Kim Hemmings-Jarrett, Ph.D.

K. Hemmings-Jarrett, J. Jarrett and M. B. Blake, "(WKSP) Sentiment Analysis of Twitter Samples That Differentiates Impact of User Participation Levels," 2018 IEEE International Conference on Cognitive Computing (ICCC), San Francisco, CA, USA, 2018, pp. 65-72, doi: 10.1109/ICCC.2018.00016.

Abstract: The microblogging social media platform Twitter, accounting for millions of 'tweets' per day, provides an effective platform for sampling conversations on a wide array of topics and influences a variety of research areas. Coupled with the presupposition that online conversations often mirror offline conversations, many researchers leverage Twitter samples to justify conclusions about the larger population. More recently, researchers are sampling Twitter for sentiment analysis or opinion mining on products and services and, relevant to this work, for political and social commentary that may lead to election prediction. Traditionally, sentiment analysis has been visualized as an aggregation of opinions expressed in the content discussed online, while neglecting the presence of the creators of the content and the impact of their varying levels of participation. This paper illustrates and proposes an alternative model for evaluating and visualizing sentiment using Twitter samples while leveraging and highlighting user participation and impact.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8457697&isnumber=8457681

K. Hemmings-Jarrett, J. Jarrett and M. B. Blake, "Evaluation of User Engagement on Social Media to Leverage Active and Passive Communication," 2017 IEEE International Conference on Cognitive Computing (ICCC), Honolulu, HI, USA, 2017, pp. 132-135, doi: 10.1109/IEEE.ICCC.2017.24.

Abstract: Individuals in society differ ideologically both online and offline. As the nature of discussions and communication evolve, so do the dynamics within collective groups. User participation on issues such as political discourse affect the opinions of collective groups prior to, during, and after the occurrence of significant events. Changes in engagement can be influenced by choice in words during these discussions. This results in naturally insulating effects that prevent a more comprehensive discussion, and a further challenge exists when opposing, less vocal voices, have a disproportional impact in less conspicuous ways. This paper introduces a communicative model to understand event stimuli triggering user participation of both active and passive actors. This approach contributes to developing more engaging on-line discussion as the nature of the communication evolves.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8029234&isnumber=8027287

J. Jarrett, K. Hemmings-Jarrett and M. B. Blake, "Towards a Service-Oriented Architecture for Pre-Processing Crowd-Sourced Sentiment from Twitter," 2019 IEEE International Conference on Web Services (ICWS), Milan, Italy, 2019, pp. 163-171, doi: 10.1109/ICWS.2019.00037.

Abstract: Online social media platforms like Twitter, provide opinion rich repositories for conducting sentiment analysis. Users engage in open discussions on a variety of topics across a wide cross-section of problem domains. Commercial, government, educational, non-profit and other types of agencies are increasingly relying on extracting conversations on Twitter to determine the general sentiment of the public on particular topics, products, services and issues. Despite being readily available and in abundance, it is also laced with nuances which can disrupt, skew and potentially lead to inaccurate analysis if not handled properly. In this paper, we propose an SOA framework to enable the pre-processing of data origination on Twitter, and configurable components that allow data consumers to filter the data using useful social media signals.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8818395&isnumber=8818298

K. Hemmings-Jarrett, J. Jarrett and M. B. Blake, "Evaluation of a Reusable Technique for Refining Social Media Query Criteria for Crowd-Sourced Sentiment for Decision Making," 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, 2019, pp. 379-388, doi: 10.1109/IRI.2019.00065.

Abstract: There are three categories of users that consume social media data either for their personal use or for aggregation and presentation to others. These users rely on a preferential combination of Social Media Signals (SMS) that satisfies their information goals and aids in their decision-making. The research community is split on how to deal with some signals such as text originating from robotic voices; some suggest removing them while others are more interested in better identifying them. This paper statistically tests the SMS's in a dataset gathered during one of the political debates during the US Presidential Elections in 2016. It introduces a reusable technique aimed at contributing to the iterative and symbiotic user-system relationship, while improving the opportunity for arriving at empirically supported results for decision-making instances regardless of the consumer group.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8843474&isnumber=8843429

