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172 results for “Media Use”
Upconverting Nanoparticles in Aqueous Media: Not a Dead-End Road. Avoiding Degradation by Using Hydrophobic Polymer Shells
<p>Dataset of https://zenodo.org/record/5793193#.YcCAcWjMJPY</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results</p> <p> Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
<p>This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. Treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organization, the sharing/liking of news content and the tweeting/liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.</p>
Huntingtin nanobody purification using osmotic shock cell lysis and growth in M9 media – 2018/04/09
<p>Huntingtin structure-function open lab notebook project. Huntingtin nanobody purification using osmotic shock cell lysis and growth in M9 media – 2018/04/09</p>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2019)
<p>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</p> <p><strong>Sources with weights:</strong></p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p><strong>Methodology</strong></p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p>The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way:</p> <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Study on the effects of heterogeneous distribution of methane hydrate on permeability of porous media using low-field NMR technique
<p>Data sets for the Publication 'Study on the effects of heterogeneous distribution of methane hydrate on permeability of porous media using low-field NMR technique' in Water Resources Research.</p>
Reductive synthesis of azoxypyridines from nitropyridines using hydroxides in alcoholic media by L. Caruana et al. - Raw NMR data
<p>NMR raw data for the work "Reductive synthesis of azoxypyridines from nitropyridine using hydroxides in alcoholic media"</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotions</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Nestor Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Data for Diffusion phase-imaging in anisotropic media using non-linear gradients for diffusion encoding
<p>Diffusion MRI data for experiments on anisotropic synthetic fibre phantom and isotropic agar phantom. </p>
Changes in the use of 'refugees' and 'migrants' in international news media and academic publications 2010–2022
<p>The dataset contains source data for Figure 2 in the article, based on search results from Factiva and Web of Science.</p>
Using social media and personality traits to assess software developers' emotional polarity
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotional polarity</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Using Social Media to Deliver HIV Self-Testing Kits and Link to Online PrEP Services
ClinicalTrials.gov study NCT04155502. IPD Sharing: YES. Countries: 1. Publications: 2.
Short Term Relief of Eustachian Tube Dysfunction and Serous Otitis Media Using Intranasal Steroid Sprays
ClinicalTrials.gov study NCT00279916. IPD Sharing: NO. Countries: 1. Publications: 2.
Change in Social Media Use and Well-being Among College Students Receiving a One-week Exercise or Mindfulness Intervention
ClinicalTrials.gov study NCT06143852. IPD Sharing: YES. Countries: 1. Publications: 5.
InTSHA: Interactive Transition Support for HIV-infected Adolescents Using Social Media
ClinicalTrials.gov study NCT03624413. IPD Sharing: YES. Countries: 1. Publications: 1.
Social Media Use During COVID-19
ClinicalTrials.gov study NCT04305574. IPD Sharing: NO. Countries: 1. Publications: 1.
Project Minority AIDS Prevention: Reaching Black and Hispanic MSM About PrEP and TasP Using Social Media
ClinicalTrials.gov study NCT03404531. IPD Sharing: YES. Countries: 1. Publications: 1.
Assessing the Impact of a Social Media-Based Educational Intervention Using WhatsApp Video Messages on Dental Caries Prevention Knowledge, Oral Hygiene Practices, and Attitudes Toward Dental Health Am
ClinicalTrials.gov study NCT07363317. IPD Sharing: NO. Countries: 1. Publications: 3.
Social Media Intervention for Cannabis Use in Emerging Adults
ClinicalTrials.gov study NCT04187989. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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