New Evaluation Index for Application of Machine Learning Algorithms to Determine Trust in Skewed Social Media Data (Record no. 14175)
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| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20210204141034.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210203b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 13161 |
| Author | Fazili, Shifaa Basharat |
| 245 ## - TITLE STATEMENT | |
| Title | New Evaluation Index for Application of Machine Learning Algorithms to Determine Trust in Skewed Social Media Data |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol 5 (3), Sep - Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | STM Journals |
| Year | 2018 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 49 -57p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | paper we study the problem of accuracy paradox which arises on the application of machine learning algorithms for inference of trust in skewed social media data. Skewness is defined as the under representation of one class over another in a binary classification problem. We achieved our purpose of identifying the accuracy paradox problem in various algorithms by identifying a new evaluation index called predictive index. The dataset used was that of Twitter one of the most commonly used collaborative system which has experienced enormous growth in a small amount of time. It has evolved from a microblogging service to a major news source used by people as a platform to share and disseminate information about current events. However, not all information posted on Twitter is trustworthy or useful in providing information about the event. Gossips, fake news etc. are also a part of genuine news. The main aim of this paper is to tackle the issue of accuracy paradox, a major problem when dealing with social media research, were the data extracted by us was highly skewed. This high skewness in the dataset gives us biased information about the performance of our machine learning algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 13162 |
| Co-Author | Ahmad, Manzoor |
| 773 0# - HOST ITEM ENTRY | |
| Title | Journal of artificial intelligence research and advances (JoAIRA) |
| Place, publisher, and date of publication | Noida STM Journals |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | http://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=1566 |
| Link text | Click Here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 03/02/2021 | 2021-2021403 | 03/02/2021 | 03/02/2021 | Articles Abstract Database |