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- Creator:
- Budak, Ceren, Goel, Sharad, and Rao, Justin M
- Description:
- Our primary analysis is based on articles published in 2013 by the top thirteen US news outlets and two popular political blogs. To compile the set of articles published by these outlets, we first examined the complete web-browsing records for US-located users who installed the Bing Toolbar, an optional add-on application for the Internet Explorer web browser. For each of the fifteen news sites, we recorded all unique URLs that were visited by at least ten toolbar users, and we then crawled the news sites to obtain the full article title and text. This process resulted in a corpus of 803,146 articles published on the fifteen news sites over the course of a year, with each article annotated with its relative popularity. , Next, we built two binary classifiers using large-scale logistic regression. The first classifier—which we refer to as the news classifier —identifies “news” articles (i.e., articles that would typically appear in the front section of a traditional newspaper). The second classifier—the politics classifier —identifies political news from the subset of articles identified as news by the first classifier. 340,191 (42 percent) were classified as news. On the set of 340,191 news articles, 114,814 (34 percent) were classified as political. , Having identified approximately 115,000 political news articles, we next seek to categorize the articles by topic (e.g., gay rights, healthcare, etc.), and to quantify the political slant of the article. To do so, we turn to human judges recruited via Mechanical Turk to analyze the articles. For every day in 2013, we randomly selected two political articles, when available, from each of the 15 outlets we study, with sampling weights equal to the number of times the article was visited by our panel of toolbar users., Amazon Mechanical Turk Labeling task: To detect and control for possible preconceptions of an outlet’s ideological slant, workers, upon first entering the experiment, were randomly assigned to either a blinded or unblinded condition. In the blinded condition, workers were presented with only the article’s title and text, whereas in the unblinded condition, they were additionally shown the name of the outlet in which the article was published. Each article was then analyzed by two workers, one each from the sets of workers in the two conditions. For each article, each worker completed the following three tasks. First, they provided primary and secondary article classifications from a list of fifteen topics: (1) civil rights; (2) Democrat scandals; (3) drugs; (4) economy; (5) education; (6) elections; (7) environment; (8) gay rights; (9) gun-related crimes; (10) gun rights/regulation; (11) healthcare; (12) international news; (13) national security; (14) Republican scandals; and (15) other. , and Second, workers determined whether the article was descriptive news or opinion. Third, to measure ideological slant, workers were asked, “Is the article generally positive, neutral, or negative toward members of the Democratic Party?” and separately, “Is the article generally positive, neutral, or negative toward members of the Republican Party?” Choices for these last two questions were provided on a five-point scale: very positive, somewhat positive, neutral, somewhat negative, and very negative. To mitigate question-ordering effects, workers were initially randomly assigned to being asked either the Democratic or Republican party question first; the question order remained the same for any subsequent articles the worker rated. Finally, we assigned each article a partisanship score between –1 and 1, where a negative rating indicates that the article is net left-leaning and a positive rating indicates that it is net right-leaning. Specifically, for an article’s depiction of the Democratic Party, the five-point scale from very positive to very negative is encoded as –1, –0.5, 0, 0.5, 1. Analogously, for an article’s depiction of the Republican Party, the scale is encoded as 1, 0.5, 0, –.0.5, –1. The score for each article is defined as the average over these two ratings. Thus, an average score of –1, for example, indicates that the article is very positive toward Democrats and very negative toward Republicans. The result of this procedure is a large, representative sample of political news articles, with direct human judgments on partisanship and article topic.
- Keyword:
- news media, media bias, crowdsourcing, and machine learning
- Citation to related publication:
- https://academic.oup.com/poq/article-abstract/80/S1/250/2223443/?redirectedFrom=fulltext Ceren Budak, Sharad Goel, Justin M. Rao, Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis, Public Opinion Quarterly, Volume 80, Issue S1, 2016, Pages 250–271, https://doi.org/10.1093/poq/nfw007
- Discipline:
- Social Sciences
- Title:
- Quantifying News Media Bias through Crowdsourcing and Machine Learning Dataset
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- Creator:
- Schulte, Erica M
- Description:
- The data set supports a study investigating which foods may be most implicated in addictive-like eating by examining how nutritionally diverse foods relate to loss of control consumption and various subjective effect reports. Participants (n = 501) self-reported how likely they were to experience a loss of control over their consumption of 30 nutritionally diverse foods and rated each food on five subjective effect report questions that assess the abuse liability of substances (liking, pleasure, craving, averseness, intensity). Hierarchical cluster analytic techniques were used to examine how foods grouped together based on each question. Highly processed foods, with added fats and/or refined carbohydrates, clustered together and were associated with greater loss of control, liking, pleasure, and craving. The clusters yielded from the subjective effect reports assessing liking, pleasure, and craving were most similar to clusters formed based on loss of control over consumption, whereas the clusters yielded from averseness and intensity did not meaningfully differentiate food items. The associated study applies methodology used to assess the abuse liability of substances to understand whether foods may vary in their potential to be associated with addictive-like consumption. Highly processed foods (e.g., pizza, chocolate) appear to be most related to an indicator of addictive-like eating (loss of control) and several subjective effect reports (liking, pleasure, craving). Thus, these foods may be particularly reinforcing and capable of triggering an addictive-like response in some individuals. Future research is warranted to understand whether highly processed foods are related to these indicators of abuse liability at a similar magnitude as addictive substances. The data set is presented in both .sav format for use with SPSS software and in csv format.
