Nerdalicious scientainment: A network analysis of English libfixes

2019 ◽  
Vol 12 (3) ◽  
pp. 353-384 ◽  
Author(s):  
Muriel Norde ◽  
Sarah Sippach
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Phonological Similarity ◽  
High Type ◽  
Type Frequency ◽  
Semantic Properties

Libfixes are parts of words that share properties with both blends, compounds and affixes. They are deliberate formations, often with a jocular character, e.g. nerdalicious ‘delicious for nerds’, or scientainment ‘scientific entertainment’. These are not one-off formations – some libfixes have become very productive, as evidenced by high type frequency in a single corpus. Libfix constructions are particularly interesting for a network analysis for three reasons: they do not always have discrete morpheme boundaries, they feature a wide variety of bases (including phrases, as in give-me-a-break-o-meter), and they may be the source of back formations such as infotain. In this paper, we present a corpus-based analysis of eight English libfixes (cracy, fection, flation, gasm, licious, (o-)meter, tainment, and tastic), detailing their formal and semantic properties, as well as their differences and similarities. We argue that libfixes are most fruitfully analysed in a Bybeean network model, in which nodes are connected on the basis of phonological similarity, which allows for both fully compositional and non-compositional constructions to be linked without an exhaustive analysis into morphemes.

scholarly journals How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

2020 ◽  
Vol 3 ◽  
pp. 251581632097208
Author(s):  
Pengfei Zhang ◽  
Santosh Bhaskarabhatla
Keyword(s):  
United States ◽  
Social Media ◽  
Network Analysis ◽  
Network Model ◽  
Large Scale ◽  
The United States ◽  
Closeness Centrality ◽  
Control Group ◽  
Annual Conference ◽  
Sources Of Information

Background: Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy. Objective: We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine. Methods: We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups. Results: Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced. Conclusion: We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

scholarly journals Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

2020 ◽  
Vol 12 (20) ◽  
pp. 8667
Author(s):  
Xi Yang ◽  
Xiang Yu
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Network Analysis ◽  
Network Model ◽  
Industrial Development ◽  
Research Collaboration ◽  
New Technology ◽  
Technological Field ◽  
Electric Equipment ◽  
Multi Level

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

scholarly journals The use of network models in the inspection of objects of industrial installations.

2018 ◽  
Vol 210 ◽  
pp. 02052
Author(s):  
Jarosław Napiórkowski
Keyword(s):  
Risk Assessment ◽  
Network Analysis ◽  
Risk Analysis ◽  
Network Model ◽  
Network Models ◽  
Single Element ◽  
Simple Method ◽  
Safety Requirements ◽  
Centrality Analysis ◽  
The Relationship

Today’s industrial installations consist of many interconnected elements. The source of a threat or failure of an installation can be not only damage to a single element (device), but also the interaction between individual elements of such a system. In order for the installation to meet safety requirements, the risk of failure should be properly assessed and appropriate prevention systems should be selected and the effects of its occurrence reduced. In order to achieve this, many care for the safety of such structures, inspection organizations carry out risk analysis and risk assessment with various methods. Due to the large number of elements that comprise installations of this class, the problem is to find cleary, uncomplicated and easy to implemented (cheap and low time-consuming) way to identify the relationship between its various elements. Possibility of specify and visualize relationships between elements of installations, their vulnerabilities, safeguards and the effects of threats is an unquestionable advantage of the network models in case of risk assessment carried out on a model built in this way. They allow network analysis based on centrality analysis. Relatively simple method of analysis of these networks. This article discusses few from many areas of application the network model in risk analysis in technical structures.

PROMPT-TYPE FREQUENCY, AUDITORY PATTERN DISCRIMINATION, AND EFL LEARNERS’ PRODUCTION OFWH-QUESTIONS

2012 ◽  
Vol 34 (3) ◽  
pp. 355-377 ◽  
Author(s):  
Kim McDonough ◽  
Jindarat De Vleeschauwer
Keyword(s):  
Positive Impact ◽  
Pattern Discrimination ◽  
Syntactic Priming ◽  
Auditory Pattern ◽  
High Type ◽  
Auxiliary Verbs ◽  
L2 Development ◽  
Type Frequency ◽  
The Relationship

