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At least 19 records

Social relations: A critical reflection on the notion of social impacts as change

This article seeks to reflect upon the dominant conception of social impacts as the change produced by development projects and programs, and the ways in which those affected perceive and experience them. Identifying change may be a necessary but not sufficient step in acknowledging the complexity of social life. Engaging with critical scholarship produced in the fields of both social impact assessment (SIA) and of the social studies of technical/planned interventions, I discuss how the understanding of social impacts as change responds ultimately to a causal–instrumental logic that, in order to make sense of the complexity of social life, tends to reduce it to a series of variables and matrices. I suggest a complementary dialectical approach focusing on social relations. This approach, allows an alternative means of analysing social impacts concerning the way policies and projects reconfigure conditions and possibilities on a societal level. To accomplish this, and in order to go beyond the sequence of potential impacts (or changes) and their generic indicators, I propose a set of analytical questions that highlight how social relations are structured. Besides, on the assumption that development is both a form of governance and a space of contestation, negotiation, and activism, this approach may contribute to further the potential for reflection and mobilisation that the practice of SIA presents. - Highlights: •Change, which is inherent to social life, is insufficient to determine social impacts. •The critique of causal-instrumental logic provides insights to reflect on social impacts. •Social impacts should rather refer to how interventions reconfigure social relations. •The complex, mutually constitutive nature of social phenomena may thus be recognized. •SIA should go beyond change to the understanding of its socio-political significance.

54 ENVIRONMENTAL SCIENCES↗

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.

Gunaratne, Chathika↗

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗

The social vulnerability approach for social impact assessment

Highlights: • Social vulnerability is a process in two stages: the Structural and the Acquired.• SVA is apt for the diagnosis and management of social impacts caused by projects. • SVA is based on the Natural disaster and the Assets Based approaches. • Structural Vulnerability influences the very nature of the project. • Understanding the project as a possible hazard improves its analysis. - Abstract: The Social Impact Assessment (SIA) literature has highlighted the need to focus attention on the most vulnerable groups to improve the management of socio-environmental risk. However, methodological proposals to introduce vulnerability in SIAs are still incipient. The aim of this article is to present a proposal to analyze the role of social vulnerability in the production of risks generated by large infrastructure projects. Taking the theoretical-methodological advances made from the social vulnerability and natural disasters approach, and from the social vulnerability, development and poverty approaches, we have designed a Social Vulnerability Approach (SVA) which we aim at incorporating into the SIA of large infrastructure projects. To illustrate our methodological proposal, the case of the socio-environmental conflict generated by the HydroAysen Project (which entailed the construction of a set of hydroelectric dams on the Baker and Pascua rivers in the Chilean Patagonia region) has been selected. Our proposal allows us to identify elements of vulnerability throughout the whole project cycle and, therefore, windows for intervention in order to reduce social vulnerability as well as other kind of hazards that large infrastructure projects may generate on affected populations.

54 ENVIRONMENTAL SCIENCES↗

Grandmaster: Interactive text-based analytics of social media

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

97 MATHEMATICS AND COMPUTING↗

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗

Social media based NPL system to find and retrieve ARM data: Concept paper

Information connectivity and retrieval has a role in our daily lives. The most pervasive source of online information is databases. The amount of data is growing at rapid rate and database technology is improving and having a profound effect. Almost all online applications are storing and retrieving information from databases. One challenge in supplying the public with wider access to informational databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it may not be practical to make the public aware of the structure of the database. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide more intuitive method for generating database queries and delivering responses. Social media makes it possible to interact with a wide section of the population. Through this medium, and with the help of Natural Language Processing (NLP) we can make the data of the Atmospheric Radiation Measurement Data Center (ADC) more accessible to the public. We propose an architecture for using Apache Lucene/Solr [1], OpenML [2,3], and Kafka [4] to generate an automated query/response system with inputs from Twitter5, our Cassandra DB, and our log database. Using the Twitter API and NLP we can give the public the ability to ask questions of our database and get automated responses.

Devarakonda, Ranjeet↗

BEPAM-E Model Code and CABBI Simulation Results for "Repeal of the Clean Power Plan: Social Cost and Distributional Implications"

The dataset consists of results and various input data that are used in the GAMS model for the publication "Repeal of the Clean Power Plan: Social Cost and Distributional Implications". All the data are either excel files or in the .inc format which can be read within GAMS or Notepad. Main data sources include: agriculture, transportation and electricity data. Model details can be found in the paper and the GAMS model package.

carbon abatement↗

Method for estimating the social sustainability of infrastructure projects

Nowadays, sustainability assessments tend to focus on the biophysical and economic considerations of the built environment. Social facets are generally underestimated when investment in infrastructure projects is appraised. This paper proposes a method to estimate the contribution of infrastructure projects to social sustainability. This method takes into account the interactions of an infrastructure with its environment in terms of the potential for short and long-term social improvement. The method is structured in five stages: (1) social improvement criteria and goals to be taken into account are identified and weighed; (2) an exploratory study is conducted to determine transfer functions; (3) each criterion is homogenized through value functions; (4) the short and long-term social improvement indices are established; and finally, (5) social improvement indices are contrasted to identify the socially selected alternatives and to assign an order of priority. The method was implemented in six alternatives for road infrastructure improvement. The results of the analysis show that the method can distinguish the contribution to social sustainability of different infrastructure projects and location contexts, according to early benefits and potential long-term equitable improvement. This method can be applied prior to the implementation of a project and can complement environmental and economic sustainability assessments. - Highlights: •Method to select suitable infrastructure projects from the social sustainability point of view •Emphasizes social interactions of the infrastructure in the short and long term •Distinguishes the social sustainability of infrastructure projects in different locations •Efficiency of a social contribution in terms of early social benefits and a long-term distribution •Supports early decision-making of public agencies regarding infrastructure projects.

54 ENVIRONMENTAL SCIENCES↗