- Keyword:
- Behavioral Addiction and Food Consumption
- Citation to related publication:
- Schulte EM, Smeal JK, Gearhardt AN (2017) Foods are differentially associated with subjective effect report questions of abuse liability. PLoS ONE 12(8): e0184220. https://doi.org/10.1371/journal.pone.0184220
- Discipline:
- Social Sciences
- Title:
- Subjective Effect Reports of Food
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- Creator:
- Detroit Residential Parcel Survey, United States Postal Service, Wayne County Register of Deeds, US Census, Yu, Deehan, Public and Affordable Housing Research Corporation (PAHRC), Veinot, Tiffany, RealtyTRAC, National Low Income Housing Coalition (NLIHC), Okullo, Dolorence, Wayne County Treasurer, Health Resource and Services Administration (HRSA), and Data Driven Detroit
- Description:
- This dataset includes census tract-level data concerning housing in Metropolitan Detroit. The data includes: 1) Total housing units and total mortgages in the tract; 2) Land use; 3) Real estate information (foreclosures, sales transactions, and home values); 4) Vacant housing; 5) Housing age and available facilities; 6) Housing condition; and 7) Spatial measures of subsidized housing in the tract. Data coverage should say 2006 to 2015.
- Keyword:
- Housing Conditions, Community Health, Subsidized Housing Locations, Availability of Kitchen and Plumbing Facilities, Vacant Properties, Census tract level, Age of Housing, Spatial Measures, Home Sale Transactions, Land Use by Category, Housing Units and Ownership, Housing Values, and Foreclosed Properties
- Citation to related publication:
- Discipline:
- Health Sciences and Social Sciences
- Title:
- Neighborhood effects : Housing
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- Creator:
- Clarke, Phillipa J., Veinot, Tiffany C, Okullo, Dolorence, Gomez-Lopez, Iris N., Reddy, Shruthi, Goodspeed, Robert, and Data Driven Detroit
- Description:
- The information and education environment refers to: 1) the presence of information infrastructures such as broadband Internet access and public libraries in a location; 2) a person’s proximity to information infrastructures and sources; 3) the distribution of information infrastructures, sources and in a specific location; and 4) exposure to specific messages (information content) within a specific location. Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
- Keyword:
- Residential Broadband Data Adoption Rates, Census tract level, Broadband Internet Access and Speed, Colleges and Universities, Public Libraries, Spatial Measures, and Schools
- Citation to related publication:
- Discipline:
- Health Sciences, Science, and Social Sciences
- Title:
- Neighborhood effects : Information and Education Environment
-
- Creator:
- Reference USA, City of Detroit, ESRI, Data Driven Detroit, and Veinot, Tiffany C.
- Description:
- Active living resources include spaces and organizations that facilitate physical activity, including 1) park land, 2) recreation areas (including parks, golf courses, amusement parks, beaches and other recreational landmarks); and 3) recreation centers (including gyms, dancing instruction, martial arts instruction, bowling centers, yoga instruction, sports clubs, fitness programs, golf course, pilates instruction, personal trainers, swimming pools, skating rinks, etc.) Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
- Keyword:
- Recreation Areas, Park Land, Census tract level, Metropolitan Detroit, Spatial Measures, Recreation Centers, and Michigan
- Citation to related publication:
- Discipline:
- Health Sciences, Social Sciences, and Other
- Title:
- Neighborhood Effects Active Living Resources
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- Creator:
- Yan, Haoyang MI and Yates, J Frank
- Description:
- The research aims to demonstrate judgment accuracy about other individuals' socio-political opinions produced by prototype matching and base/shift heuristics or people's own strategies (control).
- Keyword:
- judgment accuracy and socio-political opinions
- Citation to related publication:
- Discipline:
- Social Sciences
- Title:
- Judgment Accuracy Experiment Data
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- Creator:
- Brennan, Jonathan R.
