Alexandria: Extensible Framework for Rapid Exploration of Social Media

The Alexandria system under development at IBM Research provides an extensible framework and platform for supporting a variety of big-data analytics and visualizations. The system is currently focused on enabling rapid exploration of text-based social media data. The system provides tools to help with constructing "domain models" (i.e., families of keywords and extractors to enable focus on tweets and other social media documents relevant to a project), to rapidly extract and segment the relevant social media and its authors, to apply further analytics (such as finding trends and anomalous terms), and visualizing the results. The system architecture is centered around a variety of REST-based service APIs to enable flexible orchestration of the system capabilities; these are especially useful to support knowledge-worker driven iterative exploration of social phenomena. The architecture also enables rapid integration of Alexandria capabilities with other social media analytics system, as has been demonstrated through an integration with IBM Research's SystemG. This paper describes a prototypical usage scenario for Alexandria, along with the architecture and key underlying analytics.Comment: 8 page

Decoding social media speak: developing a speech act theory research agenda

Purpose – Drawing on the theoretical domain of speech act theory (SAT) and a discussion of its suitability for setting the agenda for social media research, this study aims to explore a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data. Design/methodology/approach – Examining previously published cross-disciplinary research, the study identifies how recent conceptual and empirical advances in SAT may further guide the development of text analytics in a social media context. Findings – Decoding content and function word use in customers’ social media communication can enhance the efficiency of determining potential impacts of customer reviews, sentiment strength, the quality of contributions in social media, customers’ socialization perceptions in online communities and deceptive messages. Originality/value – Considering the variety of managerial demand, increasing and diverging social media formats, expanding archives, rapid development of software tools and fast-paced market changes, this study provides an urgently needed, theory-driven, coherent research agenda to guide the conceptual development of text analytics in a social media context

A systematic study on predicting depression using text analytics

Social Networking Sites (SNS) provides online communication among groups but somehow it is affecting the status of mental health. For adolescents with limited social media friends and using internet for communication purposes predicted less depression, whereas  non-communication desire reveals more depression and anxiety disorder. Social media posts and comments provide a rich source of text data for academic research. In this paper, we have discussed various text analytical approaches to predict depression among users through the sharing of online ideas over such websites. This paper presents a  comprehensive review for predicting depression disorder by various text analytics approaches. This paper also presents the summary of results obtained by some researchers available in literature to predict MajorDepressive Disorder (MDD). In future research, enable self-monitoring of health status of each individuals which may help to increase well-being of an identity.Keywords: Social Networking Sites; Sentiment Analysis; Machine Learning; Support Vector Machine

Reflektierte algorithmische Textanalyse. Interdisziplinäre(s) Arbeiten in der CRETA-Werkstatt

The Center for Reflected Text Analytics (CRETA) develops interdisciplinary mixed methods for text analytics in the research fields of the digital humanities. This volume is a collection of text analyses from specialty fields including literary studies, linguistics, the social sciences, and philosophy. It thus offers an overview of the methodology of the reflected algorithmic analysis of literary and non-literary texts

Discourse-centric learning analytics

Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners' discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning

Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

With the rapid rise in the popularity of social media (500M+ Facebook users, 100M+ twitter users), and near ubiquitous mobile access (4.1 billion actively-used mobile phones), the sharing of observations and opinions has become common-place (nearly 100M tweets a day, 1.8 trillion SMSs in US last year). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it towards targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped and developing countries. This tutorial will address challenges and techniques for building applications that support a broad variety of users and types of social media. This tutorial will focus on social intelligence applications for social development, and cover the following research efforts in sufficient depth: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) building social media analytics platforms. Technical insights will be coupled with identification of computational techniques and real-world examples

When can social media lead financial markets?

Social media analytics is showing promise for the prediction of financial markets. The research presented here employs linear regression analysis and information theory analysis techniques to measure the extent to which social media data is a predictor of the future returns of stock-exchange traded financial assets. Two hypotheses are proposed which investigate if the measurement of social media data in real-time can be used to pre-empt – or lead – changes in the prices of financial markets. Using Twitter as the social media data source, this study firstly investigates if geographically-filtered Tweets can lead the returns of UK and US stock indices. Next, the study considers if string-filtered Tweets can lead the returns of currency pairs and the securities of individual publically-traded companies. The study evaluates Tweet message sentiments – mathematical quantifications of text strings’ moods – and Tweet message volumes. A sentiment classification system specifically designed and validated in literature to accurately rank social media’s colloquial vernacular is employed. This research builds on previous studies which either use sentiment analysis techniques not geared for such text, or which instead only consider social media message volumes. Stringent tests for statistical-significance are employed. Tweets on twenty-eight financial instruments were collected over three months – a period chosen to minimise the effect of the economic cycle in the time-series whilst encapsulating a range of market conditions, and during which no major product changes were made to Twitter. The study shows that Tweet message sentiments contain lead-time information about the future returns of twelve of these securities, in excess of what is achievable via the analysis of Twitter message volumes. The study’s results are found to be robust against modification in analysis parameters, and that additional insight about market returns can be gained from social media data sentiment analytics under particular parameter variations

Improving Customer Education: Study Of Customer Engagement Of Tokopedia And Shopee On Twitter Using Social Network Analysis

Customer education is one type of customer engagement. Through the active involvement of customers in the value creation process, customer education can give a business a competitive advantage. This study uses social network analysis (SNA) and text analysis to examine user interaction and information sharing about two Indonesian e-commerce sites on Twitter concerning customer engagement and customer education. Text data was scraped from Twitter from November 1 to December 31, 2022, and processed using Gephi and Orange software. This study found that the data shows that both official accounts of two Indonesian e-commerce become the most influential accounts in sharing information about products and services. They also play a role in educating their customers regarding the buying process, promotions, and solutions on their social networks. SNA and text analytics can be used to see customer engagement and improve customer education strategies for e-commerce to reach a competitive advantage. For further research, examining the same or different objects is recommended but using data sources from different social media, such as Instagram and Facebook

MINING FACEBOOK PAGE FOR BI-PARTISAN ANALYSIS

Social media, particularly Facebook, has become ubiquitous in everyday life. Almost all news sources have adopted Facebook as a platform for dissemination of news. There are many opinions and studies on the partisanship of journalism. What makes social media interesting is that people do not only consume but also interact with others centered around a news article or post. Depending on the partisan bias of both the provider and the consumer, the interactions, and thus the conversation may vary. This research is a preliminary step towards mining these interactions and conversations pivoted against the topic of “fake news” from CNN and Fox News. We used several techniques of data mining, data analytics, and text analytics to generate summaries and descriptive statistics to explore user behavior. Our findings suggest that CNN follower base is more interactive and gregarious. Additionally, CNN followers’ use of Facebook reactions is more diverse, favoring the “haha” (funny / sarcastic) reaction, while those on Fox News’ inclined more towards “like” and “love” (agreement)

Learning about text and data mining: The future of Open Science

The volume of digital data is doubling every two years. In the world of science, the cumulative total of articles published since 1665 is estimated to be more than 50 million. There is a wealth of knowledge hidden in this huge amount of articles, but reading and analysing all of them manually is not humanly possible. Text and data mining (TDM) can provide a solution. It can process millions of texts quickly and reveal patterns and trends that can lead to new discoveries in various fields, for example in research analytics, medicine, agriculture and social sciences. The European project OpenMinTeD [http://openminted.eu/] helps to solve these problems with a new platform on text and data mining