Application of Data Visualization in Social Media Analysis

With the rapid development of Big Data and the necessity for analyzing their huge volumes, the issue of Unstructured Data analysis in social media was appeared. The Data analysis process is very important in all fields as to make decisions at the right time and over certain facts. The usage of social media has become the latest trend in today's world in which users send, read posts known as `message' and communicate with various groups. Users are sharing their regular life, posting their views on everything like products and locations. This data is extremely unstructured, making it hard to analyze. Machine learning technology offers important data preparation techniques for processing large-scale data to extract knowledge, e.g., classifying data. Extract useful information from social media data is essential to success in the big data age. Therefore, fresh strategies are needed for handling huge quantities of unstructured data and finding the hidden information in these data and achieving better data analysis outcomes, In this paper, the proposed framework recommends the construction of a machine-learning model capable of analyzing unstructured text data with highly accuracy compared to other machine learning algorithms.

Social media is being employed to build support for social, economic, and political justice (Selander et al. 2016; Vaast et al. 2014) in movements such as occupy Wall Street, violence against women's movement and global sustainability movement. These movements have used social media in ways that goes beyond simple communications. As social media allows people to produce and share user generated content, they enable a certain set of affordances of these technologies (Bharati et al 2015; Bharati et al 2014). The social media affordances, available to both collective and individual actors, translate into capabilities afforded to social movements (Tufecki 2014). Research on connective action has examined the effects of digital action repertoires on interaction and engagement such as in the Tea Party and Occupy movements (Agarwal et al. 2014; Selander et al. 2016). Social media can also facilitate mobilization of movement and participation by new volunteers and oftentimes provides a transnational character by diffusing actions beyond the virtual (Van Laer and Van Aelst 2010). Conversation, an essential part of social movements, shapes “social life by altering individual and collective understandings, by creating and transforming social ties, by generating cultural materials that are then available for subsequent social interchange, and by establishing, obliterating, or shifting commitments on the part of participants&x201D; (Tilly 2002, p. 122). In a personal interaction that involves repeated organized interactions between individuals, typically, leads to shared values and trust. The role of social media technologies in furthering this conversation has to be studied and its' influence on social movement ascertained. A few scholars have started to investigate social media affordances and capabilities, especially focused on discourse, during contentious collective action. Still the research has been limited to studying mechanisms of participation, development of a sense of collective identity, creation of community, and framing of political discourse (Farrell 2012; Garrett 2006). Social media data on social movements can involve impersonal “like&x201D; and “share&x201D; to more engaged conversations. Twitter, Instagram, Facebook, and YouTube offer a wide communicative and discourse reach in networks with text, image, audio, and video data. This social media based big data, consisting mostly of unstructured data, comes in the form of social media posts, digital pictures and videos. The symposium will discuss how formal organizational structures and practices might be integrated with social media capabilities to reinforce and enhance social movement organizations as leaders in social change movements. It will explore the characteristics of social media discourse and assess the dynamics of social movements and, subsequent, impact on real-world protests. The symposium will also demonstrate how discourse analysis can be applied visually in order to understand communication patterns. The panel symposium will focus on theoretical and methodological challenges of social media analytics, big data and social movements. Panelists will engage the audience in an interactive discussion on: 1) Theoretical challenges: a. How and why are social movement recruitment and engagement mechanisms being impacted as a result of social media? b. How do we advance theory on social media and social movements when we are overwhelmed with social media based big data? c. What approach should we undertake if big data analysis contradicts most theories on social media and social movements? d. How do we address the issue of generalizability of social media and social movement research when data collection was limited to one social media platform, albeit involving big data? 2) Methodological challenges: a. What methodological approaches have worked in the analysis of social media, big data and social movements? b. How can discourse analysis be applied visually in order to understand communication patterns evident in social media-based big data? c. How can we employ social media analytics to investigate image and video data? d. What combinations of qualitative and quantitative methodologies be employed for big data and social media analytics in the context of social movements? e. What are the limitations of quantitative data analysis techniques, such as structural equation modeling, because of an extremely large sample size? f. What are the limitations of qualitative data analysis techniques as they become extremely labor intensive and, maybe even, impractical because of big data?

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research.

