Hand Coding Research Articles

American social media users have ideological differences of opinion about the War in Ukraine

Though ideological differences have long been a ubiquitous feature of American politics, the rise of online news and social media has exacerbated divisions between groups. While existing research has documented how political preferences manifest online, relatively few studies have considered whether ideological divisions extend to discussions of foreign policy. We examine this question by analyzing nearly 2 million tweets about the war in Ukraine posted by Americans during the opening stages of the Russian invasion. We first categorize each tweet according to the user’s ideological leanings estimated by the network of political accounts they follow. Then, we apply a natural language processing model specifically designed for short texts to classify the tweets into clusters that we hand code into substantive topics. We find that the topic distributions of conservative, moderate, and liberal users are substantively and statistically different. We further find that conservatives are more likely to spread some form of misinformation and that liberals are more likely to express support for Ukraine. Our paper concludes with a discussion of the implications of our findings for the conduct of U.S. foreign policy.

Linking policy design and policy diffusion to advance both theories: Evidence from the elements, attributes, and adoptions of Uniform Law Commission model legislation

AbstractWe integrate the study of policy design and diffusion theoretically and empirically to advance each and raise novel research questions. We argue that policy dissemination is an important rationale in the design of model legislation and present a theory for how each of the five elements of design map onto the policy innovation attribute of complexity. Empirically, we both hand code policy design elements and use a novel textual approach to measure policy complexity across 84 model policies promulgated by the Uniform Law Commission, an interstate organization that writes model laws with input from 50 state delegations of lawmakers and disseminates the bills for passage in as many states as possible. We assess how the elements of policy design map onto a policy's textual complexity. Additionally, we find that more complex policies are less likely to be adopted and are slower to be adopted, though the strength of this relationship depends on the measurement approach. We conclude with the future directions of this research, including how new text analysis tools and large policy datasets can help to solve important measurement and analysis challenges.

Continuous sign language recognition algorithm based on object detection and variable-length coding sequence

Currently, continuous sign language recognition faces challenges such as difficulty in acquiring skeletal data, long training time for Three-Dimensional convolutional neural networks, and easy occlusion and blurring of hands. To address these problems, this paper proposes a continuous sign language recognition method based on target detection and coding sequence. The algorithm uses Dual-branch Shuffle Attention Mechanis-You Only Look Once version X (DSA-YOLOX) detection network to detect the head and hands, and encodes the sign language video according to the partition to achieve the transformation from Three-Dimensional to One-Dimensional; and then uses the proposed Bi-directional Long Short-Term Memory (BiLSTM) hand coding sequence classification model with jointly weighted Fast Dynamic Time Warping (FastDTW) to extract hand coding similarity and features while reducing the number of parameters to achieve the classification and recognition of unequal-length hand coding sequences. From the results of ablation experiments and comparison experiments, all parts of the improvement perform well. The word error rate (WER) of this paper’s method is reduced by 21.26% compared to Dynamic Time Warping-Hidden Markov Model (DTW-HMM) and 11.53% compared to Long Short-Term Memory-A(LSTM-A); the Giga Floating-point Operations Per Second(GFLOPs) of the algorithm are reduced dramatically, which is about 1/13 of the Visual Alignment Constraint(VAC) model and 1/57 of the Spatial-Temporal Multi-Cue(STMC) model; and the algorithm takes better account of the speed and accuracy of sign language recognition.

Role-Play Based Gamification for Communication Skills and Nursing Competence in Internal Medicine Nursing

Objective: This study aims to the effect of role-play-based gamification on the analysis of symptoms in Internal Medicine Nursing, decision making, administration of appropriate nursing care, and the communication skills of nursing students. Methods: The study was designed in participatory action research as a qualitative design. In the action research, gamification elements were constructed on a role-play performance of the students in Nursing Department at a private university in Ankara-Türkiye. A gamification based on systems role play was performed for students taking the internal medicine nursing course (n=10). Checklists for each system regarding the selection and use of equipment appropriate for nursing interventions have been added to the games. At the end of the semester they were asked to evaluate this intervention via qualitative interview. In the in-depth interviews, data were collected by asking the students 9 unstructured open-ended questions prepared by the researchers. Data analysis was carried out by hand coding by the researchers. Codes were created from the research objectives based on Werbach's D6 Model for gamification design. Results: The qualitative analysis revealed six major themes (i) Defining Business Goals (ii) Delineating Target Behaviors (iii) Describing Players (iv) Devising Activity Loops (v) Deploying Tools (vi) Not forgetting the fun. In these themes, students stated that by using game elements in lessons, it became easier to remember and apply theoretical knowledge about internal medicine nursing, improved their practical skills, time management skills and critical decision-making abilities, and that they better understood the importance of effective communication within the team. Additionally, students stated that the experience of learning the basic aspects of internal diseases, which are difficult to learn in theory, became fun with the gamification technique. Conclusion: The use of gamification, students learned nursing care and communication skills related to Internal Medicine Nursing education in a motivated and more permanent manner.

