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Big Data and Cognitive Computing, Volume 6, Issue 1

March 2022 - 32 articles

Cover Story: Although social media platforms are a valuable source of information, their content is often affected by spam and disinformation campaigns carried out by automatic entities defined as social bots. In this paper, we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns. This methodology is temporally aware and filters out data produced by social bots, thus avoiding heavily biased information. We assessed the effectiveness of TIMBRE by analyzing the online conversation on Twitter during the 2016 US presidential election. The results show how the removal of bots and the use of temporal information allow for the accurate estimation of the voting intentions of legitimate users. View this paper
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Articles (32)

  • Article
  • Open Access
7 Citations
4,385 Views
23 Pages

Service Oriented R-ANN Knowledge Model for Social Internet of Things

  • Mohana S. D.,
  • S. P. Shiva Prakash and
  • Kirill Krinkin

Increase in technologies around the world requires adding intelligence to the objects, and making it a smart object in an environment leads to the Social Internet of Things (SIoT). These social objects are uniquely identifiable, transferable and shar...

  • Article
  • Open Access
33 Citations
7,003 Views
22 Pages

Factors Influencing Citizens’ Intention to Use Open Government Data—A Case Study of Pakistan

  • Muhammad Mahboob Khurshid,
  • Nor Hidayati Zakaria,
  • Muhammad Irfanullah Arfeen,
  • Ammar Rashid,
  • Safi Ullah Nasir and
  • Hafiz Muhammad Faisal Shehzad

Open government data (OGD) has gained much attention worldwide; however, there is still an increasing demand for exploring research from the perspective of its adoption and diffusion. Policymakers expect that OGD will be used on a large scale by the...

  • Article
  • Open Access
14 Citations
8,034 Views
17 Pages

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

  • Muhammad Salman,
  • Hafiz Suliman Munawar,
  • Khalid Latif,
  • Muhammad Waseem Akram,
  • Sara Imran Khan and
  • Fahim Ullah

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary...

  • Article
  • Open Access
58 Citations
19,463 Views
29 Pages

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

  • Maha Gharaibeh,
  • Dalia Alzu’bi,
  • Malak Abdullah,
  • Ismail Hmeidi,
  • Mohammad Rustom Al Nasar,
  • Laith Abualigah and
  • Amir H. Gandomi

Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in t...

  • Article
  • Open Access
3 Citations
4,034 Views
19 Pages

Virtual reality technologies, including head-mounted displays (HMD), can provide benefits to psychological research by combining high degrees of experimental control with improved ecological validity. This is due to the strong feeling of being in the...

  • Article
  • Open Access
10 Citations
6,050 Views
42 Pages

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0

  • Anna Kirkpatrick,
  • Chidozie Onyeze,
  • David Kartchner,
  • Stephen Allegri,
  • Davi Nakajima An,
  • Kevin McCoy,
  • Evie Davalbhakta and
  • Cassie S. Mitchell

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness...

  • Review
  • Open Access
12 Citations
9,750 Views
24 Pages

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

  • Mohammad Fikry Abdullah,
  • Zurina Zainol,
  • Siaw Yin Thian,
  • Noor Hisham Ab Ghani,
  • Azman Mat Jusoh,
  • Mohd Zaki Mat Amin and
  • Nur Aiza Mohamad

The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment i...

  • Article
  • Open Access
5 Citations
5,946 Views
28 Pages

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

  • Tek Raj Chhetri,
  • Chinmaya Kumar Dehury,
  • Artjom Lind,
  • Satish Narayana Srirama and
  • Anna Fensel

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-m...

  • Feature Paper
  • Article
  • Open Access
21 Citations
4,786 Views
22 Pages

Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

  • Jogeswar Tripathy,
  • Rasmita Dash,
  • Binod Kumar Pattanayak,
  • Sambit Kumar Mishra,
  • Tapas Kumar Mishra and
  • Deepak Puthal

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of whi...

  • Article
  • Open Access
8 Citations
6,015 Views
27 Pages

A Framework for Content-Based Search in Large Music Collections

  • Tiange Zhu,
  • Raphaël Fournier-S’niehotta,
  • Philippe Rigaux and
  • Nicolas Travers

We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music docume...

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Big Data Cogn. Comput. - ISSN 2504-2289