Interactive learning through social media for large size classes in the clothing and textile curriculum

Analysis of stomach contents for Dasyatis chrysonota revealed that diet varied with size and habitat. The diet of all size classes in the surf zone was comprised primarily of Callianassa spp., Donax spp. and unidentified polychaete species. The medium and large size classes fed primarily on Donax spp., whereas the very large size class fed mainly on Callianassa spp. Polychaetes were of secondary importance as prey for the medium size class. The diet of D. chrysonota in the nearshore zone consisted mainly of Balanoglossus capensis and Callianassa spp. Balanoglossus capensis decreased from an index of relative importance (IRI) of 75.3% for the medium size class to 59.9% for the very large size class, whereas Callianassa spp. increased from 22.8% to 39.4% between the medium and the very large size classes. The offshore zone was the only area in which small size class D. chrysonota were caught. The diet of these small D. chrysonota was primarily polychaetes and amphipods. Polychaetes increased in importance in the medium size class, but declined in each successively larger size class. Conversely, Pterygosquilla armata capensis became the single most important prey item for the very large size class, comprising an IRI of 50.9%. The behaviour pattern used by D. chrysonota to locate and extract prey is described.

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Can altmetric mentions predict later citations? A test of validity on data from ResearchGate and three social media platforms

Online Information Review ◽

10.1108/oir-11-2019-0364 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sumit Kumar Banshal ◽

Vivek Kumar Singh ◽

Pranab Kumar Muhuri

Keyword(s):

Social Media ◽

Peer Review ◽

Citation Window ◽

Content Type ◽

Large Size ◽

Social Media Platforms ◽

Academic Social Network ◽

Citation Counts ◽

Citation Advantage ◽

Analyze Data

PurposeThe main purpose of this study is to explore and validate the question “whether altmetric mentions can predict citations to scholarly articles”. The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations.Design/methodology/approachA large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups.FindingsResults show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations.Research limitations/implicationsThe results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms.Originality/valueThe paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364

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Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.2375 ◽

2021 ◽

Vol 12 (6) ◽

pp. 962-970

Author(s):

Prof. Manisha Sachin Dabade, Et. al.

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Daily Monitoring ◽

Fine Grain ◽

Large Size ◽

Twitter Data ◽

The Social ◽

Enormous Amount

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.

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SOCIAL MEDIA INTERDISCIPLINARITY IN MULTIMODALITY OF ARTS AND CULTURE EDUCATION FOR MILLENIALS

JURNAL PAJAR (Pendidikan dan Pengajaran) ◽

10.33578/pjr.v5i1.8257 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Yasinda Widya Fahmi ◽

Djuli Djatiprambudi ◽

Warih Handayaningrum

Keyword(s):

High School ◽

Social Media ◽

Junior High School ◽

Learning Process ◽

Junior High ◽

Interactive Learning ◽

Culture Learning ◽

Problem Solving Skills ◽

Arts And Culture ◽

Art And Culture

This study aims to explore the problems of art and culture interactive learning at the Junior High School level which belongs to the millennial generation. The focus of the study lies in the interdisciplinary aspect of social media in its delivery in multimodality of arts and culture learning process. Furthermore, to find out about opportunities, challenges, and responses from students about the use of social media in its development as a medium in art and culture interactive learning. The research method uses qualitative-analytic. Data collection used observation techniques which were carried out from January 2020 to June 2020, and questionnaires to 75 art teachers and 500 Junior High School students who were taken randomly, with spatial boundaries in Surabaya, East Java. The results showed that the learning involvement experienced by students had complexity and multimodality, including collaborative work, observing and evaluating each other's work, and involvement in finding, identifying, and exploring trends related to delivery in social media as a medium for art and culture learning process. Furthermore, it's able to motivate students to be more actively involved in learning with a sense of joy; positioning artwork with others on social media; increase the contextual and conceptual understanding in the material of art and culture and apply it as a process of actualizing students' aesthetic skills; and improve critical thinking and problem-solving skills.

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The Effect of the Decorator Crab Notomithrax Minor on Cultivated Greenshell Mussel Spat (Perna Canaliculus) Survival, Growth, and Byssal Characteristics

10.26686/wgtn.16992331 ◽

2021 ◽

Author(s):

◽

Irene Van de Ven

Keyword(s):

Retention Rate ◽

Experimental Testing ◽

Preliminary Evidence ◽

Size Classes ◽

Perna Canaliculus ◽

Large Size ◽

Field Investigations ◽

Attachment Strength ◽

Decorator Crab ◽

Financial Losses

<p>The decorator crab Notomithrax minor is common on Greenshell mussel (Perna canaliculus) farms in the Marlborough Sounds, New Zealand. Individuals in the Greenshell mussel industry have suggested that the presence of N. minor, found on mussel lines, is related to substantial losses of Greenshell mussel spat. Laboratory and field investigations were used to assess the effect of N. minor presence on the retention and productivity of Greenshell musselTM spat. Specific consideration was given to predation pressure and induced anti-predator defenses, both of which can cause financial losses to mussel farmers. High (12 crabs/cage-1) and low (3 crabs/cage-1) densities of large (males: >20mm, females: >15mm TCW) and medium (males: 15-20mm, females: 10-15mm TCW) decorator crabs were placed in cages on commercial Greenshell mussel farm droppers at two sites in the Pelorus Sound. The byssal characteristics, spat retention rate and spat shell length were assessed at 8 and 11 weeks after trial initiation. Greenshell mussel density on the experimental droppers decreased significantly when medium and high densities of the decorator crabs (N. minor) were introduced. N. minor presence induced the remaining Greenshell mussel spat to produce more and thicker byssus threads which consequently lead to increased mussel attachment. The decrease in retention rate and the increase in mussel attachment strength were more pronounced in small recently seeded spat. Laboratory experiments to assess the consumption rate of small (≤5mm) Greenshell mussel spat by decorator crabs showed that mussel consumption by N. minor peaked at 56.43 (± 13.02 (95% C.I.)) crab-1 hr-1, however the rate of consumption decreased significantly over the duration of the three day trial. N. minor prey size preference was also assessed using Laboratory trials; crabs were offered 4 size classes of mussels (small (<5 mm), small-medium (5-10 mm), medium-large (10-15 mm), large (>15 mm) simultaneously. Female crabs consumed more mussels in the <5 mm and 5-10 mm size classes than in the two larger mussel size classes (10-15 mm and >15 mm), whereas the male crabs showed a numerical preference for mussel spat in the small-medium and medium-large size classes. This study provides preliminary evidence that the decorator crab N. minor is a previously overlooked and under-estimated threat to the Greenshell Mussel industry in the Marlborough Sounds that deserves closer scrutiny and experimental testing.</p>

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InterSpot: Interactive Spammer Detection in Social Media

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/939 ◽

2019 ◽

Author(s):

Kaize Ding ◽

Jundong Li ◽

Shivam Dhar ◽

Shreyash Devan ◽

Huan Liu

Keyword(s):

Social Media ◽

Learning Process ◽

Interactive Learning ◽

Detection System ◽

Feedback Information ◽

Detection Model ◽

Empirical Success ◽

Detection Strategy ◽

Current Detection ◽

Over Time

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.

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