An External Communication Audit of the Kentucky Arts Council: An Analysis of the Publications Distributed by a State Agency

Problem The specific objective of the audit is to conduct a formal examination of how the Kentucky Arts Council (KAC) is communicating with target authorizing groups outside of KAC, such as artists, arts administrators, educators, media and legislators. Research Strategy The external communication audit is an instrument used to gauge authorizers’ feedback on the publications distributed regularly by the agency. This audit is designed to paint a picture of the current communication patterns, policies, practices, capabilities and needs in order to make informed recommendations for the improvement of communication. The specific research question guiding this audit is: How effective are the publications distributed by the Kentucky Arts Council in communicating with the authorizers of the Arts Councils work? The audit focuses on the external communications of KAC, however, interviews have been conducted with program directors that will help pinpoint areas of additional research and provide insight into the staff’s influence on communications. A focus group was held with a random sample of members of target authorizing groups. The surveys were developed based on the information gathered in interviews and the focus group then distributed by mail to a random sample. The survey was designed to collect data about the strengths and weaknesses of current communications as well as gather information about the needs of authorizing groups. By collecting and measuring the data, the end result of the audit is to learn how to better communicate with authorizing groups. Major Findings Early on during the investigation of the Arts Council it became clear that there were no standardized methods of gauging feedback from authorizing groups about publications. When asked, fewer than 65% of respondents that said they receive publications from KAC said they read or refer to them on a regular basis. There is a strong preference for print publications among some authorizing groups, while the majority of publications distributed are available only in an online format. Another major finding is that several of publications lack relevance to the personal interests of target authorizing groups. Recommendations Based on the results of my research, I recommend that KAC develop methods of asking for constituent feedback on a regular basis. They should also make a conscious effort to provide and make easily available print copies of publications as well as online publications when possible. The majority of the respondents indicated as a level of importance that publications are relevant to their specific work or personal interests. This information might be grounds for future research to be conducted to identify the specific needs and interests of each group. When possible, publications should be individually tailored to the authorizing groups that KAC has identified as most important to its work to reduce unnecessary information overload

An Examination of Organizational Resources' Influence on the Hyperlink and Political Networks of Organizations

National housing social movement organizations transformed and publicly reconstructed their network structures online and offline during two different political environments (pre-housing bubble and post-housing bubble). This study investigates how this process takes place in a changing political environment. This study uses two different network types: an online hyperlink network and a network of co-participation in congressional hearings. Through a comparison of resources deployed and resulting social solidarities gathered and lost, this study found that during the United States economic recession during 2007–2010, network centrality has decreased in the hyperlink network where organizations have more agency. The majority of ties in the network of co-participation in congressional hearings were attributed to organizations of similar age and housing focus. Implications are discussed from theoretical, methodological, and practical points of view.ye

Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis

This study examines machine learning methods used in crisis management. Analyzing detected patterns from a crisis involves the collection and evaluation of historical or near-real-time datasets through automated means. This paper utilized the meta-review method to analyze scientific literature that utilized machine learning techniques to evaluate human actions during crises. Selected studies were condensed into themes and emerging trends using a systematic literature evaluation of published works accessed from three scholarly databases. Results show that data from social media was prominent in the evaluated articles with 27% usage, followed by disaster management, health (COVID) and crisis informatics, amongst many other themes. Additionally, the supervised machine learning method, with an application of 69% across the board, was predominant. The classification technique stood out among other machine learning tasks with 41% usage. The algorithms that played major roles were the Support Vector Machine, Neural Networks, Naive Bayes, and Random Forest, with 23%, 16%, 15%, and 12% contributions, respectively

Understanding Decision-Making Needs of Open Government Data Users

Open Government Data (OGD) portals make data publicly available to promote transparency, innovation, and value creation. Although these data sets are available and used by a broad audience, little is known about how users engage with this data and the websites where they are hosted. The City of Cincinnati hosts an award-winning Open Government Data Portal and is used as a case study in this paper to understand the decision-making needs of OGD end-users. The portal allows users to access local data sets such as crime reports, permits and licenses, market analysis, education/research data, viewing public safety, and public health, as part of a local OGD initiative. To investigate users’ social, economical, political and other decision-making needs, this study is conducted in two steps 1) a think-aloud activity, and 2) a design iteration combined with heuristic evaluation. Observing the use of the portal through this user study provided insights into user expectations as well as system and information requirements illustrated in design implications for OGD systems

Data Census of a Geographically-Bounded Tweet Set to Enhance Common Operational Picture Tools

Location information is of particular importance to crisis informatics. The Twitter API provides several methods to assess a rough location and/or the specific latitude and longitude in which a post originated. This paper offers a comparison of location information provided by Twitter’s four geolocation methods. The study aggregates one month of data from the greater Cincinnati, Ohio metropolitan area and assesses the relative contribution that each method can make to common operational picture tools used by crisis informatics researchers. Results show that of 49,744 Tweets, 4% contained geotags, 85.2% contained a location in the users’ profile, and 3.5% contained no apparent location data, but were gathered using the bounding box method and would not have been identified using traditional methods of gathering data using geotagged Tweets or user profile information alone. We reflect on these results in light of design implications for common operational picture tools (COPs)

A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing

This study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation. The proposed framework examines all the unique features in the various expressions within a text to resolve the contextual usage of all tokens and decipher the effect of negation on sentiment analysis. The application of popular expression detectors skips this important step, thereby neglecting the root words caught in the web of negation and making text classification difficult for machine learning and sentiment analysis. This study adopts the Natural Language Processing (NLP) approach to discover and antonimize words that were negated for better accuracy in text classification using a knowledge base provided by an NLP library called WordHoard. Early results show that our initial analysis improved on traditional sentiment analysis, which sometimes neglects negations or assigns an inverse polarity score. The SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob by 6%

It Takes a Village: A Case for Including Extended Family Members in the Joint Oversight of Family-based Privacy and Security for Mobile Smartphones

We conducted a user study with 19 parent-teen dyads to understand the perceived benefits and drawbacks of using a mobile app that allows them to co-manage mobile privacy, safety, and security within their families. While the primary goal of the study was to understand the use case as it pertained to parents and teens, an emerging finding from our study was that participants found value in extending app use to other family members (siblings, cousins, and grandparents). Participants felt that it would help bring the necessary expertise into their immediate family network and help protect the older adults and children of the family from privacy and security risks. However, participants expressed that co-monitoring by extended family members might cause tensions in their families, creating interpersonal conflicts. To alleviate these concerns, participants suggested more control over the privacy features to facilitate sharing their installed apps with only trusted family members