scholarly journals Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

2019 ◽  
Vol 8 (2) ◽  
pp. 113-135 ◽  
Author(s):  
Dimitra Pappa ◽  
Lampros K. Stergioulas
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Future Directions ◽  
Social Media Data ◽  
Current State ◽  
Media Data

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals Using Social Media Data as Research Data

2013 ◽  
Vol 1 (3) ◽  
pp. 49-55
Author(s):  
Suman Silwal ◽  
Dale W Callahan
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Communication Channel ◽  
Social Networking Site ◽  
Science And Engineering ◽  
Social Media Data ◽  
Breaking News ◽  
Current State ◽  
Media Data

Social Media (SM) is becoming a normal part of everyday life. The information generated from Social Media (SM) data is becoming increasingly utilized as a communication channel for market trend, brand awareness, breaking news, and online social interaction between person to person. SM is also rapidly growing and maturing [1]. Further, SM is becoming a reliable tool for interdisciplinary industries like banks, travel, healthcare, biotech, software, sports etc.SM data can also be used as a research tool to apply in different areas of Humanities, Art, Science and Engineering. There are unlimited possibilities using Social Networking Site (SNS) to collect, process and evaluate data. This paper reviews the current state of Social Networking Sites and Text-based Language Processes, and how it can be used to generate valuable information.

scholarly journals Analysis and Visualization Considerations for Quantitative Social Science Research Using Social Media Data

2021 ◽  
Author(s):  
J. Bradford Jensen ◽  
Lisa Singh ◽  
Pamela Davis-Kean ◽  
Katharine Abraham ◽  
Paul Beatty ◽  
...  
Keyword(s):  
Social Media ◽  
Visual Analytics ◽  
Social Science Research ◽  
Science Research ◽  
Data Set ◽  
Future Directions ◽  
Social Media Data ◽  
Analysis Techniques ◽  
Media Data ◽  
Different Levels

This is the fifth in a series of white papers providing a summary of the discussions and future directions that are derived from these topical meetings. This paper focuses on issues related to analysis and visual analytics. While these two topics are distinct, there are clear overlaps between the two. It is common to use different visualizations during analysis and given the sheer volume of social media data, visual analytic tools can be important during analysis, as well as during other parts of the research lifecycle. Choices about analysis may be informed by visualization plans and vice versa - both are key in communicating about a data set and what it means. We also recognized that each field of research has different analysis techniques and different levels of familiarity with visual analytics. Putting these two topics into the same meeting provided us with the opportunity to think about analysis and visual analytics/visualization in new, synergistic ways.

scholarly journals Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

2020 ◽  
Vol 34 (10) ◽  
pp. 13732-13733
Author(s):  
Annika Marie Schoene
Keyword(s):  
Social Media ◽  
Recurrent Neural Networks ◽  
Emotion Detection ◽  
Problem Statement ◽  
Future Directions ◽  
Social Media Data ◽  
Fine Grained ◽  
Hybrid Approaches ◽  
Research Plan ◽  
Media Data

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.

scholarly journals MapReduce and Semantics Enabled Event Detection using Social Media

2017 ◽  
Vol 7 (3) ◽  
pp. 201-213 ◽  
Author(s):  
Peng Yan
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Domain Knowledge ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Temporal Analysis ◽  
Semantic Interpretation ◽  
Social Media Data ◽  
Twitter Data ◽  
Media Data

Abstract Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.

scholarly journals Modeling Considerations for Quantitative Social Science Research Using Social Media Data

2021 ◽  
Author(s):  
Ceren Budak ◽  
Stuart Soroka ◽  
Lisa Singh ◽  
Michael Bailey ◽  
Leticia Bode ◽  
...  
Keyword(s):  
Social Media ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Model Fit ◽  
Model Construction ◽  
Interesting Aspect ◽  
Future Directions ◽  
Social Media Data ◽  
Media Data

In this paper, the fourth in a series of white papers, we provide a summary of the discussions and future directions that came from the topical meeting that focused on model construction with social media data. A particularly interesting aspect of this meeting was, in our view, discussion of the different disciplines’ requirements and approaches to modeling and the different considerations that are used to assess model fit.

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