History by Diversity: Helping Historians search News Archives

TL;DR

This paper introduces HistDiv, a novel algorithm that models historical search intent to diversify news archive results across aspects and important time periods, improving recall and user satisfaction.

Contribution

It defines the concept of Historical Query Intent and develops HistDiv, a new diversification algorithm tailored for temporal and aspect-based news archive searches.

Findings

HistDiv outperforms competitors in subtopic recall.

Users prefer HistDiv's ranking despite slight precision loss.

Temporal and aspect diversification enhances historical search effectiveness.

Abstract

Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour in these archives. While searching for articles published over time, a key preference is to retrieve documents which cover the important aspects from important points in time which is different from standard search behavior. To support this search strategy, we introduce the notion of a Historical Query Intent to explicitly model a historian's search task and define an aspect-time diversification problem over news archives. We present a novel algorithm, HistDiv, that explicitly models the aspects and important time windows based on a historian's information seeking behavior. By incorporating temporal priors based on publication times and temporal expressions, we diversify both on the aspect and temporal dimensions. We test our methods by constructing a test collection based on The New York Times Collection with a workload of 30 queries of historical intent assessed manually. We find that HistDiv outperforms all competitors in subtopic recall with a slight loss in precision. We also present results of a qualitative user study to determine wether this drop in precision is detrimental to user experience. Our results show that users still preferred HistDiv's ranking.