Scaling in words on Twitter.
Ontology highlight
ABSTRACT:
SUBMITTER: Bokanyi E
PROVIDER: S-EPMC6837183 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
Scaling in words on Twitter.
Bokányi Eszter E Kondor Dániel D Vattay Gábor G
Royal Society open science 20191002 10
Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the metropolitan and micropolitan statistical areas of the United States. We observe a slightly superlinear urban scaling with the city population for the total volume of the tweets and words created in a city. We then find that a certain core vocabulary follows the scaling relationship of that of the bulk text, but most wo ...[more]
Similar Datasets
Project description:Twitter has become an important platform for geo-spatial analyses, providing high-volume spatial data on a wide variety of social processes. Understanding the relationship between population density and Twitter activity is therefore of key importance. This study reports a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power law functions with exponents greater than one. These relations are consistent with each other and hold across a range of spatial scales. This implies that population density can accurately predict Twitter activity, but importantly, it also implies that correct predictions are not given by a naive linear scaling analysis. The observed super-linearity has implications for any spatial analyses performed with Twitter data and is important for understanding the relationship between Twitter use and demographics. For example, the robustness of this relationship means that we can identify 'anomalous' geographic areas that deviate from the observed trend, identifying several towns with high/low usage relative to expectation; using the scaling relationship we are able to show that these anomalies are not caused by age structure, as has been previously proposed. Proper consideration of this scaling relationship will improve robustness in future geo-spatial studies using Twitter.
| S-EPMC6655604 | biostudies-literature
Project description:Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes-the leading cause of infant mortality-could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms-feature-engineered and deep learning-based classifiers-that automatically distinguish tweets referring to the user's pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the "defect" class and 0.51 for the "possible defect" class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.
| S-EPMC6773753 | biostudies-literature
Project description:E-cigarettes were initially introduced as a less harmful alternative to combustible cigarettes, but marketing efforts may now be exceeding these claims by associating e-cigarettes with words related to healthy foods. These associations could mislead people to assume vaping is a healthy practice. Tweets from January to March 2017 were obtained from the Twitter Streaming Application Programming Interface (API) to assess content about vaping linked to healthy food words. Tweets were classified into one of nine categories along with their source (marketer vs. non-marketer). We content analyzed original English language public postings on Twitter that included vaping-related keywords and at least one of eight co-occurring healthy food-related labels (e.g., 'natural,' 'vitamin,' 'vegan,' and 'organic') (N?=?1205). Chi-square analyses compared themes by message source. Findings suggest vaping is being marketed in ways that could paradoxically lead consumers to believe that e-cigarettes are health-enhancing. We found more tweets representing vaping as health-enhancing (9%) than referring to it as a smoking-cessation device (1%). The largest category of tweets referred to vaping as harmless (28%) and therefore compatible with a healthy lifestyle. Tweets presenting vaping as harmless or with a sensation theme were more likely to be authored by marketers than by non-marketers. Food and drug regulation needs to be more vigilant to prevent misleading advertising from e-cigarette marketers.
| S-EPMC6180293 | biostudies-other
Project description:BackgroundZipf's discovery that word frequency distributions obey a power law established parallels between biological and physical processes, and language, laying the groundwork for a complex systems perspective on human communication. More recent research has also identified scaling regularities in the dynamics underlying the successive occurrences of events, suggesting the possibility of similar findings for language as well.Methodology/principal findingsBy considering frequent words in USENET discussion groups and in disparate databases where the language has different levels of formality, here we show that the distributions of distances between successive occurrences of the same word display bursty deviations from a Poisson process and are well characterized by a stretched exponential (Weibull) scaling. The extent of this deviation depends strongly on semantic type -- a measure of the logicality of each word -- and less strongly on frequency. We develop a generative model of this behavior that fully determines the dynamics of word usage.Conclusions/significanceRecurrence patterns of words are well described by a stretched exponential distribution of recurrence times, an empirical scaling that cannot be anticipated from Zipf's law. Because the use of words provides a uniquely precise and powerful lens on human thought and activity, our findings also have implications for other overt manifestations of collective human dynamics.
| S-EPMC2770836 | biostudies-literature
Project description:We analyze the occurrence frequencies of over 15 million words recorded in millions of books published during the past two centuries in seven different languages. For all languages and chronological subsets of the data we confirm that two scaling regimes characterize the word frequency distributions, with only the more common words obeying the classic Zipf law. Using corpora of unprecedented size, we test the allometric scaling relation between the corpus size and the vocabulary size of growing languages to demonstrate a decreasing marginal need for new words, a feature that is likely related to the underlying correlations between words. We calculate the annual growth fluctuations of word use which has a decreasing trend as the corpus size increases, indicating a slowdown in linguistic evolution following language expansion. This "cooling pattern" forms the basis of a third statistical regularity, which unlike the Zipf and the Heaps law, is dynamical in nature.