Analytics is very important in all fields in order to make decisions over certain facts. Social media analytics is the process of collecting information from various social media platforms, websites and blogs. This analytics is done to make effective business conclusions. The usage of social media has become the latest trend in today's world. Social data analytics is not about just collecting likes and comments shared by individuals but it has become the platform for many trademarks to bring out promotion. Applications such as marketing, elections widely used social data to make predictive decisions. Some of the approaches followed are forming hypothesis, getting deep into the data, mapping events etc. This analytics can also be done in applications such as business, Change in amendments, Education, Demonetization etc. The challenges faced are metrics formed by social media should reach the right people, unstructured data being difficult to process. This paper discusses about the model, theme, performance evaluation, advantages and disadvantages under literature survey.

PurposeThe use of social media in crisis communication is growing and spreading in an instantaneous speed. The social media technology enables immediate information sharing reaching millions of users on various social media platforms. This paper has gathered lessons learnt from the experiences of the Norfolk Fire and Rescue Service in the UK, the Oslo Police Operation Center in Norway, and from an extensive literature review on social media and crisis communication. This empirical and theoretical information was the basis of the 18 guidelines or “do’s” and “don’ts” on how to use social media in crisis communication. The purpose of this paper was to gather best practices that can help crisis managers when attempting to use social media as a crisis communication tool.Design/methodology/approachThe paper had two cases with semi-structured interviews with representatives from two crisis management organizations (Norfolk Fire and Rescue Service in the UK dealing with safety and Oslo Police Operation Center in Norwa dealing with security). The empirical data were complemented by documentary analysis of the most up-to-date articles on social media in crisis communication.FindingsThe paper provides empirical insights about how to use social media as a crisis communication tool. The two case studies provide different ways to use social media (one-way communication tool and as a two-way information share). This empirical and theoretical information was the basis of the 18 guidelines or “do’s” and “don’ts” on how to use social media in crisis communication. The “do’s” and “don’ts” are best practices that can help crisis managers when attempting to use social media as a crisis communication tool. Some of the main “do’s” include building a relationship with the public prior to a crisis; being courteous, honest and transparent; being factual, accurate and credible; being timely in your messages during a crisis, etc. Some of the main “don’ts” include do not speculate; do not post personal opinions; do not post anything that could bring the organization into disrepute etc.Research limitations/implicationsBecause of the chosen research approach with two case studies complemented by a literature review, the research results may lack generalizability. Therefore, researchers are encouraged to test the guidelines provided and to further find other case studies.Practical implicationsThe guidelines including 18 “do’s” and “don’ts” are best practices that can help crisis managers when attempting to use social media as a crisis communication tool.Originality/valueThis paper fulfills an identified need to study how social media technology can play a major role in the response efforts of the crisis management community during a crisis. It also reveals the potential of using social media as an “information harvesting” tool and a tool for “rumor management”.

Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated.

Purpose of the study: This study focuses on understanding the potential unfavourable social media analytics practices (SMA) and how they may lead to the negative perception of consumer self-disclosure on social media sites and causing market discomfort for people. We also examined a framework regarding the SMA practices, and their impact on the customer’s trust to use social media networks.
 Methodology: Data were collected from 527 social media users with the help of an online questionnaire. Partial Least Squares Structural equation modeling—PLS-SEM was used using SmartPLS to validate the questionnaire and measure the hypothesized relationships between the constructs.
 Main Findings: The results show that perceived risk mediates the relationship between the perceived ethicality of social media analytics and the marketing discomfort of the consumers. Moreover, we also found that the negatively perceived ethicality of social media analytics leads to marketing discomfort of the consumers. This negative attitude impacts the consumer’s intention to purchase, interest in the product, and intention to be vulnerable toward the company.
 Applications of this study: These findings have practical utility for all types of organizations that use social media. This study is general for all social media sites. This study also provides direction for future research, such as managing the personal information of the social media users from being used by the organizations without consumer permission and developing ethical standards to use the consumer information for the marketing strategies.
 Novelty/Originality of this study: Social media analytics (SMA) gathered data from the social media platforms such as Twitter, Instagram, and blogs, representing a massive room to utilize public data, and these lead towards the ethical concerns of the consumers. For this problem, we studied a complete framework to identify the major outcomes of the PESMA on the consumers' self-disclosure and marketing comfort.

