E-petition popularity: Do linguistic and semantic factors matter?

Hagen, Loni; Harrison, Teresa M.; Uzuner, Özlem; May, William C.; Fake, Tim; Katragadda, Satya

doi:10.1016/j.giq.2016.07.006

Cited by 42 publications

(27 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main purpose of text mining is to elicit high-quality information by applying natural language processing and statistical learning methods [3,38]. In particular, we used frequency analysis to track the use of frequent non-common words such as nouns, which is perceived to represent the main points that an individual respondent aims to convey [3,38]. We examined the frequencies of nouns and noun phrases in a semi-automatic manner through coded data and commands (see below for the different analytical steps).…”

Section: Text Miningmentioning

confidence: 99%

“…In particular, during the preprocessing stage, we used the R package to decompose the words from the civil complaint data [3,38]. For word extraction, we used the Korean Natural Language Processing (KoNLP) program and the Sejong dictionary package from the R program [17,32,39].…”

Section: Text Miningmentioning

confidence: 99%

“…With the advent of the Fourth Industrial Revolution, interest in smart cities and big data (big data generally refers to large and complex sets of data that represent digital traces of human activities and can be defined in terms of scale or volume, analytical methods [1], or organizational effects [2]) has been increasing. In particular, the demand for science-based urban policies based on big data has grown, as has the value of big data and the possibilities of using it as an analytical tool for rational urban public policy making and evidence-based administration [3][4][5][6][7][8][9]. For example, cities around the world collect massive amounts of data related to urban life, from objects (e.g., energy infrastructure) and people external and young migrants.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

Lee

Kim

et al. 2019

Sustainability

View full text Add to dashboard Cite

Conventional studies on policy demand identification that are anchored in big data on urban residents are limited in that they mostly involve the top-down and government-oriented use of such data. It restricts treatment to specific issues (e.g., public safety and disaster management), even from the beginning of data collection. Scant research has emphasized the general use of data on civil complaints—which are independent of areas of application—in the examination of sustainable cities. In this work, we hypothesized that the analyses of civil complaint data and big data effectively identify what urban residents want from local governments with respect to a broad range of issues. We investigated policy demand using big data analytics in examining unstructured civil complaint data on safety and disaster management. We extracted major keywords associated with safety and disaster management via text mining to inquire into the relevant matters raised in the civil complaints. We also conducted a panel analysis to explore the effects exerted by the characteristics of 16 locally governed towns on residents’ policy demands regarding safety and disaster management-related complaints. The results suggest that policy needs vary according to local sociocultural characteristics such as the age, gender, and economic status of residents as well as the proportion of migrants in these localities, so that, city governments need to provide customized services. This research contributes to extend with more advanced big data analysis techniques such as text mining, and data fusion and integration. The technique allows the government to identify more specifically citizens’ policy needs.

show abstract

Section: Text Miningmentioning

confidence: 99%

Section: Text Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

Lee

Kim

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Là, encore, on peut noter un degré de sophistication et d'inventivité très différent dans l'étude de ces traces de dynamiques pétitionnaires présentes sur le Net. Certains se contentent de rapporter le nombre de signatures obtenues par chaque pétition à d'autres faits sociaux : les textes eux-mêmes pour s'interroger sur l'efficacité différentielle des contenus linguistiques (Hagen et al, 2016), les mobilisations via les réseaux sociaux en comparant appartenances à des groupes Facebook et signatures de pétitions (Panagiotopoulos et al , 2011). D'autres prennent en compte de manière plus précise les dynamiques des campagnes pétitionnaires en vue de mettre en avant la variété des formes de mobilisation (Yasseri et al, 2013) et d'en rendre compte à partir d'expérimentations psycho-sociales (Margetts et al , 2015).…”

Section: éTudier De Nouvelles Pratiques Avec D'anciennes Méthodesunclassified

Les transformations des comportements politiques au prisme de l’e-pétitionnement

Contamin¹,

Leonard²,

Soubiran³

2017

Réseaux

View full text Add to dashboard Cite

S’appuyant sur la base d’un site de pétitions en ligne relative à plus de 15 000 pétitions qui ont récolté près de 3,8 millions de signatures, cet article montre que l’usage d’une telle source permet de dépasser certaines des apories des méthodes classiques d’étude des actions de mobilisations, notamment en passant de l’étude des déclarations de comportement à l’étude des comportements réels et en pouvant travailler une variété de mobilisations, mais aussi qu’une telle étude se heurte à une difficulté commune aux travaux sur les big data issus de l’Internet : l’absence d’une collecte supervisée aux fins de la recherche qui induit un ensemble de difficultés de traitement. Il invite dès lors à dépasser ces difficultés, d’une part en jouant du recoupement des données inédites issues de la base (horodatage, présence de commentaires…), et d’autre part en éclairant ces données par d’autres méthodes classiquement utilisées en sciences sociales (entretiens, questionnaires, etc.).

show abstract

“…We know that the ability of a petition to meet its target signature threshold is heavily determined by the rate of signature accumulation during the initial day of the petition's life [18,42]. We also know that signature accumulation is related to certain linguistic and semantic aspects of the petition itself [17]. It makes sense to expect that collective action on the Internet in the form of tweets and the use of other online media will be involved in stimulating the accumulation of e-petition signatures.…”

Section: Electronic Petitioning and Signature Accumulationmentioning

confidence: 99%

E-Petitioning and Online Media

Harrison

Dumas

DePaula

et al. 2017

Proceedings of the 18th Annual International Conference on Digital Government Research

Self Cite

View full text Add to dashboard Cite

E-petitioning, a genre of technology-based collective action tools, makes it possible for members of the public to address government decision makers directly with their requests for action. In this paper we use time series analysis to explore the effects of Twitter and other forms of online media on the accumulation of signatures in e-petitioning. We explore the case of "Bring Back Our Girls," a Change.org petition initiated in spring 2014 following the abduction of 276 female students from a school in Chibok, Nigeria by heavily armed members of an Islamic extremist group. The petition targeted government leaders around the world. We found evidence that tweeting and _______________________________________________ Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. dg.o '17, June 07-09, 2017, Staten Island, NY, USA © 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-5317-5/17/06…$15.00 http://dx.doi.org/10.1145/3085228.3085320 certain forms of online media are related to the likelihood of individuals signing an e-petition, providing evidence of a hybrid media system in which diverse forms of online media behave with diverse logics and impacts in their effects on e-petitions. CCS CONCEPTS • Applied Computing~E-government Social and professional topics➝Computing/technology policy • Government technology policy

show abstract

E-petition popularity: Do linguistic and semantic factors matter?

Cited by 42 publications

References 29 publications

Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

Les transformations des comportements politiques au prisme de l’e-pétitionnement

E-Petitioning and Online Media

Contact Info

Product

Resources

About