Automated Motive Scoring and International Crisis Behavior

Abstract Although implicit motives are well established, coding for implicit motives has been a time-consuming and difficult manual process. This study (i) evaluates automated motive coding schemes for Need for Achievement, Need for Affiliation, and Need for Power, (ii) assesses the relationship between these three motives and crisis initiation, and (iii) assesses the explanatory power of other psychological variables for crisis behavior. The results establish the convergent validity of the Need for Achievement and Need for Power coding schemes with hand coding and support findings of a significant relationship between Need for Power and crisis behavior. A unit increase in Need for Power doubles the probability that a leader will initiate a crisis. Even stronger models were obtained by including other previously studied psychological variables. The scope of the dataset (154 leaders across a diverse set of 93 countries) lends weight to the findings and provides many opportunities for additional research.

U.S. and Canadian Higher Education Protests and University and Police Responses, 2012 to 2018

The authors describe protest patterns at U.S. and Canadian universities in the 2010s. The research draws on a new dataset, the Higher Ed Protest Event Dataset, which combines machine learning and sociological hand coding of 16,069 campus newspaper articles. The sample consists of 5,553 higher ed protests involving 584 universities and colleges between 2012 and 2018. The dataset also includes university and police responses to a subset of protests. The authors find that protest frequency is patterned by the academic calendar. The top issue in both U.S. and Canadian higher education protests was university administration and governance. The comparative analysis reveals distinctive patterns in other issues raised and protest intensity. In the United States, the periods of greatest protest activity were waves of mass mobilization across the country on often racialized issues with a national dimension: racist police violence, racially hostile campus climates, and Donald Trump’s presidency. In Canada, protest activity was most intense during provincial or local campaigns led by formal student organizations and unions on issues of economic security: public tuition, austerity, and labor conditions. Across both countries, university administrations and police usually avoided extensive intervention during protests. The findings contribute to social movements research through methodological innovations and new empirical insights on movements in higher education.

The Utility of Head-Mounted Eye Gaze Tracking for Vision-in-Action Assessments to Enhance Skill Acquisition and Sport Performance: A Strengths, Weaknesses, Opportunities, and Threats Analysis

Mobile eye tracking technology enables the objective evaluation of what, when, and how athletes use visual information from the environment to control action in service of skilled performance. This review highlights the strengths, weaknesses, opportunities, and threats (SWOT) to consider before adopting this technology to evaluate visual–motor control for enhanced skill acquisition and performance in sport. Strengths include increased ecological validity by recording the athlete within their natural performance environment. Weaknesses include diminished tracking fidelity based on the eye’s morphology or squinting, eye tracker movement, and lens fogging. Opportunities consist of novel assessments around research themes such as combatting the negative effects of pressure and performance enhancement through personalized training. The formfitting technologies also introduce recording opportunities during standard practices. Data processing time, synchronization of video streams, and hand coding of variables comprise the threats in addition to the hardware cost and the fees associated with crowdsourcing data coding. Currently, it is also not feasible to provide coaches with a report of their athletes’ data in a timeline suitable to create player-specific drills. Evaluating portable eye tracking technology with a SWOT framework helps translate scientific knowledge to practitioners considering implementing this technology for athlete assessment and development.

HARDWARE DESIGN OF THE TOUCHLESS HAND CODE AND CONVOLUTIONAL NEURAL NETWORKS - BASED AUTOMATIC DOOR SECURITY SYSTEM

The spread of viruses and bacteria through touching door surfaces is essential in maintaining public hygiene and health. In this context, a hand-coded touchless automatic door hardware design has been developed to reduce the spread of diseases through touch. This research aims to create a plan that includes interface development and hardware design to open and close doors automatically without contact. In this research, the automatic door hardware response is tested based on the numeric input from the hand code represented by the numeric database. The input and output control is connected to Python's graphical user interface (GUI). The GUI system design involves tools to connect the Python programming language and the Arduino microcontroller. Based on the experimental results, the hardware design of the automatic door security system based on hand code and Convolutional Neural Networks functions appropriately.