| S-EPMC3517984 | biostudies-other
Project description:BackgroundThe lack of trust in vaccines is a major contributor to vaccine hesitancy. To overcome vaccine hesitancy for the COVID-19 vaccine, the Australian government launched multiple public health campaigns to encourage vaccine uptake. This sentiment analysis examines the effect of public health campaigns and COVID-19-related events on sentiment and vaccine uptake.ObjectiveThis study aims to examine the relationship between sentiment and COVID-19 vaccine uptake and government actions that impacted public sentiment about the vaccine.MethodsUsing machine learning methods, we collected 137,523 publicly available English language tweets published in Australia between February and October 2021 that contained COVID-19 vaccine-related keywords. Machine learning methods were used to extract topics and sentiments relating to COVID-19 vaccination. The relationship between public vaccination sentiment on Twitter and vaccine uptake was examined.ResultsThe majority of collected tweets expressed negative (n=91,052, 66%) rather than positive (n=21,686, 16%) or neutral (n=24,785, 18%) sentiments. Topics discussed within the study time frame included the role of the government in the vaccination rollout, availability and accessibility of the vaccine, and vaccine efficacy. There was a significant positive correlation between negative sentiment and the number of vaccine doses administered daily (r267=.15, P<.05), with positive sentiment showing the inverse effect. Public health campaigns, lockdowns, and antivaccination protests were associated with increased negative sentiment, while vaccination mandates had no significant effect on sentiment.ConclusionsThe study findings demonstrate that negative sentiment was more prevalent on Twitter during the Australian vaccination rollout but vaccine uptake remained high. Australians expressed anger at the slow rollout and limited availability of the vaccine during the study period. Public health campaigns, lockdowns, and antivaccination rallies increased negative sentiment. In contrast, news of increased vaccine availability for the public and government acquisition of more doses were key government actions that reduced negative sentiment. These findings can be used to inform government communication planning.
| S-EPMC9484485 | biostudies-literature
Project description:Combinatorial genetics reveals a scaling law for the effects of mutations on splicing
| PRJEB24588 | ENA
Project description:A surprisingly small portion of reading research has been dedicated to investigating how the visual word recognition process is influenced by embedded words (e.g., 'arm' in 'charm'), and no research has yet investigated embedded words in a natural reading setting. Covering this issue, the present work reports analyses of eye-tracking data from the GECO bilingual book reading corpus. Word viewing times were analyzed as a function of the number, frequency and proportional length of embedded words. We anticipated two scenarios: embedded words would either facilitate processing due to increased word-letter feedback, or inhibit processing due to increased lexical competition. A main facilitatory effect of embedded words on the recognition process was established, with an increasing number of embedded words resulting in shorter word viewing times and fewer fixations. This pattern was depicted by readers of Dutch as well as readers of English. Long, high-frequency embedded words formed an exception however, as these led to inhibition (Dutch participants) or a null-effect (English participants). The present results indicate that both scenarios outlined above are at play, but with a theoretical constraint on the role of word-to-word inhibitory connections. Specifically, such connections may predominantly exist among words of similar length. Hence, embedded words generally facilitate processing through word-letter feedback, but this facilitatory effect is countered by word-to-word inhibition if the embedded word's length approximates that of its superset.
| S-EPMC6634368 | biostudies-literature
Project description:The meaning of a picture can be extracted rapidly, but the form-to-meaning relationship is less obvious for printed words. In contrast to English words that follow grapheme-to-phoneme correspondence rule, the iconic nature of Chinese words might predispose them to activate their semantic representations more directly from their orthographies. By using the paradigm of repetition blindness (RB) that taps into the early level of word processing, we examined whether Chinese words activate their semantic representations as directly as pictures do. RB refers to the failure to detect the second occurrence of an item when it is presented twice in temporal proximity. Previous studies showed RB for semantically related pictures, suggesting that pictures activate their semantic representations directly from their shapes and thus two semantically related pictures are represented as repeated. However, this does not apply to English words since no RB was found for English synonyms. In this study, we replicated the semantic RB effect for pictures, and further showed the absence of semantic RB for Chinese synonyms. Based on our findings, it is suggested that Chinese words are processed like English words, which do not activate their semantic representations as directly as pictures do.
| S-EPMC5974396 | biostudies-literature
Project description:This article introduces the Twitter Parliamentarian Database (TPD), a multi-source and manually validated database of parliamentarians on Twitter. The TPD includes parliamentarians from all European Free Trade Association countries where over 45% of parliamentarians are on Twitter as well as a selection of English-speaking countries. The database is designed to move beyond the one-off nature of most Twitter-based research and in the direction of systematic and rigorous comparative and transnational analysis. The TPD incorporates, in addition to data collected through Twitter's streaming API and governmental websites, data from the Manifesto Project Database; the Electoral System Design Database; the ParlGov database; and the Chapel Hill Expert Survey. By compiling these different data sources it becomes possible to compare different countries, political parties, political party families, and different kinds of democracies. To illustrate the opportunities for comparative and transnational analysis that the TPD opens up, we ask: What are the differences between countries in parliamentarian Twitter interactions? How do political parties differ in their use of hashtags and what is their common ground? What is the structure of interaction between parliamentarians in the transnational debate? Alongside some interesting similarities, we find striking cross-party and particularly cross-national differences in how parliamentarians engage in politics on the social media platform.
| S-EPMC7494116 | biostudies-literature