THE BENEFITS AND BURDENS OF HIGH REPUTATION DURING DISRUPTIONS: THE ROLE OF MEDIA REPUTATION, ORGANIZATIONAL IDENTIFICATION, AND DISRUPTION TYPE

Organizational researchers are increasingly interested in the role of social approval assets, such as reputation and celebrity, for financial success of organizations. In this three-essay dissertation I examine the role of these assets when an organization is involved in negative disruptive events. In Essay 1, I introduce four media generated organizational types: celebrity, infamous, peripheral, and unfamiliar organizations and develop a theoretical framework and propositions that examine how stakeholder decisions whether or not to transact with an organization after disruptions depend on the type of organization under examination. In Essay 2, I argue theoretically and find empirically that stakeholder reactions to disruptions depend on the level of organizational identification. On a sample of on-campus murders in U.S. colleges and universities in 2001-2009, I find that universities receive fewer applications after murders, and this effect is stronger for ranked universities. Additionally, percentage of alumni donating to schools increases after on-campus murders, but only in ranked universities. I test the robustness of these findings using different operationalizations of disruptions and stakeholder groups. The results indicate that reputation is a liability during disruptions when stakeholders under examination have low levels of organizational identification and reputation is a buffer for reactions by high-identification stakeholders. In Essay 3, I argue that the amplifying role of organizational reputation is due to differences in news coverage of disruptions in high-reputation compared to low-reputation organizations. The results of empirical analysis of news coverage of 106 on-campus murders indicate that even after controlling for the characteristics of the event, disruptions in high-reputation organizations receive more coverage. I further examine this finding using content analysis of articles that covered four pairs of similar murders that took place in ranked vs. non-ranked universities. I find that not only do disruptions in high-reputation organizations receive more news coverage, but the coverage is more in-depth and the name of a high-reputation organization is more likely to appear in the article title. Taken together, the findings advance research on the role of media reputation, reputation, and organizational identification for organizations experiencing negative disruptions

Using Sensor Metadata Streams to Identify Topics of Local Events in the City

In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification

The Potential for cross-drive analysis using automated digital forensic timelines

Cross-Drive Analysis (CDA) is a technique designed to allow an investigator to “simultaneously consider information from across a corpus of many data sources”. Existing approaches include multi-drive correlation using text searching, e.g. email addresses, message IDs, credit card numbers or social security numbers. Such techniques have the potential to identify drives of interest from a large set, provide additional information about events that occurred on a single disk, and potentially determine social network membership. Another analysis technique that has significantly advanced in recent years is the use of timelines. Tools currently exist that can extract dates and times from the file system metadata (i.e. MACE times) and also examine the content of certain file types and extract metadata from within. This approach provides a great deal of data that can assist with an investigation, but also compounds the problem of having too much data to examine. A recent paper adds an additional timeline analysis capability, by automatically producing a high-level summary of the activity on a computer system, by combining sets of low-level events into high-level events, for example reducing a setupapi event and several events from the Windows Registry to a single event of ‘a USB stick was connected’. This paper provides an investigation into the extent to which events in such a high-level timeline have the properties suitable to assist with Cross-Drive Analysis. The paper provides several examples that use timelines generated from multiple disk images, including USB stick connections, Skype calls, and access to files on a memory card

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Crowdsourced Rumour Identification During Emergencies

When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss Kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation