Social media analytics is a critical research area spawned by the increasing availability of rich and abundant online user-generated content. So far, social media analytics has had a profound impact on organizational decision making in many aspects, including product and service design, market segmentation, customer relationship management, and more. However, the cybersecurity sector is behind other sectors in benefiting from the business intelligence offered by social media analytics. Given the role of hacker communities in cybercrimes and the prevalence of hacker communities, there is an urgent need for developing hacker social media analytics capable of gathering cyber threat intelligence from hacker communities for exchanging hacking knowledge and tools. My dissertation addressed two broad research questions: (1) How do we help organizations gain cyber threat intelligence through social media analytics on hacker communities? And (2) how do we advance social media analytics research by developing innovative algorithms and models for hacker communities? Using cyber threat intelligence as a guiding principle, emphasis is placed on the two major components in hacker communities: threat actors and their cybercriminal assets. To these ends, the dissertation is arranged in two parts. The first part of the dissertation focuses on gathering cyber threat intelligence on threat actors. In the first essay, I identify and profile two types of key sellers in hacker communities: malware sellers and stolen data sellers, both of which are responsible for data breach incidents. In the second essay, I develop a method for recovering social interaction networks, which can be further used for detecting major hacker groups, and identifying their specialties and key members. The second part of the dissertation seeks to develop cyber threat intelligence on cybercriminal assets. In the third essay, a novel supervised topic model is proposed to further address the language complexities in hacker communities. In the fourth essay, I propose the development of an innovative emerging topic detection model. Models, frameworks, and design principles developed in this dissertation not only advance social media analytics research, but also broadly contribute to IS security application and design science research.

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

Yes / Purpose This study aims to understand pumpkin waste awareness among people by converting unstructured quantitative data into insightful information to understand the public's awareness of pumpkin waste during Halloween. Design/methodology/approach To fulfil the study's purpose, we extracted Halloween-related tweets by employing #halloween and #pumpkin hashtags and then investigated Halloween-related tweets via a topic modelling approach, specifically Latent Dirichlet Allocation. The tweets were collected from the UK between October 25th and November 7th, 2020. The analysis was completed with 11,744 tweets. Findings The topic modelling results revealed that people are aware of the pumpkin waste during Halloween. Furthermore, people tweet to reduce pumpkin waste by sharing recipes for using leftover pumpkins. Originality/value The study offers a novel approach to convert social media data into meaningful knowledge about public perception of food waste. This paper contributes to food waste literature by revealing people's awareness of pumpkin waste during Halloween using social media analytics. Norm activation model and communicative ecology theory are used for the theoretical underpinning of topic modelling.

Die Arbeit untersucht insgesamt 25 kostenfreie Social Media Analytics-Werkzeuge und liefert einen Beitrag zu einer systematischen Beurteilung dieser Anwendungssystemklasse im Rahmen des Social Customer Relationship Managements.:1 Einleitung 1.1 Motivation 1.2 Problemstellung 1.3 Vorgehen 2 Grundlagen 2.1 Social Media 2.2 Social CRM 2.3 Social Media-Analys 2.4 Softwareanalyse 2.5 Prototyping 3 Analyse von Social Media-Analyse-Tools 3.1 Kurzvorstellung der einzelnen Tools 3.2 Kernfunktionalitäten kostenfreier SMA-Anwendungen 3.3 Realisierbare Anwendungsfälle im SCRM 3.4 Vergleich mit Funktionalitäten einer kostenpflichtigen SMA-Anwendung 3.5 Betrachtung von Unterschieden 4 Entwicklung einer Auswahlhilfe 4.1 Vorüberlegungen 4.2 Implementierung 4.3 Beschreibung 5 Erkenntnisse 5.1 Ergebnisse 5.2 Defizite 6 Ausblick

The purpose of this study is to develop a guideline/map for small businesses to effectively utilise social media analytics and to create appropriate strategies through an examination of Key Performance Indicators with the business analytics process (Strategy-making map). Also, the researcher examines the new sequential relationships among business analytics types, the role of human analysts in business analytics, and the reciprocal relationships among the social media marketing goals. This is a case study with a local small business’s social media analytics provided by a social media network platform (i.e., Facebook Insights). The map visualises how to interpret the KPIs and how to create effective marketing strategies based on the organisational decision-making model. The results supported that if a business could create a winning strategy based on accurate business analytics with human analysts, the business could achieve multiple social media goals at once with a single marketing strategy.

This research builds on the social movements theory of networks and coalition building, the theory of digital activism, and the social networks theory of organizations to study the rich case of online mobilization for the 2011 Egyptian revolution. I use the analytical tools of social network analysis to study Twitter networks of activists of the Egyptian revolution in early 2011, when solidarity characterized the movement, and late 2014, when schism spread it apart. In this, I investigate how the repertoire of online activism relates to the on-the-ground movement. The social movements theory of networks states that activists’ ideological congruence, the presence of bridge builders who bring the movement together, and the presence of previous ties among the activists are all factors of coalition building and movement solidarity. This dissertation tested the role of these factors in the Twitter networks of Egyptian activists. The results suggest that digital activism complements rather than mirrors on-the-ground activism. While all three factors influence the network, they manifest somewhat differently than research has suggested they do in offline networks. This dissertation contributes to social movements theory of coalition building through adding validity to its application to digital activism, and suggests modifications to be made while applying this theory to the repertoire of online mobilization. The research has a methodological contribution through using cutting edge techniques of social network analysis to study Twitter networks of activists. Unlike earlier studies on the Egyptian revolution, this methodological approach revealed new findings that could not have been studied through other methods of research. / Dissertation / Doctor of Philosophy (PhD)

Dynamic query expansion is a method of automatically identifying terms relevant to a target domain based on an incomplete query input. With the explosive growth of online media, such tools are essential for efficient search result refining to track emerging themes in noisy, unstructured text streams. It's crucial for large-scale predictive analytics and decision-making, systems which use open source indicators to find meaningful information rapidly and accurately. The problems of information overload and semantic mismatch are systemic during the Information Retrieval (IR) tasks undertaken by such systems. In this dissertation, we develop approaches to dynamic query expansion algorithms that can help improve the efficacy of such systems using only a small set of seed queries and requires no training or labeled samples. We primarily investigate four significant problems related to the retrieval and assessment of event-related information, viz. (1) How can we adapt the query expansion process to support rank-based analysis when tracking a fixed set of entities? A scalable framework is essential to allow relative assessment of emerging themes such as airport threats. (2) What visual knowledge discovery framework to adopt that can incorporate users' feedback back into the search result refinement process? A crucial step to efficiently integrate real-time `situational awareness' when monitoring specific themes using open source indicators. (3) How can we contextualize query expansions? We focus on capturing semantic relatedness between a query and reference text so that it can quickly adapt to different target domains. (4) How can we synchronously perform knowledge discovery and characterization (unstructured to structured) during the retrieval process? We mainly aim to model high-order, relational aspects of event-related information from microblog texts. / Ph. D. / Analysis of real-time, social media can provide critical insights into ongoing societal events. Where consequences and implications of specific events include monetary losses, threats to critical infrastructure and national security, disruptions to daily life, and a potential to cause loss of life and physical property. It is imperative for developing good ‘ground truth’ to develop adequate data-driven information systems, i.e., an authoritative record of events reported in the media cataloged alongside important dimensions. Availability of high-quality ground truth events can support various analytic efforts, e.g., identifying precursors of attacks, developing predictive indicators using surrogate data sources, and tracking the progression of events over space and time. A dynamic search result refinement is useful for expanding a general set of user queries into a more relevant collection. The challenges of information overload and misalignment of context between the user query and retrieved results can overwhelm both human and machine. In this dissertation, we focus our efforts on these specific challenges. With the ever-increasing volume of user-generated data large-scale analysis is a tedious task. Our first focus is to develop a scalable model that dynamically tracks and ranks evolving topics as they appear in social media. Then to simplify the cognitive tasks involving sense-making of evolving themes, we take a visual approach to retrieve situationally critical and emergent information effectively. This visual analytics approach learns from user’s interactions during the exploratory process and then generates a better representation of the data. Thus, improving the situational understanding and usability of underlying data models. Such features are crucial for big-data based decision & support systems. To make the event-focused retrieval process more robust, we developed a context-rich procedure that adds new relevant key terms to the user’s original query by utilizing the linguistic structures in text. This context-awareness allows the algorithm to retrieve those relevant characteristics that can help users to gain adequate information from social media about real-world events. Online social commentary about events is very informal and can be incomplete. However, to get the complete picture and adequately describe these events we develop an approach that models the underlying relatedness of information and iteratively extract meaning and denotations from event-related texts. We learn how to express the high-order relationships between events and entities and group them to identify those attributes that best explain the events the user is trying to uncover. In all the augmentations we develop, our strategy is to allow only very minimal human supervision using just a small set of seed event triggers and requires no training or labeled samples. We show a comprehensive evaluation of these augmentations on real-world domains - threats on airports, cyber attacks, and protests. We also demonstrate their applicability as for real-time analysis that provides vital event characteristics, and contextually consistent information can be a beneficial aid for emergency responders.