‘Super-Unsupervised’ Classification for Labelling Text: Online Political Hostility as an Illustration

AbstractWe live in a world of text. Yet the sheer magnitude of social media data, coupled with a need to measure complex psychological constructs, has made this important source of data difficult to use. Researchers often engage in costly hand coding of thousands of texts using supervised techniques or rely on unsupervised techniques where the measurement of predefined constructs is difficult. We propose a novel approach that we call ‘super-unsupervised’ learning and demonstrate its usefulness by measuring the psychologically complex construct of online political hostility based on a large corpus of tweets. This approach accomplishes the feat by combining the best features of supervised and unsupervised learning techniques: measurements of complex psychological constructs without a single labelled data source. We first outline the approach before conducting a diverse series of tests that include: (i) face validity, (ii) convergent and discriminant validity, (iii) criterion validity, (iv) external validity, and (v) ecological validity.

Characterizing Heated Tobacco Products Marketing on Instagram: Observational Study.

Heated tobacco products (HTPs), including I Quit Ordinary Smoking (IQOS), are new tobacco products that use an electronic device to heat compressed tobacco leaves to generate an aerosol for consumers to inhale. Marketing of HTPs is prevalent on Instagram, a popular social media platform. This study aims to characterize posts related to HTPs on Instagram and their associations with user engagement. Through the Instagram application programming interface, 979 Instagram posts were collected using keywords related to HTPs, such as "IQOS" and "heat-not-burn." Among them, 596 posts were related to IQOS and other HTP marketing. The codebook was developed from a randomly selected 200 posts on the post content by hand coding, which was applied to the remaining 396 Instagram posts. Summary statistics were calculated, and statistical hypothesis testing was conducted to understand the popularity of Instagram posts on HTPs. Negative binomial regression models were applied to identify Instagram post characteristics associated with user engagement (eg, count). Among Instagram posts related to HTP marketing (N=596), "product display" was dominant (n=550, 92.28%), followed by "brand promotion" (n=41, 6.88%), and "others" (n=5, 0.84%). Among posts within "product display," "device only" was the most popular (n=338, 61.45%), followed by "heatstick only" (n=80, 14.55%), "accessory" (n=66, 12%), "device and heatstick" (n=56, 10.18%), and "capsule" (n=10, 1.82%). A univariate negative binomial regression model with pairwise comparisons across "product display" types showed that the number of likes for posts with HTP heatsticks was significantly lower compared to posts with HTP devices, accessories, and device-heatstick sets. Multivariate negative binomial regression models showed that HTP-related Instagram posts with a model or lifestyle elements (;=.60, 95% CI 0.36-0.84) or without obvious product advertising information (=.69, 95% CI 0.49-0.89) received more likes. It is shown that posts with product displays were dominant among HTP-related posts on Instagram. Posts with model or lifestyle elements are associated with high user engagement, which might be one of the web-based marketing strategies of HTPs.

Do Male and Female Legislators Have Different Twitter Communication Styles?

AbstractCommunication is a fundamental step in the process of political representation, and an influential stream of research hypothesizes that male and female politicians talk to their constituents in very different ways. To build the broad dataset necessary for this analysis, we harness the massive trove of communication by American politicians through Twitter. We adopt a supervised learning approach that begins with the hand coding of over 10,000 tweets and then use these to train machine learning algorithms to categorize the full corpus of over three million tweets sent by the lower house state legislators who were serving in the summer of 2017. Our results provide insights into politicians’ behavior and the consequence of women’s underrepresentation on what voters learn about legislative activity.

Differences in Religious Framing of Muslims and Islam in American Media Coverage Before and After the Trump Campaign

ABSTRACT Over the last 20 years Muslims have received a disproportionate amount of news media attention. In 2017 they were again thrust into the spotlight when the Trump administration issued a travel ban blocking entry from seven predominately-Muslim countries. With our hand coding of over 900 newspaper articles and mixed modeling techniques, we examine the newspaper rhetoric surrounding Muslims before and during the Trump administration and across US states. We find that after President Trump’s election, fewer newspapers mentioned religion or referenced Muslim claimsmakers. We also reveal that higher state-level religious salience was associated with more newspapers referencing religious claimsmakers in Islam-related discussions. Other state-level political and religious characteristics had no effect on the use of religious elements or Muslims claimsmakers. These results provide insight into the position of Muslims and Islam in the United States, as well as the impact of political rhetoric on media portrayals.

