Category: evaluation

In this publication in Digital Government: Research and Practice Julia Romberg and Tobias Escher offer a review of the computational techniques that have been used in order to support the evaluation of contributions in public participation processes. Based on a systematic literature review, they assess their performance and offer future research directions.

Abstract

Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpora and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modelling.

Key findings

• There are a number of tasks in the evaluation processes that could be supported through Natural Language Processing (NLP). Broadly speaking, these are i) detecting (near) duplicates, ii) grouping of contributions by topic and iii) analyzing the individual contributions in depth. Most of the literature in this review focused on the automated recognition and analysis of arguments, one particular aspect of the task of in-depth analysis of contribution.
• We provide a comprehensive overview of the datasets used as well as the algorithms employed and aim to assess their performance. Generally, despite promising results so far the significant advances of NLP techniques in recent years have barely been exploited in this domain.
• A particular gap is that few applications exist that would enable practitioners to easily apply NLP to their data and reap the benefits of these methods.
• The manual labelling efforts required for training machine learning models risk any efficiency gains from automation.
• We suggest a number of fruitful future research avenues, many of which draw upon the expertise of humans, for example through active learning or interactive topic modelling.

Publication

Romberg, Julia; Escher, Tobias (2023): Making Sense of Citizens’ Input through Artificial Intelligence. In: Digital Government: Research and Practice, Artikel 3603254. DOI: 10.1145/3603254.

In this publication in the Workshop on Argument Mining, Julia Romberg develops a method to incorporate human perspectivism in machine prediction. The method is tested on the task of argument concreteness in public participation contributions.

Abstract

Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important perspectives and in many cases cannot do justice to the subjective character of the tasks. One solution to this shortcoming are multi-perspective approaches, which have received very little attention in the field of argument mining so far.

In this work we present PerspectifyMe, a method to incorporate perspectivism by enriching a task with subjectivity information from the data annotation process. We exemplify our approach with the use case of classifying argument concreteness, and provide first promising results for the recently published CIMT PartEval Argument Concreteness Corpus.

Key findings

• Machine learning often assumes a single ground truth to learn from, but this does not hold for subjective tasks.
• PerspectifyMe is a simple method to incorporate perspectivism in existing machine learning workflows by complementing an aggregated label with a subjectivity score.
• An example of a subjective task is the classification of the concreteness of an argument (low, medium, high), a task whose solution can also benefit the machine-assisted evaluation of public participation processes.
• First approaches to classifying the concreteness of arguments (aggregated label) show an accuracy of 0.80 and an F1 value of 0.67.
• The subjectivity of concreteness perception (objective vs. subjective) can be predicted with an accuracy of 0.72 resp. an F1 value of 0.74.

Publication

Romberg, Julia (2022, October). Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity. In Proceedings of the 9th Workshop on Argument Mining (pp.115-125), Gyeongju, Republic of Korea. Association for Computational Linguistics. https://aclanthology.org/2022.argmining-1.11

In this publication in Electronic Government, Julia Romberg and Tobias Escher investigate the potential of active learning for reducing the manual labeling efforts in categorizing public participation contributions thematically.

Abstract

Political authorities in democratic countries regularly consult the public on specific issues but subsequently evaluating the contributions requires substantial human resources, often leading to inefficiencies and delays in the decision-making process. Among the solutions proposed is to support human analysts by thematically grouping the contributions through automated means.

While supervised machine learning would naturally lend itself to the task of classifying citizens’ proposal according to certain predefined topics, the amount of training data required is often prohibitive given the idiosyncratic nature of most public participation processes. One potential solution to minimize the amount of training data is the use of active learning. While this semi-supervised procedure has proliferated in recent years, these promising approaches have never been applied to the evaluation of participation contributions.

Therefore we utilize data from online participation processes in three German cities, provide classification baselines and subsequently assess how different active learning strategies can reduce manual labeling efforts while maintaining a good model performance. Our results show not only that supervised machine learning models can reliably classify topic categories for public participation contributions, but that active learning significantly reduces the amount of training data required. This has important implications for the practice of public participation because it dramatically cuts the time required for evaluation from which in particular processes with a larger number of contributions benefit.

Key findings

• We compare a variety of state-of-the-art approaches for text classification and active learning on a case study of three nearly identical participation processes for cycling infrastructure in the German municipalities of Bonn, Ehrenfeld (a district of Cologne) and Moers.
• We find that BERT can predict the correct topic(s) for about 77% of the cases.
• Active learning significantly reduces manual labeling efforts: it was sufficient to manually label 20% to 50% of the datasets to maintain the level of accuracy. Efficiency-improvements grow with the size of the dataset.
• At the same time, the models operate within an efficient runtime.
• We therefore hypothesize that active learning should significantly reduce human efforts in most use cases.

Publication

J. Romberg and T. Escher. Automated topic categorisation of citizens’ contributions: Reducing manual labelling efforts through active learning. In M. Janssen, C. Csáki,I. Lindgren, E. Loukis, U. Melin, G. Viale Pereira, M. P. Rodríguez Bolívar, and E. Tambouris, editors,Electronic Government, pages 369–385, Cham, 2022. SpringerInternational Publishing. ISBN 978-3-031-15086-9