VARIACIÓN DE PRECIOS DE LA PAPA Y PROMEDIO PROYECTADO
7.5.5 Ejercicio Práctico #
Sexual assault is a pervasive, societal issues that has recently been brought to the forefront of society through the #MeToo movement on social media. This research sought to identify barriers for reporting sexual assault by analyzing the hashtag #WhyIDidntReport. The findings of this study highlighted some important aspects of the network for everyone who has participated, whether they were supporters of the movement or not.
A full network analysis showed that the hashtag produced a lot of different communities within the network that were densely connected. Despite the number of communities, the network was not splintered and was highly efficient. It was also structurally centered around influential users who were able to create a shared meaning for the hashtag and subsequently drive the conversation around reporting issues. These network characteristics were significant to victims’ abilities to share their stories on Twitter because high levels of support and community prioritized victims’ voices.
Even though characteristics of dialogic communication are not always present on social media, platforms like Twitter have proven to be viable for hosting a supportive and uplifting communities for victims of sexual assault. In looking at sentiment and emotion, we realize that the public is favorable towards victims of sexual assault even though negative sentiment and emotions prevailed. An informal look at the discourse in tweet replies also provided insight on which sentiments and emotions were driving the conversation and how different characteristics could encourage or deter victims from sharing their stories.
An analysis of conflicts in #WhyIDidntReport tweets coincide with topics in Planned Parenthood’s Get Real curriculum. The solution to a lot of issues within sexual assault is to expose people to evidence-based, accurate prevention and intervention methods through
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comprehensive sex education in schools. Learning about things like communication skills, refusal tactics, healthy relationships, and responsible decision making will contribute to more knowledge about sex and relationship but are also transferable skills that contribute to better overall interpersonal relationships.
Conducting different types of analysis allowed me to mine data and gain insights about a vulnerable population. These insights can inform agencies about trends and themes and how to make agency improvements without prodding victims of sexual assault. I believe as analysis tools improve and more diverse data is available, it will allow agencies to be proactive in their efforts to support victims in their communities, thus improving their organization-public relationships.
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57 Appendix A
Traffic to the U.S. Equal Employment Opportunity Commission’s Page About Sexual Harassment from April to August 2017
58 Appendix B
59 Appendix C
Network Terminology Used on Network Analysis and Visualization Software Gephi Basic Network Terminology
• Vertex - A vertex is simply drawn as a node or a dot.
• Edge - An edge (a set of two elements) is drawn as a line connecting two vertices, called endpoints or end vertices or end vertices.
o Directed Edge - A directed edge is an ordered pair of nodes that can be represented graphically as an arrow drawn between the nodes.
o Undirected Edge - An undirected edge disregards any sense of direction and treats both nodes interchangeably.
• Node Degree - The degree of a node in a network is the number of connections it has to other nodes and the degree distribution is the probability distribution of these degrees over the whole network.
o Out Degree - The number of edges leaving a vertex. o In Degree - The number of edges entering a vertex. • Size - The size of a graph is the number of its edges.
• Weight - A weighted graph associates a label (weight) with every edge in the graph. Weights are usually real numbers. The weight of an edge is often referred to as the "cost" of the edge. In applications, the weight may be a measure of the length of a route, the capacity of a line, the energy required to move between locations along a route, etc. Network Overview
• Average Degree - Average number of links per node.
• Average Weighted Degree - Average of sum of weights of the edges of nodes.
• Distance - The distance between two nodes is defined as the number of edges along the shortest path connecting them.
• Average Distance - The Average of distance between all pairs of nodes.
• Network Diameter - The maximum distance between any pair of nodes in the graph. • Modularity - Modularity is one measure of the structure of networks or graphs. It was
designed to measure the strength of division of a network into modules (also called groups, clusters or communities). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.
• Connected Components - a connected component (or just component) of an undirected graph is a subgraph in which any two vertices are connected to each other by paths, and which is connected to no additional vertices in the supergraph.
Node Overview
• Clustering Coefficient - a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together.
• Centrality - centrality refers to indicators which identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social
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network, key infrastructure nodes in the Internet or urban networks, and super spreaders of disease.
o Closeness Centrality - In connected graphs there is a natural distance metric between all pairs of nodes, defined by the length of their shortest paths. The farness of a node is defined as the sum of its distances to all other nodes, and its closeness is defined as the reciprocal of the farness. Thus, the more central a node is the lower its total distance to all other nodes.
o Betweenness Centrality - Betweenness is a centrality measure of a vertex within a graph (there is also edge betweenness, which is not discussed here).
Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes.
o Eigenvector Centrality - Eigenvector centrality is a measure of the influence of a node in a network. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.
Edge Overview
• Average Path Length - Average path length is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network.
61 Appendix D
65 Appendix E
72 Appendix F
77 VITA
Jordyn Nicole Warren was born in Allentown, Pennsylvania. She received her bachelor’s degree from Appalachian State University in 2015, where she played Division I field hockey. She always had a passion for social issues and used this opportunity to look at ways the mass media can improve social issues for marginalized groups in society.