Recently researchers have suggested that syntactic priming may facilitate the production ofwh-questions with obligatory auxiliary verbs, particularly when learners are prompted to produce those questions with a wide variety of lexical items (McDonough & Kim, 2009; McDonough & Mackey, 2008). However, learners’ ability to benefit from syntactic priming materials with prompt-type frequency may be mediated by their ability to recognize patterns in aural input. The purpose of this replication study is to confirm the positive impact of prompt-type frequency on learners’ production ofwh-questions reported by McDonough and Kim (2009), and to investigate whether its impact is mediated by learners’ auditory pattern-discrimination abilities. Thai learners (n= 43) of English as a foreign language (EFL) carried out three oral tests, two sets of syntactic priming activities, and an auditory pattern-discrimination test over a 4-week period. Half of the learners carried out the syntactic priming activities with low-type-frequency prompts, whereas the other learners received high-type-frequency prompts. The results revealed a significant interaction between Type Frequency × Auditory Pattern Discrimination on the immediate and delayed posttests. The findings are discussed in terms of the potential role of individual cognitive factors in mediating the relationship between syntactic priming and second language (L2) development.

Research on Organization Network Model for Construction Project Based on Social Network Analysis

2012 ◽  
Vol 174-177 ◽  
pp. 2001-2005 ◽  
Author(s):  
Yang Lin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Model ◽  
Construction Project ◽  
Social Characteristics ◽  
Visual Methods ◽  
Engineering Project ◽  
Project Organization ◽  
Organization And Management

Current project organization is mostly non-professional and low maturity, which lack of mature and visual methods and tools. Engineering project has obvious social characteristics, which includes not only material operating activities also cooperation and collaboration of complex staff a large number of social activities, etc. This paper point out the organization of control of project management into the field of social network analysis(SNA), A qualitative social network model for construction project organization is established, and then this model is quantitatively analyzed with the help of UCINET, which can fully demonstrate the organization and management in the areas of project feasibility.

scholarly journals 1370Introducing network analysis to measure early life adversity

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Tjeerd Rudmer De Vries ◽  
Iris Arends ◽  
Naja Hulvej Rod ◽  
Albertine J. Oldehinkel ◽  
Ute Bültmann
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Early Life ◽  
Childhood And Adolescence ◽  
Early Life Adversity ◽  
Promising Alternative ◽  
Undirected Network ◽  
Financial Difficulties ◽  
Insight Into ◽  
Over Time

Abstract Focus of Presentation Many studies have investigated associations between early life adversity (ELA) and outcomes across the life course. A defining characteristic of ELA is its complex nature, as many individual adverse experiences (e.g., parental mental health problems, financial difficulties) co-occur and interact over time. Commonly used methods for measuring ELA have not been able to elucidate pathways through which individual AEs are associated with each other during early life. We propose using network analysis to overcome this research gap. Findings Figure 1 shows the conditional associations between AEs in childhood and adolescence in an undirected network model, based on empirical data from the longitudinal TRAILS cohort. First, we found that the network model allows us to explain co-occurrences between AEs. For example: the co-occurrence of parental illness and financial difficulties in childhood is likely due to parental unemployment. Second, we identified which AEs are associated over time, e.g., familial conflicts in childhood and adolescence are strongly associated, the latter being associated with parental divorce in adolescence. Conclusions/Implications These findings add to the literature by providing insight into how individual AEs are conditionally associated, in distinct developmental periods and over time. The findings can be used in future research on pathways between AEs and guide the development of interventions. Key messages Undirected network models are a promising alternative approach to measuring ELA that can provide insight into pathways through which AEs co-occur and interact over time.

scholarly journals The recursive formation of Old English non-verbal categories. Productivity and constraints

2015 ◽  
Vol 13 ◽  
pp. 157
Author(s):  
Raquel Vea Escarza
Keyword(s):  
Old English ◽  
Semantic Interpretation ◽  
Formal Representation ◽  
High Type ◽  
Semantic Effect ◽  
Type Frequency ◽  
The One

This paper aims at analysing the recursivity in the formation of non-verbal categories, more specifically, of nouns and adjectives in old English. Pounder’s (2000) model, known as Process and Paradigm Model, provides the formal representation of recursive operations. The data of analysis consist of a total of 388 recursive nouns and adjectives, 11 of which undergo a two-level recursivity, or slot-II recursivity. Both in the case of nouns and adjectives, suffixation has a clearly preeminent role over prefixation. As for nouns, the suffix -nes is the most frequent one in number of tokens, whereas -∂ is the one that combines with a greater number of suffixes in prefinal position. Regarding adjectives, -lic is by far the suffix present in a higher number of predicates, and also the one that undergoes a wider variety of different recursive patterns, what evinces that there is correlation between a high type frequency and the assignment of a high number of different recursive patterns. Positional constraints affect -nes and -lic, since none of them can occur in a position other than final. A semantic interpretation of recursive suffixation leads to assign a semantic effect of this phenomenon when it applies to nouns, and a pragmatic one in the case of adjectives.

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