A sphere of influence is an area in which the City has power to affect developments although it has no formal authority.

Resources produced for the following conference paper and its extended version:

J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in 2nd European Network Intelligence Conference (ENIC), 2015, pp. 83–90. 〈hal-01164453〉 DOI: 10.1109/ENIC.2015.20

J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” Social Network Analysis and Mining 6:25, 2016. 〈hal-01203171〉 DOI: 10.1007/s13278-016-0329-x

It was partly funded by the French National Research Agency (ANR), through the project ImagiWeb ANR-12-CORD-0002.

This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.

File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).

Contact: Jean-Valère Cossu <jean-valere.cossu@alumni.univ-avignon.fr>

The source code used to generate these output is available on GitHub: https://github.com/CompNet/Influence.
Raw data are available through the official RepLab page: http://nlp.uned.es/replab2014/ (follow http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz)

RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.

The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all.

Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.

This statistic ranks the social media content formats that influence internet users in the United States to purchase a product. According to the April 2017 findings, 86 percent of respondents stated that video content on social media influenced them to make a product purchase.

Explore INFLUENCE AT WORK through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets

Critical to any regression analysis is the identification of observations that exert a strong influence on the fitted regression model. Traditional regression influence statistics such as Cook's distance and DFFITS, each based on deleting single observations, can fail in the presence of multiple influential observations if these influential observations “mask” one another, or if other effects such as “swamping” occur. Masking refers to the situation where an observation reveals itself as influential only after one or more other observations are deleted. Swamping occurs when points that are not actually outliers/influential are declared to be so because of the effects on the model of other unusual observations. One computationally expensive solution to these problems is the use of influence statistics that delete multiple rather than single observations. In this article, we build on previous work to produce a computationally feasible algorithm for detecting an unknown number of influential observations in the presence of masking. An important difference between our proposed algorithm and existing methods is that we focus on the data that remain after observations are deleted, rather than on the deleted observations themselves. Further, our approach uses a novel confirmatory step designed to provide a secondary assessment of identified observations. Supplementary materials for this article are available online.

In the southwestern US, the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989-2011 and between 25-42.5ºN, to annual metrics of the Normalized Difference Vegetation Index (NDVI; an indicator of vegetation productivity) and daily-resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern US. We mapped correlations between winter AR precipitation during landfalling ARs and 1) annual maximum NDVI and 2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. The data produced by this study include four sets of eight raster grids (total = 32 grids) representing Spearman Rank correlation coefficients for four types of comparisons across eight different latitudinal bands. Each dataset is named according to the comparison type and latitude of AR landfall. The four types of comparisons (with corresponding filenames indicated in parentheses) include: 1) annual winter atmospheric river precipitation vs. total annual winter precipitation (AR_WinterPrecip), 2) annual winter atmospheric river precipitation vs. annual maximum NDVI (AR_NDVI), 3) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire during the same year by Level IV ecoregion (AR_Fire_SameYear), and 4) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire with a 1-year lag by Level IV ecoregion (AR_Fire_OneYearLag). The eight landfall latitudes are indicated in filenames as follows: 25N, 27_5N, 30N, 32_5N, 35N, 37_5_N, 40N, 42_5N.

The adopted Sacramento General Plan includes a policy to work with the Sacramento Local Agency Formation Commission (SLAFCO) to adjust the City's Sphere of influence to be in conformity with the City's Annexation Policy. The Sphere of Influence (SOI) is defined as "a plan for the probable ultimate physical boundaries and service area of a local government agency." All proposed annexations must be located within the City's adopted SOI before they can be considered by SLAFCO. Contact GIS at: sacgis@cityofsacramento.org

