A Comparison Between Inbound and Outbound Marketing

2021 ◽  
pp. 2129-2139
Author(s):  
Mohammad Izzuddin Mohammed Jamil ◽  
Mohammad Nabil Almunawar
Keyword(s):  
Social Media ◽  
Relevant Information ◽  
Communications Media ◽  
Low Costs ◽  
Inbound Marketing

Social media has not only enabled us to freely express our ideas and thoughts and facilitates us with interactive communications media with friends, but also allows businesses to use it as a platform for marketing. Since social media facilitates businesses to keep in touch with their consumers at low costs while catering to their specific needs and wants in order to satisfy them. As a result, there is a trend towards inbound marketing, in which customers are persuaded to actively find useful and relevant information on products or services to suit their needs and wants. In contrast, conventional marketing is outbound marketing where businesses push marketing contents to their customers regardless of if they need such contents or not, which may annoy them. This chapter compares inbound and outbound marketing and discusses advantages of inbound marketing over outbound marketing.

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

scholarly journals Considerations in Designing Digital Peer Support for Mental Health: Interview Study Among Users of a Digital Support System (Buddy Project) (Preprint)

2020 ◽  
Author(s):  
Nazanin Andalibi ◽  
Madison K Flood
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Peer Support ◽  
Support System ◽  
Relevant Information ◽  
Coping Mechanisms ◽  
Interview Study ◽  
Comparative Approach ◽  
Mental Health Diagnosis ◽  
Health Diagnosis

BACKGROUND Peer support is an approach to cope with mental illness, and technology provides a way to facilitate peer support. However, there are barriers to seeking support in offline and technology-mediated contexts. OBJECTIVE This study aims to uncover potential ways to design digital mental health peer support systems and to outline a set of principles for future designers to consider as they embark on designing these systems. By learning how existing systems are used by people in daily life and by centering their experiences, we can better understand how to design mental health peer support technologies that foreground people’s needs. One existing digital peer support system is Buddy Project, the case study in this paper. METHODS This paper reports on an interview study with Buddy Project users (N=13). Data were analyzed using the constant comparative approach. RESULTS Individuals matched through Buddy Project developed supportive friendships with one another, leading them to become each other’s peer supporters in their respective journeys. It was not only the mental health peer support that was important to participants but also being able to connect over other parts of their lives and identities. The design of Buddy Project provided a sense of anonymity and separation from pre-existing ties, making it easier for participants to disclose struggles; moreover, the pairs appreciated being able to browse each other’s social media pages before connecting. Buddy Project has an explicit mission to prevent suicide and demonstrates this mission across its online platforms, which helps reduce the stigma around mental health within the peer support space. Pairs were matched based on shared interests and identities. This choice aided the pairs in developing meaningful, compatible, and supportive relationships with each other, where they felt seen and understood. However, the pairs were concerned that matching based on a shared mental health diagnosis may lead to sharing unhealthy coping mechanisms or comparing themselves and the severity of their experiences with their peers. CONCLUSIONS The results of this study shed light on desirable features of a digital mental health peer support system: matching peers based on interests and identities that they self-identify with; having an explicit mental health–related mission coupled with social media and other web-based presences to signal that discussing mental health is safe within the peer support ecosystem; and not matching peers based on a broad mental health diagnosis. However, if the diagnosis is important, this matching should account for illness severity and educate peers on how to provide support while avoiding suggesting unhelpful coping mechanisms; allowing for some degree of anonymity and control over how peers present themselves to each other; and providing relevant information and tools to potential peers to help them decide if they would like to embark on a relationship with their matched peer before connecting with them. CLINICALTRIAL

Supporting Policy-Makers with Social Media Analysis Tools to Get Aware of Citizens' Opinions

2014 ◽  
pp. 121-156
Author(s):  
Timo Wandhöfer ◽  
Steve Taylor ◽  
Miriam Fernandez ◽  
Beccy Allen ◽  
Harith Alani ◽  
...  
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Behavior ◽  
Relevant Information ◽  
Media Analysis ◽  
Control Group ◽  
Data Sets ◽  
Online Forums ◽  
Policy Makers ◽  
Facebook Pages

