scholarly journals A Multivariate Approach to Facebook Data for Marketing Communication

2016 ◽  
Author(s):  
Caterina Liberati ◽  
Elisa Arrigo ◽  
Paolo Mariani
Keyword(s):  
Social Media ◽  
Prescription Drugs ◽  
Multivariate Statistical ◽  
Corporate Brand ◽  
Social Media Data ◽  
Multivariate Statistical Approach ◽  
Marketing Activities ◽  
Legal Constraints ◽  
Communication Decisions ◽  
Media Data

The aim of this paper is to propose a method to explore and synthesize social media data in order to aid businesses to make their communication decisions. The research was conducted at the end of 2014 on 5607 Italian Facebook subjects interested in drugs and health. In this study, we refer to the pharmaceutical market that is characterized by strict legal constraints, which prevent any promotional activities (such as advertising) of companies on prescription drugs. Thus, pharmaceutical businesses tend to promote their corporate brand instead of a single product brand. In such context, social media offer the opportunity to gather customers’ information about their attitudes and preferences, helpful to address marketing activities. Through a multivariate statistical approach on Facebook data, we have highlighted the associations existing between TV channels and users’ profiles. Therefore, depending on the value proposition to promote, every business could choose, first, the target group to reach and, then, the nearest suitable channel where to develop the corporate brand communication.

scholarly journals Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

10.2196/18350 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18350 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Social Media Text ◽  
Media Data

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

scholarly journals Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis (Preprint)

2020 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Abuse Case ◽  
Media Data

BACKGROUND Social media is considered a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes, behaviors, and medications. It can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate challenges and limitations surrounding the use such data. OBJECTIVE The objective of this research was to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. METHODS The proposed framework starts with defining different terms related to keywords, categories, and characteristics of the topic of interest. Next, we used Crimson Hexagon to collect data based on a search query that is informed by a drug abuse ontology developed using the identified terms. Then, we preprocessed the data and examined it’s quality using an evaluation matrix. Finally, suitable data analysis approach could be used to analyze the collected data. RESULTS The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media that relate to opioids. Results from the case analysis showed that the framework could improve the discovery and identification of topics on social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. CONCLUSIONS The proposed framework addressed challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. CLINICALTRIAL

A Status Property Classifier of Social Media User's Personality for Customer-Oriented Intelligent Marketing Systems

2021 ◽  
pp. 557-581
Author(s):  
Tsung-Yi Chen ◽  
Yuh-Min Chen ◽  
Meng-Che Tsai
Keyword(s):  
Social Media ◽  
Personality Type ◽  
Social Media Data ◽  
Customer Preferences ◽  
Semantic Mining ◽  
The Status ◽  
Marketing Systems ◽  
Marketing Activities ◽  
High Degree ◽  
Media Data

Enterprises need to obtain information about not only specific customer preferences, but also, more importantly, customers' psychological characteristics that significantly influence their consumption behaviors and response to intelligent-based marketing activities. If enterprises want to implement more precise intelligent selling activities for customers, customers' personality information will serve as a highly valued reference. The automatic detection method proposed in this study is based on techniques such as text semantic mining and machine learning to conduct personality type prediction on the target by collecting and analyzing the target's social media data. In the test, 5,858 statuses were obtained, 815 of which were labeled, with 122 effective tags. In general, when n = 5, the labeling rate can reach 60-80%. The status property classifier (SPC) proposed in this study can predict the personality type (PT) of the user publishing the status set with a high degree of accuracy by conducting text semantic mining on the status set.

A Status Property Classifier of Social Media User's Personality for Customer-Oriented Intelligent Marketing Systems

2020 ◽  
Vol 16 (1) ◽  
pp. 25-46
Author(s):  
Tsung-Yi Chen ◽  
Yuh-Min Chen ◽  
Meng-Che Tsai
Keyword(s):  
Social Media ◽  
Personality Type ◽  
Social Media Data ◽  
Customer Preferences ◽  
Semantic Mining ◽  
The Status ◽  
Marketing Systems ◽  
Marketing Activities ◽  
High Degree ◽  
Media Data

Enterprises need to obtain information about not only specific customer preferences, but also, more importantly, customers' psychological characteristics that significantly influence their consumption behaviors and response to intelligent-based marketing activities. If enterprises want to implement more precise intelligent selling activities for customers, customers' personality information will serve as a highly valued reference. The automatic detection method proposed in this study is based on techniques such as text semantic mining and machine learning to conduct personality type prediction on the target by collecting and analyzing the target's social media data. In the test, 5,858 statuses were obtained, 815 of which were labeled, with 122 effective tags. In general, when n = 5, the labeling rate can reach 60-80%. The status property classifier (SPC) proposed in this study can predict the personality type (PT) of the user publishing the status set with a high degree of accuracy by conducting text semantic mining on the status set.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

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