State law requires sellers of real property to disclose "any fact materially affecting the value and desirability of the property". Assembly Bill 2776, which went into effect January 1, 2004, requires such disclosure when the property is either within two miles of an airport or if it is within an "airport influence area". The disclosure notice must state that the property may be subject to noise, vibration, odors or other annoyances or inconveniences "associated with proximity to airport operations". This law defines the "airport influence area" as the area where airport-related factors "may significantly affect land uses or necessitate restrictions on those uses as determined by an airport land use commission". The California Public Utilities Code establishes airport land use commissions in every county to provide for the orderly development of air transportation and ensure compatible land uses around airports which are open to public use. According to the State Division of Aeronautics, the "airport influence area" is usually the planning area designated by an airport land use commission for each airport. Please click here to see the Los Angeles County Airport Land Use Commission portion of our website for maps and documents. You can also review the following document from the State of California for further information: California Airport Land Use Planning Handbook. SOURCE: Los Angeles County Airport Land Use Plan (see Bibliography on P. 18); adopted 1991 / revised 2004; Brackett Field Land Use Compatibility Plan (adopted 12/9/15).All airport layers can be seen and interacted with together in our A-NET GIS web mapping application - click here.NEED MORE FUNCTIONALITY? If you are looking for more layers or advanced tools and functionality, then try our suite of GIS Web Mapping Applications.

How do policies in international organizations reflect the preferences of powerful institutional stakeholders? Using an underutilized data set on the conditions associated with World Bank loans, we find that borrower countries that vote with the United States at the United Nations are required to enact fewer domestic policy reforms, and on fewer and softer issue areas. Though U.S. preferences permeate World Bank decision making, we do not find evidence that borrower countries trade favors in exchange for active U.S. intervention on their behalf. Instead, we propose that U.S. influence operates indirectly when World Bank staff — consciously or unconsciously — design programs that are compatible with U.S. preferences. Our study provides novel evidence of World Bank conditionality and shows that politicized policies can result even from autonomous bureaucracies.

raw data on the status of military influence on different atolls

Feed the Future (FtF) seeks to reduce poverty and undernutrition in 19 developing countries by focusing on accelerating growth of the agricultural sector, addressing the root causes of undernutrition, and reducing gender inequality. This dataset captures data in the geographic areas within Tajikistan known as Zones of Influence (ZOI) targeted by FtF interventions. These data cover the Tajikistan FtF population-based survey )PBS) and secondary sources that serve as the baseline values for the U.S. Government's FtF initiative led by USAID.

Abstract Work underwent transformations that changed the values and determinants of their meanings, putting its centrality in check. This research investigates the meanings of work among Brazilians, as well as the influence of demographic and structural elements on this attribution. The meanings of work refer to individual interpretation, influenced by the social context, about work and what it represents. World Values Survey Brazilian’s sample was used. The influence of socioeconomic and structural characteristics was analyzed via structural equation modeling. The model was well adjusted, having a coefficient of determination of .951. Descriptive results indicated high valuation of work and strong perception of it as a social obligation. The SEM results indicated that men attribute higher meaning to work compared to women and that increasing age influences the attribution of meaning to work. Activities with creativity, intellectuality and independence have indirect (via NSE) and negative influence on the perception of work meanings. Analyzes prioritized the articulation between social and economic aspects with the process of meaning of work, a perspective little explored in the Brazilian’s scientific production, but fundamental for a broader understanding of the phenomenon, especially in stratified societies such as Brazil.

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network—a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Dataset of the influence of temperature on the emissions of organophosphate flame retardants from polyisocyanurate foam: measurement and modelling. This dataset is associated with the following publication: Liang, Y., X. Liu, and M. Allen. The Influence of Temperature on the Emissions of Organophosphate Ester Flame Retardants from Polyisocyanurate Foam: Measurement and Modeling. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 233: 347-354, (2019).

This is the historic impact factors of Social Influence computed for each year in CSV format. The first column shows the exaly JournalID for mixing this table with those of other journals

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. 13 datasets were submitted including one household dataset comprising of 1,900 records and a roster dataset with 9,809 records. Other datasets were a Women dataset (N=1,382), a children dataset (N=1,346), and 9 modules-based or indicators-based datasets.

LAFCO designated sphere of influence.Data is published on Mondays on a weekly basis.

An enduring concern about democracies is that citizens conform too readily to the policy views of elites in their own parties, even to the point of ignoring other information about the policies in question. This article presents two experiments that suggest an important condition under which the concern may not hold. People are rarely exposed to even modest descriptions of policies, but when they are, their attitudes seem to be affected at least as much by those descriptions as by cues from party elites. The experiments also include measures of the extent to which people think about policy, and contrary to many accounts, they suggest that party cues do not inhibit such thinking. This is not cause for unbridled optimism about citizens’ ability to make good decisions, but it is reason to be more sanguine about their ability to use information about policy when they have it.

Explore Hip-hop-Influence through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets