Search Results for author: Peter Sheridan Dodds

Found 27 papers, 9 papers with code

Characterizing narrative time in books through fluctuations in power and danger arcs

no code implementations19 Aug 2022 Mikaela Irene Fudolig, Thayer Alshaabi, Kathryn Cramer, Christopher M. Danforth, Peter Sheridan Dodds

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.

Denoising Time Series Analysis

Sentiment and structure in word co-occurrence networks on Twitter

no code implementations1 Oct 2021 Mikaela Irene Fudolig, Thayer Alshaabi, Michael V. Arnold, Christopher M. Danforth, Peter Sheridan Dodds

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.

Community Detection

Quantifying language changes surrounding mental health on Twitter

no code implementations2 Jun 2021 Anne Marie Stupinski, Thayer Alshaabi, Michael V. Arnold, Jane Lydia Adams, Joshua R. Minot, Matthew Price, Peter Sheridan Dodds, Christopher M. Danforth

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

no code implementations25 May 2021 Kelly Gothard, David Rushing Dewhurst, Joshua R. Minot, Jane Lydia Adams, Christopher M. Danforth, Peter Sheridan Dodds

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.

Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts

3 code implementations5 Aug 2020 Ryan J. Gallagher, Morgan R. Frank, Lewis Mitchell, Aaron J. Schwartz, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

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

6 code implementations25 Jul 2020 Thayer Alshaabi, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

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.

Time Series Analysis

The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong

1 code implementation15 Jun 2020 Thayer Alshaabi, David Rushing Dewhurst, James P. Bagrow, Peter Sheridan Dodds, Christopher M. Danforth

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

Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings

no code implementations9 Jul 2019 Tyler J. Gray, Christopher M. Danforth, Peter Sheridan Dodds

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

2 code implementations27 Jun 2019 David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds

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

A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter

no code implementations25 May 2018 Eric M. Clark, Ted James, Chris A. Jones, Amulya Alapati, Promise Ukandu, Christopher M. Danforth, Peter Sheridan Dodds

Conclusions: Social media can provide a positive outlet for patients to discuss their needs and concerns regarding their healthcare coverage and treatment needs.

Decision Making Sentiment Analysis

English verb regularization in books and tweets

no code implementations26 Mar 2018 Tyler J. Gray, Andrew J. Reagan, Peter Sheridan Dodds, Christopher M. Danforth

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

1 code implementation27 Aug 2016 Andrew G. Reece, Andrew J. Reagan, Katharina L. M. Lix, Peter Sheridan Dodds, Christopher M. Danforth, Ellen J. Langer

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

2 code implementations24 Jun 2016 Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, Peter Sheridan Dodds

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

no code implementations22 Jun 2016 Ryan J. Gallagher, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

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.

Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs

2 code implementations2 Dec 2015 Andrew J. Reagan, Brian Tivnan, Jake Ryland Williams, Christopher M. Danforth, Peter Sheridan Dodds

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.

Benchmarking Sentiment Analysis

Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter

no code implementations17 May 2015 Eric M. Clark, Jake Ryland Williams, Chris A. Jones, Richard A. Galbraith, Christopher M. Danforth, Peter Sheridan Dodds

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

no code implementations11 Mar 2015 Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds

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

no code implementations7 Mar 2015 Jake Ryland Williams, Eric M. Clark, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds

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

no code implementations5 Jan 2015 Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds

However, the Google Books corpus suffers from a number of limitations which make it an obscure mask of cultural popularity.

Zipf's law holds for phrases, not words

no code implementations19 Jun 2014 Jake Ryland Williams, Paul R. Lessard, Suma Desu, Eric Clark, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds

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

no code implementations15 Jun 2014 Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth

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.


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