The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

Download Full-text

Social Media to Social Media Analytics

International Journal of Technoethics ◽

10.4018/ijt.2019070104 ◽

2019 ◽

Vol 10 (2) ◽

pp. 57-70 ◽

Cited By ~ 4

Author(s):

Vikas Kumar ◽

Pooja Nanda

Keyword(s):

Social Media ◽

Social Media Analytics ◽

Social Media Data ◽

Ethical Concerns ◽

Ethical Implications ◽

The Social ◽

Social Media Platforms ◽

The Individual ◽

The Impact ◽

Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

Download Full-text

The impact of social media on human interaction in an organisation based on real-time social media data

International Journal of Data Science ◽

10.1504/ijds.2019.102793 ◽

2019 ◽

Vol 4 (3) ◽

pp. 260

Author(s):

Sharifah Sakinah Syed Ahmad ◽

Anis Naseerah Binti Shaik Osman ◽

Halizah Basiron

Keyword(s):

Social Media ◽

Real Time ◽

Human Interaction ◽

Social Media Data ◽

The Impact ◽

Media Data

Download Full-text

Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010015 ◽

2018 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 4

Author(s):

Ruoxin Zhu ◽

Diao Lin ◽

Michael Jendryke ◽

Chenyu Zuo ◽

Linfang Ding ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Research Work ◽

City Management ◽

Social Media Data ◽

Social Events ◽

Time Space ◽

The Impact ◽

Media Data

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

Download Full-text

SOCIAL MEDIA DATA PROCESSING AND ANALYSIS BY MEANS OF MACHINE LEARNING FOR RAPID DETECTION, ASSESSMENT AND MAPPING THE IMPACT OF DISASTERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1237-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1237-1241

Author(s):

P. M. Kikin ◽

A. A. Kolesnikov ◽

E. A. Panidi

Keyword(s):

Machine Learning ◽

Social Media ◽

Relevant Information ◽

Important Task ◽

Machine Learning Techniques ◽

Social Media Data ◽

Accurate Localization ◽

The Impact ◽

Official Sources ◽

Media Data

Abstract. The main factor determining the possibility of using data obtained from social media as a source of information about the threat of emergencies is their relevance and accuracy. Thus, the important task is the determination of metrics for evaluating these parameters for a specific publication in a social media. It is worth noting the importance of this information channel as a source of eyewitness accounts from the scene. A comparison of social media data and official sources shows that social media contain a significant amount of unique information at different stages of emergency development. Also, when monitoring the situation for a specific event, social media allows to get more relevant information in comparison to official sources. Another important task is to search for emergency messages and their most accurate localization in space. A promising solution for the analysis and processing of social media data during emergency response is the application of artificial intelligence methods, and, particularly, machine learning techniques.

Download Full-text

Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

10.2196/preprints.22734 ◽

2020 ◽

Author(s):

Oladapo Oyebode ◽

Chinenye Ndulue ◽

Ashfaq Adib ◽

Dinesh Mulchandani ◽

Banuchitra Suruliraj ◽

...

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Language Processing ◽

Social Issues ◽

Public Perceptions ◽

Psychosocial Issues ◽

Social Media Data ◽

Health Related ◽

The Impact ◽

Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

Download Full-text

Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5369 ◽

2020 ◽

Vol 34 (01) ◽

pp. 346-353 ◽

Cited By ~ 3

Author(s):

Mansi Agarwal ◽

Maitree Leekha ◽

Ramit Sawhney ◽

Rajiv Ratn Shah

Keyword(s):

Social Media ◽

Damage Assessment ◽

Damage Identification ◽

Detection System ◽

Learning Performance ◽

Social Media Data ◽

Online Social Media ◽

Assessment Performance ◽

The Impact ◽

Media Data

In times of a disaster, the information available on social media can be useful for several humanitarian tasks as disseminating messages on social media is quick and easily accessible. Disaster damage assessment is inherently multi-modal, yet most existing work on damage identification has focused solely on building generic classification models that rely exclusively on text or image analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data. Conventionally, when information from various modalities is presented together, it often exhibits complementary insights about the application domain and facilitates better learning performance. In this work, we present Crisis-DIAS, a multi-modal sequential damage identification, and severity detection system. We aim to support disaster management and aid in planning by analyzing and exploiting the impact of linguistic cues on a unimodal visual system. Through extensive qualitative, quantitative and theoretical analysis on a real-world multi-modal social media dataset, we show that the Crisis-DIAS framework is superior to the state-of-the-art damage assessment models in terms of bias, responsiveness, computational efficiency, and assessment performance.

Download Full-text

Determining social media impact on the politics of developing countries using social network analytics

Program electronic library and information systems ◽

10.1108/prog-02-2016-0011 ◽

2016 ◽

Vol 50 (4) ◽

pp. 481-507 ◽

Cited By ~ 15

Author(s):

Collins Udanor ◽

Stephen Aneke ◽

Blessing Ogechi Ogbuokiri

Keyword(s):

Social Media ◽

Developing Countries ◽

Developing Economies ◽

General Election ◽

Typical Application ◽

Social Media Data ◽

Content Type ◽

The Social ◽

The Impact ◽

Media Data

Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

Download Full-text

Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030135 ◽

2021 ◽

Vol 10 (3) ◽

pp. 135

Author(s):

Wei Jiang ◽

Zhengan Xiong ◽

Qin Su ◽

Yi Long ◽

Xiaoqing Song ◽

...

Keyword(s):

Social Media ◽

Analytical Framework ◽

Historical Background ◽

Spatiotemporal Pattern ◽

Government Control ◽

Social Media Data ◽

Seasonal Characteristics ◽

The Impact ◽

Media Data ◽

Tourist Flow

Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction’s historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment.

Download Full-text