- Description:
- These files contain the raw data and processing parameters to go with the paper "Hierarchical structure guides rapid linguistic predictions during naturalistic listening" by Jonathan R. Brennan and John T. Hale. These files include the stimulus (wav files), raw data (matlab format for the Fieldtrip toolbox), data processing paramters (matlab), and variables used to align the stimuli with the EEG data and for the statistical analyses reported in the paper.
- Keyword:
- linguistics, syntax, language, and eeg
- Citation to related publication:
- Brennan JR, Hale JT (2019) Hierarchical structure guides rapid linguistic predictions during naturalistic listening. PLoS ONE 14(1): e0207741. https://doi.org/10.1371/journal.pone.0207741
- Discipline:
- Social Sciences
-
- Creator:
- Moser, Carol, Schoenebeck, Sarita Y., and Resnick, Paul
- Description:
- This work investigates what features e-commerce sites use to encourage impulse buying and what tools consumers desire to curb their online spending. We present supplementary material for two studies: (1) a systematic content analysis of 200 top e-commerce websites in the U.S. and (2) a survey of online impulse buyers (N=151). Files include: (1) Study 1 Code book for content analysis of websites (2) Study 1 CSV data file resulting from the content analysis (3) Study 1 PDFs (N=200) of e-commerce websites analyzed (4) Study 2 Online survey questionnaire (5) Study 2 Survey code book for free response questions
- Discipline:
- Social Sciences
-
- Creator:
- Pearce, Alexa L.
- Description:
- This dataset accompanies a study that seeks to contribute to a clearer understanding of the discovery ecosystem in academic research libraries. Using historical literature as a case study, extensive citation analysis is employed to both reveal characteristics of secondary historical literature as well as to test a broad disciplinary discovery environment that includes six specific search platforms. By enhancing our understanding of where and how specific types of resources are –or are not—discoverable, as the case may be, this study can provide evidence to better inform the appropriate role and placement of various search platforms in a user’s process. This citation analysis drew upon all secondary literature that was cited in the American Historical Review (AHR) during a six-year period, from 2010 through 2015. The AHR is the official publication of the American Historical Association (AHA) and, as stated on its website, has served as “the journal of record for the historical profession in the United States since 1895.” Additionally, the AHR represents all subfields of history in its research articles and reviews of new scholarship. For this study, the author gathered citations from research articles only, excluding reviews. For the purposes of testing the library discovery environment, the author aimed to include citations that a researcher would be likely to identify by using library research tools, as opposed to archival finding aids. Recognizing that some tools included in this study, such as JSTOR and Historical Abstracts, do not index archival sources, the author decided to focus on published and secondary materials. All citations to archival sources, government information, and other unpublished manuscript materials were excluded. Additionally, citations to newspaper and general or popular press articles published prior to 1900 were excluded. Citations to entire periodicals, as opposed to articles, were also excluded. Books from all date ranges were included. Citations to non-scholarly newspaper and magazine articles published after 1900 were included. Citations to published primary sources were also included in the population of citations, as one may reasonably expect to locate them in a research library setting. The resulting population comprised 22,572 citations. After separating out duplicate citations, the total number was 19,937. Using a random number generator, the de-duplicated list of citations was re-ordered in order to select a random sample of 400, which affords a confidence level of 95% and a confidence interval of 5. The first step in analysis was to characterize each citation according to format, publication date, and language. Secondly, the author searched for all citations in the sample in the 6 different search platforms listed above. The primary question for each database included in the study was how comprehensively it represented the population of AHR citations, as represented by the random sample selected for this study. In order for a given citation to count as present in a particular database, it had to be represented in the format in which it was cited. For example, if a search for a cited book turned up only a dissertation, with the same author and very similar title, the analysis found that the citation was not present. For book chapters cited with authors and titles, it was not necessary for chapters to have their own records in order to be counted as present but it was necessary for them to be discernible among search results as chapters, such as in a table of contents listing. In order to expedite the search process, the author searched Historical Abstracts and America: History and Life simultaneously on the EBSCO platform. For all of the platforms except Google Scholar, the author performed advanced searches, entering both title and author information for each citation. All searching took place between February and May of 2017. The results presented here reflect the content available to search in each platform at the time of investigation.
- Keyword:
- discovery, history, secondary literature, information retrieval, reference analysis, citation analysis, library science, university libraries, and research libraries
- Discipline:
- Social Sciences
-
- Creator:
- Beck, Jess
- Description:
- These data include skeletal and dental inventories, assessments of skeletal and dental pathology, and the age and sex of individuals buried at Necropolis 1, Necropolis 2, and Necropolis 4 at the Copper Age site of Marroquíes Bajos. They are shared here in accordance with the NSF Data Management Plan associated with Doctoral Dissertation Improvement Grant BCS-1440017.
- Keyword:
- Iberia, Archaeology, Copper Age, and Bioarchaeology
- Discipline:
- Social Sciences
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