Yes / The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.

Yes / Social media analytics (SMA) is a dynamic field which has received considerable attention from both academics and management practitioners alike. A significant number of the scholarly research currently being conducted in SMA, however, is conceptual. Industry experts know that SMA creates new opportunities for organisations who want to more strongly engage with their customers and improve business performance. However, the relationship between social media analytic practices (SMAP), customer engagement (CE), and business performance (BP) has not yet been sufficiently investigated from an empirical perspective. In order to gain a better understanding of the relationship between SMAP and BP and the mediation role of CE in that process, a large-scale survey was conducted among senior and mid-level managers as well as consultants in the Retail and information technology (IT) industries in India. Specifically, a structured closed-ended questionnaire was administered to managers and management consultants country-wide and gathered usable responses from 281 respondents holding positions such as: Digital Marketing Executive/Digital Marketing Specialist, Management Consultant, Analytics Manager, Customer Relationship Manager, Marketing Director, Engagement Manager, etc. who were in charge of digital marketing strategies in the respondent retail and IT organisations. The questionnaire addressed issues related to the way in which SMAP contribute to an enhanced business performance through the mediation role of customer engagement. Structural Equation Modelling was employed to analyse the received empirical data. On the basis of the findings our research concludes that there is a significant positive relationship between SMAP and BP mediated by CE in the Indian retail and IT industries.

Yes / This exploratory research aims to investigate social media users’ expectations of information systems (IS) products that are conceived but not yet launched. It specifically analyses social media data from Twitter about forthcoming smartphones and smartwatches from Apple and Samsung, two firms known for their innovative gadgets. Tweets related to the following four forthcoming IS products were retrieved from 1st January 2020 to 30th September 2020: (1) Apple iPhone 12 (6,125 tweets), (2) Apple Watch 6 (553 tweets), (3) Samsung Galaxy Z Flip 2 (923 tweets), and (4) Samsung Galaxy Watch Active 3 (207 tweets). These 7,808 tweets were analysed using a combination of the Natural Language Processing Toolkit (NLTK) and sentiment analysis (SentiWordNet). The online community was quite vocal about topics such as design, camera and hardware specifications. For all the forthcoming gadgets, the proportion of positive tweets exceeded that of negative tweets. The most prevalent sentiment expressed in Apple-related tweets was neutral but in Samsung-related tweets was positive. Additionally, it was found that the proportion of tweets echoing negative sentiment was lower for Apple compared with Samsung. This paper is the earliest empirical work to examine the degree to which social media chatter can be used by project managers for IS development projects, specifically for the purpose of end-users’ expectation management.