Sign Language Hand Code Training as an Early Intervention Program for Year One Pupils with Hearing Impairment in Ipoh, Perak: A Case Study

This study was aimed to investigate sign language skills of Kod Tangan Bahasa Malaysia (KTBM) as the early intervention program for learning sign language skills. KTBM introduced to ensure the communication of pupils with hearing impairments were introduced sign language skills hand code. This study was descriptive in the nature and data collected based on the qualitative approach paradigm. Semi-structured interviews conducted on four respondents, namely teachers and parents of the pupils with hearing impairments. The study showed that teachers argued that factors of sign language skills selected due to support the pupils in the teaching and learning process, easy-to-use sign language and parental fluency sign language skills. The interviewed findings presented on the controversy over the appropriate medium of instruction for pupils with hearing impairments is briefly discussed. This present study suggested that sign language training for the students with hearing impairments should given as early as possible before they were entered Year One to support the teaching and learning processes as well as more options of communication strategies for students with hearing impairments in the future.

Experiences of Women With Interstitial Cystitis/Bladder Pain Syndrome: What Can We Learn From Women's Online Discussions?

Interstitial cystitis/bladder pain syndrome is a debilitating chronic condition that disproportionately affects women at a ratio of 5:1. We sought to capture women's experiences with interstitial cystitis/bladder pain syndrome by conducting a large-scale digital ethnographic analysis of anonymous posts on Internet forums. Online posts were identified using condition-specific keywords and data mining extraction services. Once posts were identified, a random sample of 200 online posts was coded and analyzed by hand using qualitative methods. A Latent Dirichlet Allocation probabilistic topic model was applied to the complete dataset to substantiate the qualitative analysis and allow for further thematic discovery. A total of 6,842 posts written by 3,902 unique users from 224 websites were identified. There was a significant overlap between the hand coding and Latent Dirichlet Allocation themes. Our analysis yielded the following themes: online community engagement, triggers and disease etiologies, medical comorbidities, quality of life impact, patient experience with medical care, and alternative therapies and self-management strategies. Additionally, our population appeared to have a high burden of nonurological associated syndromes. We identified barriers to patient-centered care and found that online peer support was important for women. Our digital ethnographic analysis is a novel application of qualitative methods using online sources. Social media analytics appears to capture a broader patient population than that typically included in clinic-based qualitative studies, such as patient interviews and focus groups. Understanding patient behaviors and concerns are important to guide strategies for improving care and the overall experience with this difficult-to-treat condition.

From Semantic Vectors to Analogical Mapping

Human reasoning goes beyond knowledge about individual entities, extending to inferences based on relations between entities. Here we focus on the use of relations in verbal analogical mapping, sketching a general approach based on assessing similarity between patterns of semantic relations between words. This approach combines research in artificial intelligence with work in psychology and cognitive science, with the aim of minimizing hand coding of text inputs for reasoning tasks. The computational framework takes as inputs vector representations of individual word meanings, coupled with semantic representations of the relations between words, and uses these inputs to form semantic-relation networks for individual analogues. Analogical mapping is operationalized as graph matching under cognitive and computational constraints. The approach highlights the central role of semantics in analogical mapping.

“The Stress of the Situation”: How Do Compounding Experiences of Oppression Impact Emotional Distress Among a Diverse Sample of Internally Displaced People in Colombia?

AimsAddressing the mental health needs of Internally Displaced People (IDPs) in Colombia has been identified as a public health priority. Women and disabled IDPs are recognised as under-researched populations, with differences in vulnerabilities to displacement and resettlement prospects. This thematic network analysis employs an intersectional approach to consider how compounding experiences of oppression impact emotional distress among IDPs to enable informed and appropriate service provision.MethodsThis is a qualitative analysis of a subset of data collected by the second author and her research team in 2017–18, as part of a larger action research project. Participants were randomly selected from the Victim's register in an industrialised municipality of Colombia. A subsample (n = 20) were invited to participate in life and family histories. Units of analysis were individual (n = 11) and family interviews (n = 9), with a mixture of self-identifying disabled and non-disabled men and women. River and tree of life tools were used to elicit culturally sensitive discussion of significant life events and ongoing distress. NVivo software and hand coding techniques were used to operationalise thematic webs. The analysis employed a grounded approach to thematic network analysis.ResultsThree global themes, each underpinned by several organisational themes, were developed. The first, Environments and contexts of displacement, considers the loss of land and community alongside the myriad of social institutions, legal entitlements, family circumstances, cultural expectations and stigma influencing participants' access to resources. The second, Making sense of it all, represents the emotional and cognitive responses to perceived injustices and eroded trust. The third, Mechanisms for managing distress, represents strategies employed by IDPs at individual and family levels. Relationships between employment status and gendered divisions of labour were noted, suggesting that non-disabled women were able to meet increased domestic and paid work demands following displacement, though this was a considerable source of stress. Concepts around racial, indigenous and class identities were alluded to by several participants but could not be fully developed due to relative scarcity of accounts within the dataset.ConclusionThe thematic networks presented illustrate several compounding and interrelated oppressions faced by IDPs, offering explanation as to how this produces and sustains emotional distress. Participants’ well-founded worries about economic security and childcare alongside concerns for safety and acceptance in host communities require co-ordinated, locally informed responses. Prevention and recovery programmes should consider interventions at a family level, whilst strengthening participants’ self-developed strategies for managing distress.

