Andrew Lampert

Bio: Andrew Lampert is an academic researcher from Palantir Technologies. The author has contributed to research in topics: Situation awareness & Social media. The author has an hindex of 10, co-authored 20 publications receiving 927 citations. Previous affiliations of Andrew Lampert include Macquarie University & Commonwealth Scientific and Industrial Research Organisation.

Using Social Media to Enhance Emergency Situation Awareness

Abstract: The described system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises.

Methods and devices for analyzing text

Abstract: A method, operating model, system, method, computer program, application, online service, or application program interface (API) Application Program Interface (API), and computer program product for analyzing any email message or text, online post, online web pages, social media sites, and online news sites to detect predefined and actionable events and intent. A method for detecting important emails or messages, and actionable emails or messages that signify intent including questions or promises. A method for detecting past or possible future events in any online posts where the event is defined a priori.

Detecting Emails Containing Requests for Action

Abstract: Automatically finding email messages that contain requests for action can provide valuable assistance to users who otherwise struggle to give appropriate attention to the actionable tasks in their inbox. As a speech act classification task, however, automatically recognising requests in free text is particularly challenging. The problem is compounded by the fact that typical emails contain extraneous material that makes it difficult to isolate the content that is directed to the recipient of the email message. In this paper, we report on an email classification system which identifies messages containing requests; we then show how, by segmenting the content of email messages into different functional zones and then considering only content in a small number of message zones when detecting requests, we can improve the accuracy of message-level automated request classification to 83.76%, a relative increase of 15.9%. This represents an error reduction of 41% compared with the same request classifier deployed without email zoning.

Using social media to enhance emergency situation awareness

Abstract: Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.

The nature of requests and commitments in email messages

Abstract: It has long been established that many workplace tasks are managed through email communication, and that these tasks involve the exchange of requests and commitments. Users would be better able to manage and monitor tasks in their email if systems could identify the utterances which place responsibility for action on themselves or others. Such systems require a robust understanding of which utterances convey requests and commitments. Previous attempts to classify similar phenomena in email have mostly been at the message level and have lacked detailed and robust category definitions that allow unambiguous classification at the utterance level. To address this gap, this paper presents precise definitions for classifying requests and commitments in email, based on concepts from Speech Act Theory, and informed by the results of two independent manual annotation experiments using data from the Enron email corpus. The specific surface realisation of requests and commitments in email are also considered, with the aim of clarifying how a range of potentially difficult cases should be dealt with. This paper thus contributes a well-grounded definitional basis for the classification of task-oriented speech acts in email.

Processing Social Media Messages in Mass Emergency: A Survey

Abstract: Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

Detection and Resolution of Rumours in Social Media: A Survey

Abstract: Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.