Kimberley Hemmings-Jarrett, Swathi Jagannath, Ali Jazayeri, and Denise Agosto. 2019. "We Need More Than Laptops!": Technology Assistance for Transitioning International Students. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing (CSCW '19). Association for Computing Machinery, New York, NY, USA, 216–220. https://doi.org/10.1145/3311957.3359461

Abstract: Although information and communication technologies (ICT's) have become pervasive across various aspects of human endeavors including educational settings, their adoption and diffusion vary among different developing and developed countries, often described as the digital divide. In this work, we examined concerns and considerations of students from developing countries transitioning into graduate and undergraduate studies in developed nations and assessed the personal and institutional resources available to them during their transition along with specific focus on the degree of their exposure to ICT's before and after their transition. The results showed that respondents had experienced different levels of technology exposure in their home countries, and that neither their sources of assistance in their home countries nor in their host countries provided the required aid for transitioning across the digital divide.
URL: https://dl.acm.org/doi/pdf/10.1145/3311957.3359461?casa_token=qd2ZBECajf8AAAAA:PpWJrCvyMTtjfAs-C1uLDtQgysz01HhAMq3lmOI_pTYaWu1Byfzn0WU8ExKdH664ihMeWx6Ug-TK_kg 

Hemmings-Jarrett, Kimberley; Jarrett, Julian; Hylton, Jasmin; Williams, Shemar; and Campbell, Yanelle, "A Preliminary Examination of Power Relationships’ Influence on Reducing the Digitally Marginalized Student Population in Jamaica." (2022). AMCIS 2022 Proceedings. 10.
https://aisel.aisnet.org/amcis2022/sig_si/sig_si/10

Abstract: The Digital Divide has historically been viewed as the separation of people from technology based primarily on socio-economic issues and other demographic vulnerabilities. More recent studies have expanded research into investigating the impact of other societal structures such as the power of the government on equitable distribution of Information & Communication Technologies (ICTs) and other resources to the digitally marginalized population. While many strides have been made, educating children in the post-Covid era has made resolving the Divide a more pressing issue for students living in developing countries like Jamaica, where pre-pandemic, technology in education was still in its infancy. Using the Grounded Theory methodology, this research introduces preliminary findings demonstrating four power-relationships that have the power to and the power over effectively bridging the Divide and amongst the youngest of the digitally marginalized population and its effect on state-sponsored initiatives such as the OYOD.
 

K. Hemmings-Jarrett, T. Barnett, J. Jarrett, M. B. Blake and D. Agosto, "Quality not Quantity! A Qualitative Evaluation and Proposal for Understanding the Depth of Audience “Knowledge” Post Data Extraction," 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA, 2020, pp. 164-171, doi: 10.1109/IRI49571.2020.00031.

Abstract: Knowledge is defined as...the result of machine extracted patterns; humans making sense of their environment; information generated and aggregated from software services or as the lowest form of human cognition. Different perspectives, different domains, but one concept. Information scientists are often concerned with retrieving knowledge from data sources and sharing that knowledge with concerned stakeholders; with such differing views on what qualifies as knowledge a cross-domain approach might prove beneficial. This work is a qualitative assessment of the layers of knowledge intended to bridge the gap between the analyst and their intended or unintended audiences. It examines the benefit of abstracting concepts used in the education discipline to justify including a post-evaluation stage to the Knowledge Discovered through Databases (KDD) framework. It also intends to promote awareness of the various human cognitive capacities and provide a useful approach for communicating and evaluating machine-extracted knowledge that supports higher order thinking.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9191399&isnumber=9191372

K. Hemmings-Jarrett, J. Jarrett and M. B. Blake, "A Taxonomy for Classifying User Group Activity in Online Political Discourse" 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC), Los Angeles, CA, USA, 2019, pp. 71-80, doi: 10.1109/CIC48465.2019.00018.

Abstract: Twitter has become a popular platform for political conversation. Within the TwitterSphere, multiple users contribute to the discourse through different modalities and with varying degrees of engagement. In this paper, we seek to profile the different groups of users present in online political discourse. We provide a taxonomy that can be used to classify the various users, determine each group's representation and their respective share of the online conversation. Along with core information foraging concepts, the taxonomy aims to reduce the foraging costs associated with exploratory searching. This work provides a practical approach for analyzing and pre-processing social media data and offers valuable recommendations for future expansion into sentiment analysis.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8998496&isnumber=8998468