The role of social media in politics has increased considerably. A particular challenge is how to deal with the deluge of information generated on social media: it is impractical to read lots of messages with the hope of finding useful information. In this chapter, the authors suggest an alternative approach: utilizing analysis software to extract the most relevant information of the discussions taking place. This chapter discusses the WeGov Toolbox as one concept for policy-makers to deal with the information overload on Social Media, and how it may be applied. Two complementary, in depth case studies were carried out to validate the usefulness of the analysis results of the WeGov Toolbox components' within its target audience's everyday life. Firstly, the authors used the “HeadsUp” forum, operated by the Hansard Society. Here, they were able to compare the key themes and opinions extracted automatically by the Toolbox to a control group of manually pre-analyzed data sets. In parallel, results of analyses based on four weeks' intensive monitoring on policy area-specific Facebook pages selected by German policy makers, as well as topics on Twitter globally and local, were assessed by taking into account their existing experience with content discussed and user behavior in their respective public spheres. The cases show that there are interesting applications for policy-makers to use the Toolbox in combination with online forums (blogs) and social networks, if behavioral user patterns will be considered and the framework will be refined.

Reconciling Social Media with Luxury Fashion Brands

2015 ◽  
pp. 917-945 ◽  
Author(s):  
Wilson Ozuem ◽  
Kerri Tan
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Cultural Transformation ◽  
Marketing Communications ◽  
Communication Media ◽  
Communications Media ◽  
Customer Expectations ◽  
Fashion Brand ◽  
Social Media Platforms ◽  
Human Association

Modern developments in communication media are creating new networks of information diffusion which are profoundly altering the way in which people can construct shared ‘realities'. Internet along with its prototypical subsets, notably social media, is enabling the emergence of new mechanism of human association which are shaped by – yet also shape – the development of this new medium of communication. This chapter integrates social media theory and luxury fashion brand theory arguments to examine the knowledge benefits that this cultural transformation provides to the development of a marketing communications programme. The authors argue that the key to providing an effective marketing communication programme is understanding and responding to customer expectations through the integration of social media platforms and traditional marketing communications media.

Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis

2018 ◽  
Vol 17 (03) ◽  
pp. 883-910 ◽  
Author(s):  
P. D. Mahendhiran ◽  
S. Kannimuthu
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Relevant Information ◽  
Neural Net ◽  
Video Information ◽  
Automatic Feature Extraction ◽  
Multimodal Sentiment Analysis

Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports to achieve the best performance to enhance the combined model that integrates Linguistic, Acoustic and Video information extraction method. This paper focuses on the various techniques used for classifying the given portion of natural language text, audio and video according to the thoughts, feelings or opinions expressed in it, i.e., whether the general attitude is Neutral, Positive or Negative. From the results, it is perceived that Deep Learning classification algorithm gives better results compared to other machine learning classifiers such as KNN, Naive Bayes, Random Forest, Random Tree and Neural Net model. The proposed MSA in deep learning is to identify sentiment in web videos which conduct the poof-of-concept experiments that proved, in preliminary experiments using the ICT-YouTube dataset, our proposed multimodal system achieves an accuracy of 96.07%.

Analyzing the startup ecosystem of India: a Twitter analytics perspective

2019 ◽  
Vol 17 (2) ◽  
pp. 262-281 ◽  
Author(s):  
Shiwangi Singh ◽  
Akshay Chauhan ◽  
Sanjay Dhir
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Relevant Information ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Content Type ◽  
Twitter Analytics ◽  
Bayes Algorithm

Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

The impact of information and communication technology on real estate agency in Lagos Metropolis, Nigeria

2018 ◽  
Vol 36 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Theophilus Olugbenga Babatunde ◽  
Cyril Ayodele Ajayi
Keyword(s):  
Social Media ◽  
Real Estate ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Relevant Information ◽  
Research Approach ◽  
Content Type ◽  
Information And Communication ◽  
Estate Agency ◽  
The Impact

