Large-scale social-media analytics on stratosphere

The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows

Social Media Analytics Reporting Toolkit

With the fast growth of social media services, vast amount of user-generated content with time-space stamps are produced everyday. Considerable amount of these data are publicly available online, some of which collectively convey information that are of interest to data analysts. Social media data are dynamic and unstructured by nature, which makes it very hard for analysts to efficiently and effectively retrieve useful information. Social Media Analytics Reporting Toolkit (SMART), a system developed at Purdue VACCINE lab, aims to support such analyzing. The current framework collects real-time Twitter messages and visualizes volume densities on a map. It uses Latent Dirichilet Allocation (LDA) to extract regional topics and can optionally apply Seasonal-Trend decomposition using Loess (STL) to detect abnormal events. While Twitter has a fair amount of active users, they account for a small portion of total active social media users. Data generated by many other social media services are not currently utilized by SMART. Therefore, my work focused on expanding data sources of SAMRT system by creating means to collect data from other sources such as Facebook and Instagram. During a test run using a collection of 88 specified keywords in search, over two million Facebook posts were collected in one week. Besides, current SMART framework utilizes only one topic model, i.e. LDA, which is considered to be slower than Non-negative Matrix Factorization (NMF) model, thus I also put my effort into integrating NMF algorithm into the system. The improved SMART system can be used to fulfill a variety of analyzing tasks such as monitoring regional social media responses from different sources in disastrous events, detecting user reported crimes and so on. SMART is currently an ongoing and promising project that can be further improved by integrating new features

Credibility by automation : Expectations of future knowledge production in social media analytics

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 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.Peer reviewe

Social Media Analytics in Disaster Response: A Comprehensive Review

Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

#Liberty breach: An exploratory usage case of NodeXL Pro as a social media analytics tool for Twitter

Abstract: Social media analytics uses data mining tools, platforms, and analytics techniques to collect and analyse infinite amounts of social media data. Social media analytics tools extract patterns and connections from data, for insight into market sentiments and requirements, to enhance business intelligence. ‘Network Overview, Discovery and Exploration for Excel Pro’ (NodeXL Pro) is a social media analytics tool that simplifies basic network analysis tasks and supports the analysis of social media networks. NodeXL Pro does sophisticated ‘crawling’ (extracting data) across a range of social media platforms. Through a qualitative case study design, this study explores and describes the use of NodeXL Pro through empirical and multimodal analysis and social network visualisation of social media data of the Liberty Holdings Ltd data breach crisis case in June 2018. The hashtag ‘#Liberty breach’ resulted in 10 000 data sources (‘tweets’) from the social media platform Twitter. This study is unique on two levels. Firstly, it appears to be the first study in the South African marketing literature to use NodeXL Pro in social media analytics. Secondly, it presents the case study as a usage case to describe, in a step‐by‐step way, the functionalities of NodeXL Pro through social network analysis. The main finding of the paper focuses on the usability and manifold features (including the integrated visualisation tool) of NodeXL Pro. This social media analytics tool can open doors for marketing scholars and practitioners alike to measure, map, and model collections of connections

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

Social Media Analytics using Data Mining

There is a rapid increase in the usage of social media in the most recent decade. Getting to social media platforms for example, Twitter, Facebook LinkedIn and Google+ via mediums like web and the web 2.0 has become the most convenient way for users. Individuals are turning out to be more inspired by and depending on such platforms for data, news and thoughts of different clients on various topics. The substantial dependence on these social platforms causes them to produce huge information described by three computational issues in particular; volume, velocity and dynamism. These issues frequently make informal organization information exceptionally complex to break down physically, bringing about the related utilization of computational method for dissecting them. Information mining gives an extensive variety of strategies for recognizing valuable information from huge datasets like patterns, examples and standards. Various data mining strategies are utilized for useful data recovery, factual displaying and machine learning. These systems generally do a sort of pre-processing of data, performs the data analysis and information. This study examines distinctive information mining procedures utilized as a part of mining different parts of the informal community over decades going from the chronicled systems to the forward model

Social Media Analytics and Information Privacy Decisions: Impact of User Intimate Knowledge and Co-ownership Perceptions

Social media analytics has been recognized as a distinct research field in the analytics subdomain that is developed by processing social media content to generate important business knowledge. Understanding the factors that influence privacy decisions around its use is important as it is often perceived to be opaque and mismanaged. Social media users have been reported to have low intimate knowledge and co-ownership perception of social media analytics and its information privacy decisions. This deficiency leads them to perceive privacy violations if firms make privacy decisions that conflict with their expectations. Such perceived privacy violations often lead to business disruptions caused by user rebellions, regulatory interventions, firm reputation damage, and other business continuity threats. Existing research had developed theoretical frameworks for multi-level information privacy management and called for empirical testing of which constructs would increase user self-efficacy in negotiating with firms for joint social media analytics decision making. A response to this call was studied by measuring the constructs in the literature that lead to normative social media analytics and its information privacy decisions. The study model was developed by combining the relevant constructs from the theory of psychological ownership in organizations and the theory of multilevel information privacy. From psychological ownership theory, the impact that intimate knowledge had on co-ownership perception of social media analytics was added. From the theory of multi-level information privacy, the impact of co-ownership perception on the antecedents of information privacy decisions: the social identity assumed, and information privacy norms used were examined. In addition, the moderating role of the cost and benefits components of the privacy calculus on the relationship between information privacy norms and expected information privacy decisions was measured. A quantitative research approach was used to measure these factors. A web-based survey was developed using survey items obtained from prior studies that measured these constructs with only minor wording changes made. A pilot-study of 34 participants was conducted to test and finalize the instrument. The survey was distributed to adult social media users in the United States of America on a crowdsourcing marketplace using a commercial online survey service. 372 responses were accepted and analyzed. The partial least squares structural equation modeling method was used to assess the model and analyze the data using the Smart partial least squares 3 statistical software package. An increase in intimate knowledge of social media analytics led to higher co-ownership perception among social media users. Higher levels of co-ownership perception led to higher expectation of adoption of a salient social identity and higher expected information privacy norms. In addition, higher levels of expectation of social information privacy norm use led to normative privacy decisions. Higher levels of benefit estimation in the privacy calculus negatively moderated the relationship between social norms and privacy decision making. Co-ownership perception did not have a significant effect on the cost estimation in social media analytics privacy calculus. Similarly, the cost estimation in the privacy calculus did not have a significant effect on the relationship between information privacy norm adoption and the expectation of a normative information privacy decision. The findings of the study are a notable information systems literature contribution in both theory and practice. The study is one of the few to further develop multilevel information privacy theory by adding the intimate knowledge construct. The study model is a contribution to literature since its one of first to combine and validate elements of psychological ownership in organization theory to the theory of multilevel information privacy in order to understand what social media users expect when social media analytics information privacy decisions are made. The study also contributes by suggesting approaches practitioners can use to collaboratively manage their social media analytics information privacy decisions which was previously perceived to be opaque and under examined. Practical suggestions social media firms could use to decrease negative user affectations and engender deeper information privacy collaboration with users as they seek benefit from social media analytics were offered