Adaptive Representations for Tracking Breaking News on Twitter

Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of these mechanisms on a number of recent news events for which manually curated timelines are available. Assessments based on ROUGE metrics indicate that an adaptive approaches are best suited for tracking evolving stories on Twitter.Comment: 8 Pag

TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and other info available at http://cs.stanford.edu/~althoff/timemachine

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

Evaluation of spoken document retrieval for historic speech collections

The re-use of spoken word audio collections maintained by audiovisual archives is severely hindered by their generally limited access. The CHoral project, which is part of the CATCH program funded by the Dutch Research Council, aims to provide users of speech archives with online, instead of on-location, access to relevant fragments, instead of full documents. To meet this goal, a spoken document retrieval framework is being developed. In this paper the evaluation efforts undertaken so far to assess and improve various aspects of the framework are presented. These efforts include (i) evaluation of the automatically generated textual representations of the spoken word documents that enable word-based search, (ii) the development of measures to estimate the quality of the textual representations for use in information retrieval, and (iii) studies to establish the potential user groups of the to-be-developed technology, and the first versions of the user interface supporting online access to spoken word collections