Purpose The purpose of this paper is to evaluate the effect of information and communication technology (ICT) on real estate agency transactions with a view to determine its influence on the performance of estate agents. Design/methodology/approach A research approach in which questionnaire was administered to elicit relevant information from 220 practicing Estate Surveyors and Valuers surveyed in the course of the study. Data collected were analysed using mean ranking, relative influence index and analysis of variance. Findings The results showed that the use of ICT impacted positively on real estate agency transactions by promoting company’s brand thereby increasing the level of patronage. Consequently, the increased level of patronage signifies an increase in the level of income of the agents. Research limitations/implications The study was limited to social media applications otherwise referred to as ICT, which are used in real estate agency transactions. Further study on other ICT media and their effects on more areas of real estate practice in the developing economy may be required. Originality/value This paper is one of the few works on the impact of ICT on real estate agency transactions with particular reference to the social media networking especially in an emerging economy. Most of the previous studies conducted on ICT and real estate focussed only on internet use with respect to real estate agents and practices.

scholarly journals Dermatopathology and Social Media

2017 ◽  
Vol 142 (2) ◽  
pp. 184-190 ◽  
Author(s):  
Erin Carlquist ◽  
Nathan E. Lee ◽  
Sara C. Shalin ◽  
Michael Goodman ◽  
Jerad M. Gardner
Keyword(s):  
Social Media ◽  
Medical Profession ◽  
Relevant Information ◽  
Medical Professionals ◽  
Patient Confidentiality ◽  
Collaborative Tool ◽  
The World ◽  
The Future ◽  
Use Of Social Media

Context.— Use of social media in the medical profession is an increasingly prevalent and sometimes controversial practice. Many doctors believe social media is the future and embrace it as an educational and collaborative tool. Others maintain reservations concerning issues such as patient confidentiality, and legal and ethical risks. Objective.— To explore the utility of social media as an educational and collaborative tool in dermatopathology. Design.— We constructed 2 identical surveys containing questions pertaining to the responders' demographics and opinions regarding the use of social media for dermatopathology. The surveys were available on Twitter and Facebook for a period of 10 days. Results.— The survey was completed by 131 medical professionals from 29 different countries: the majority (81%, 106 of 131) were 25 to 45 years of age. Most replied that they access Facebook or Twitter several times a day (68%, 89 of 131) for both professional and social purposes (77%, 101 of 131). The majority agreed that social media provides useful and relevant information, but stated limitations they would like addressed. Conclusions.— Social media is a powerful tool with the ability to instantaneously share dermatopathology with medical professionals across the world. This study reveals the opinions and characteristics of the population of medical professionals currently using social media for education and collaboration in dermatopathology.

scholarly journals Is LinkedIn making you more successful? The informational benefits derived from public social media

2016 ◽  
Vol 18 (11) ◽  
pp. 2685-2702 ◽  
Author(s):  
Sonja Utz
Keyword(s):  
Social Capital ◽  
Social Media ◽  
Representative Sample ◽  
Relevant Information ◽  
Weak Ties ◽  
Fine Grained ◽  
Career Opportunities ◽  
The Social ◽  
Timely Access ◽  
Network Composition

This article uses a social capital framework to examine whether and how the use of three types of publicly accessible social media (LinkedIn, Twitter, Facebook) is related to professional informational benefits among a representative sample of Dutch online users. Professional informational benefits were conceptualized as the (timely) access to relevant information and being referred to career opportunities. The effect of content and structure of the respective online network on professional informational benefits was examined on the general (users vs. non-users of a platform) and more fine-grained level (within users of a specific platform). Overall, users of LinkedIn and Twitter reported higher informational benefits than non-users, whereas the Facebook users reported lower informational benefits. Posting about work and strategically selecting ties consistently predicted informational benefits. The network composition mattered most on LinkedIn; strong and weak ties predicted informational benefits. The results demonstrate the usefulness of the social capital framework.

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