Special Education Teachers’ Skills and Knowledge in Handling Students with Hearing Problems in Special Education Integration Program

This study aims to explore the challenges faced by special education teachers in the PPKI Hearing Problems from the aspect of knowledge and readiness of special education teachers with hearing problems. A qualitative study in the form of a case study was conducted by purposive sampling consisting of heteregeneous sampling involving six special education teachers with hearing problems from the Special Education Integration Program in one of the primary schools. Two types of research instruments were used in this study namely interviews, observation without participants. The data of this study were thematically analyzed by using Nvivo version 12. The results of this study show that special education teachers for hearing impairment in this study location face challenges such as lack of exposure to the world of special education for hearing impairment because most of the study samples do not have a special education degree for hearing impairment and lack skills in Signed Hand Code Language (KTBM) . The findings of this study also identify that teachers are also faced with very low classroom management skills and the guidelines provided also do not include inputs that can help special education teachers of hearing problems.

Asymmetric flooding as a tool for foreign influence on social media

AbstractResearch on Russian troll activity during the 2016 US presidential campaign largely focused on divisive partisan messaging. Here, we document the use of apolitical content—content that could counteract mobilization efforts and escape detection in future campaigns. We argue this resembled techniques used by autocratic regimes domestically, in “flooding” social media with entertainment content to distract from and displace mobilizing messaging. Using automated text analysis and hand coding to construct a timeline of IRA messaging on Twitter, we find left-leaning trolls posted large volumes of entertainment content in their artificial liberal community and shifted away from political content late in the campaign. Simultaneously, conservative trolls were targeting their community with increases in political content. This suggests the use of apolitical content might be an overlooked strategy to selectively manipulate levels of attention to politics.

Natural Language Processing for Virtual Reference Analysis

Objective – Chat transcript analysis can illuminate user needs by identifying common question topics, but traditional hand coding methods for topic analysis are time-consuming and poorly suited to large datasets. The research team explored the viability of automatic and natural language processing (NLP) strategies to perform rapid topic analysis on a large dataset of transcripts from a consortial chat service. Methods – The research team developed a toolchain for data processing and analysis, which incorporated targeted searching for query terms using regular expressions and natural language processing using the Python spaCy library for automatic topic analysis. Processed data was exported to Tableau for visualization. Results were compared to hand-coded data to test the accuracy of conclusions. Results – The processed data provided insights about the volume of chats originating from each participating library, the proportion of chats answered by operator groups for each library, and the percentage of chats answered by different staff types. The data also captured the top referring URLs for the service, course codes and file extensions mentioned, and query hits. Natural language processing revealed that the most common topics were related to citation, subscription databases, and finding full-text articles, which aligns with common question types identified in hand-coded transcripts. Conclusion – Compared to hand coding, automatic and NLP processing approaches have benefits in terms of the volume of data that can be analyzed and the time frame required for analysis, but they come with a trade-off in accuracy, such as false hits. Therefore, computational approaches should be used to supplement traditional hand coding methods. As NLP becomes more accurate, approaches such as these may widen avenues of insight into virtual reference and patron needs.

Foreign Aid, Development, and US Strategic Interests in the Cold War

Abstract Scholars have argued that during the Cold War, the United States gave aid to its allies to reward them for maintaining an anti-Communist foreign policy rather than to promote their economic development. This finding is mostly based on data starting in the 1970s and does not accurately characterize US grand strategy before the 1970s, when the United States used aid to promote development among its allies in order to strengthen them against Communism. Using original data collected from historical editions of USAID's “Greenbook,” this article identifies the amount of unconditional aid in the United States’ foreign-aid programs in the period 1955–1970. This type of aid was designed to be politically attractive rather than to be developmentally effective. This article also develops an original measure of aid recipients’ geopolitical alignment that draws on hand coding of 466 diplomatic documents. Using these data, this article finds that there was more unconditional aid in the United States’ aid programs to neutral and nonaligned countries than in the United States’ aid programs to its allies and security partners—a counterintuitive finding that shows how different the first half of the Cold War was from the second.1