded.
The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong
However, the extent to which mortality in a geographical region is a function of socioeconomic factors in both that region and its neighbors is unclear.
Physics and Society Social and Information Networks Applications
The growing amplification of social media: Measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009-2020
We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content.
Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
Stretched words like `heellllp' or `heyyyyy' are a regular feature of spoken language, often used to emphasize or exaggerate the underlying meaning of the root word.
The shocklet transform: A decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series
We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series---termed the Discrete Shocklet Transform (DST)---and an associated similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm, that indicates time windows during which panels of time series display qualitatively-similar anomalous behavior.
Physics and Society Data Structures and Algorithms Signal Processing Data Analysis, Statistics and Probability
Visitors to urban greenspace have higher sentiment and lower negativity on Twitter
With more people living in cities, we are witnessing a decline in exposure to nature.
A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter
Conclusions: Social media can provide a positive outlet for patients to discuss their needs and concerns regarding their healthcare coverage and treatment needs.
English verb regularization in books and tweets
We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books.
Forecasting the onset and course of mental illness with Twitter data
Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy).
Physics and Society Social and Information Networks
The emotional arcs of stories are dominated by six basic shapes
Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture's evolution through its texts using a "big data" lens.
Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter
Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag #BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans.
Zipf's law is a consequence of coherent language production
The task of text segmentation may be undertaken at many levels in text analysis---paragraphs, sentences, words, or even letters.
Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs
The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, bearing profound implications for our understanding of human behavior.
Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter
Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion.
Is language evolution grinding to a halt? The scaling of lexical turbulence in English fiction suggests it is not
Of basic interest is the quantification of the long term growth of a language's lexicon as it develops to more completely cover both a culture's communication requirements and knowledge space.
Identifying missing dictionary entries with frequency-conserving context models
With our predictions we then engage the editorial community of the Wiktionary and propose short lists of potential missing entries for definition, developing a breakthrough, lexical extraction technique, and expanding our knowledge of the defined English lexicon of phrases.
Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution
However, the Google Books corpus suffers from a number of limitations which make it an obscure mask of cultural popularity.
Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language
Natural languages are full of rules and exceptions.
Zipf's law holds for phrases, not words
With Zipf's law being originally and most famously observed for word frequency, it is surprisingly limited in its applicability to human language, holding over no more than three to four orders of magnitude before hitting a clear break in scaling.
Human language reveals a universal positivity bias
Using human evaluation of 100, 000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage.