Integrating color cues to improve multimodal sentiment analysis in social media

Research on Sentiment Analysis in social media by using Mesopotamian-Iraqi Dialect (MID) of Arabic language was rarely found, there is no reliable dataset developed in MID neither an annotated corpus for the sentiment analysis of social media in this dialect. Therefore, this gap was the main stumbling block for researchers of sentiment analysis in MID, for this reason, this paper introduced the development of an annotated corpus of Mesopotamian-Iraqi Dialect for sentiment analysis in social media and named it as (ACMID) stands for (the annotated corpus of Mesopotamian-Iraqi Dialect) to help researchers in future for using this corpus for their studies, to the best of our knowledge this is the first annotated corpus that both classify polarity as well as emotion classification in MID. Likewise, Facebook as the most popular social platform among Iraqis was used to extract the data from its popular Iraqi pages. 5000 comments were extracted from these pages classified by its polarity (Positive, Negative, Neutral, Spam) by two Iraqi annotators, these annotators were simultaneously classifying the same comments according to Ekman seven universal emotions (Anger, Fear, Disgust, Happiness, Sadness, Surprise, Contempt) or no emotion. Cohen's kappa coefficient was then used to compare the two annotators’ results to find the reliability of these results. The data shows a comparable value among the two annotators for the polarity classification as high as 0.82, while for the emotion classification the result was 0.65.

Purpose Based on an organizational capability perspective, this paper aims to propose a development model for social media analytics (SMA) capability that can be applied to business-to-business (B2B) marketing, with the aim of facilitating the use and integration of SMA in B2B marketing and maximizing the benefits of business networks in the age of social media. Design/methodology/approach This is a critical interpretive synthesis of SMA publications collected from academic journals, business magazines and the SMA service industry. In addition, an inter-disciplinary approach was adopted by drawing upon both marketing and information systems literature. In total, 123 academic papers, 106 industry case studies and 141 magazine papers were identified and analyzed. The findings were synthesized and compiled to address the predefined research question. Findings An SMA capability development model is proposed. The proposed model consists of four inter-dependent levels (technological, operational, managed and strategic) that collectively transfer the technological capability of SMA to the dynamic organizational capability. Each level of SMA capability is detailed. SMA-in-B2B marketing is highlighted as a socio-technical phenomenon, in which a technological level SMA capability is emphasized as the foundation for developing organizational level SMA capabilities and organizational capabilities, in turn, supporting and managing SMA activities and practices (e.g. strategic planning, social and cultural changes, skills and resources, measurements and values). Practical implications The proposed research framework may have implications for the operational level SMA development and the investigations on the direct and/or indirect measurements to help firms see the impact of SMA on their business. Originality/value This study may have implications for the adoption, use, integration and management of SMA in B2B marketing. The proposed model is grounded on the integrated insights from academia and industry. It is particularly relevant to B2B firms that have engaged in or plan to engage in applying SMA to extract insights from their online networks and is relevant to B2B researchers who are interested in SMA, big data and information technology organization integration studies.

Although social media has become the most widely utilized and active form of communication, research on its usage in crisis management is still in its early stages. As a result, this research examines the rising body of knowledge on social media and crisis management. [1] Between October 2017 and January 2018, a review was conducted, which included locating and retrieving records from an electronic database. The outcomes of this study indicated that the rise of social media has altered the landscape of crisis communication by allowing for greater engagement. However, due to its nature, social media might also be used to spark a crisis. This means that the crisis can be both produced and disseminated through social media. Nonetheless, social media's promise as a crisis-resolution tool is undeniable. It has the capability of proving a claim, dispelling false rumors, or just demonstrating a fact. As a result, practitioners should understand how social media works and how to best use it to interact with their stakeholders. This study also includes other findings, limits, and useful suggestions for scholars and practitioners interested in learning more about the role of social media on crisis communication and management. As most of the crisis problem were reported via twitter. However, most of the problem reported and corresponding responses via twitter were not successfully exchanged between victim’s and resource organization. As a result, most of the tweets were not getting help. Thus, we designed a platform where people can avail the resources of crisis through tweets matching concept using machine learning.

Social media analytics is a burgeoning new field associated with high promises of societal relevance and business value but also methodological and practical problems. In this article, we build on the sociology of expectations literature and research on expertise in the interaction between humans and machines to examine how analysts and clients make their expectations about social media analytics credible in the face of recognized problems. To investigate how this happens in different contexts, we draw on thematic interviews with 10 social media analytics and client companies. In our material, social media analytics appears as a field facing both hopes and skepticism – toward data, analysis methods, or the users of analytics – from both the clients and the analysts. In this setting, the idea of automated analysis through algorithmic methods emerges as a central notion that lends credibility to expectations about social media analytics. Automation is thought to, first, extend and make expert interpretation of messy social media data more rigorous; second, eliminate subjective judgments from measurement on social media; and, third, allow for coordination of knowledge management inside organizations. Thus, ideas of automation importantly work to uphold the expectations of the value of analytics. Simultaneously, they shape what kinds of expertise, tools, and practices come to be involved in the future of analytics as knowledge production.

The volume of data in the world is growing very fast and generated from verity of sources like social media, sensors airline industry or scientific data in different formats. Biggest challenge is how to infer meaningful insights from such a varietyful and big data along with concern of data storage and management of fast growing data. The size of the databases used in today’s enterprises has been growing at exponential rates day by day. Hence, industries requirement to quickly process and analyze the big data volumes for business decision making and customer insights has also grown exponentially. Data pouring from various sources may be can be structured or unstructured in nature. Structured data refers to a relatively well-organized information, which can be further inserted into traditional RDBMS. As Traditional RDBMS are efficient and easy queries by simple, straightforward search algorithms or SQL queries. In contrast to structured data, unstructured data can be considered as information, which does not, comes in a pre-defined data format, well organized data storage model, or cannot be stored well into relational tables. It is assumed to be fastest growing type of data, e.g. image, sensors data, web chats, social networking messaging data, video, documents, log files, and email data. There are many techniques and software available, which can process and provide efficient storage of unstructured data and help organization to perform analytics on unstructured data. Unstructured data does not well-organized and not stored in predefined manner e.g. logs, web chats. The variety and on ordered nature of data makes storage methods and structure makes execution a time and resource-consuming affair. Advancement into technology has open floodgates to push huge volume of unstructured type of data. Multimedia data is one of the example of unstructured big data, which spans all over the Internet. This needs high execution capability to extract useful information. Rapid processing of multimedia data such as video is important for e.g. criminal investigations, surveillance monitoring, news analysis, sports analytics domain, emotion extraction, etc. Hence, analysis of multimedia data in minimum timeframe is one of the latest research areas. Therefore, we have researched techniques for analyzing unstructured data to extract meaningful information hidden in the big data. In addition, we will describe about various techniques and software used to Manage, process unstructured big data in efficient manner, and increases the performance of complexity analysis.

Sentiment Analysis in Social Media and Its Application: Systematic Literature Review

This has changed in the digital age, where social media is no longer seen as a tool of communication, but rather it has become a transformation factor that is transforming how organisations innovate and redesign their business processes. The review paper discusses the complex influence of social media on digital innovation and transformation of business processes based on the strategic importance of social media in facilitating agile, data-driven and customer-centric organisational ecosystems. Based on an integrative review of the existing research, the research paper summarises the ways social media platforms enable open innovation, promote collaboration, spread knowledge faster and democratise the generation of ideas via digital communities. The paper explains how social media analytics, artificial intelligence, and big data help in enhancing innovation potentials through providing real-time insights, predictive intelligence, and evidence-based decision-making. The review further examines how the business processes, especially marketing, customer relationship management, human resources and supply chain operations, are undergoing reengineering with the integration of social media, which is facilitating the firms to respond with personalised digital experiences. Although these opportunities exist, the review marks various challenges, such as privacy concerns, overload of information, skills gaps, organisational resistance and reliance on a particular platform that could lead to barriers to beneficial transformation. The paper ends with future research directions highlighting the use of social media and emerging technologies, including AI, IoT, blockchain, and the metaverse, to provide future directions for further digital transformation. On balance, the study offers a detailed insight into the role of social media as an agent of digital innovation and the transformation of the business process that allows organisations to be competitive in a constantly evolving technological environment. Keywords: Social Media, Digital Innovation, Business Process Transformation, Social Media Analytics, Open Innovation, Digital Transformation, Emerging Technologies