Enabling discourse representation and meta-consensus in online ...

Enabling discourse representation and meta-consensus in online deliberation using Internet technologies

Authors Vanessa Liston

Clodagh Harris

Deirdre Lee

Trinity College Dublin [email protected]

University College Cork [email protected]

Digital Enterprise Research (DERI) Galway [email protected]

Brian Davis

Mark O’Toole

Digital Enterprise Research Centre (DERI), Galway [email protected]

Kilkenny County Council [email protected]

Paper prepared for the annual conference of the Political Studies Association, Belfast, 3-4 April 2012

Work in progress: Please do not cite without authors’ permission.

Abstract Deliberative democratic innovations, such as citizen assemblies and participatory budgeting, aim to move beyond aggregation of individual preferences to engage citizens in preference transformation and collaborative decision making.

However the problem of scale persists in attempts to reach

deliberative processes beyond discrete events. Online communication tools offer the potential to overcome this barrier, as they are automated, scalable and distributed. However, as of yet, these tools are 1) not designed for inclusive deliberation and 2) lack the capacity to support incentivised and structured online deliberation. Most collaboration platforms lead to huge amounts of unstructured, natural language comments on a wide variety of topics, which have not yet been harnessed towards the normative aims of citizen deliberation. In this paper we present solutions to these two problems in the design of an online deliberative environment. First, we base the design on the propositions of discourse representation and metaconsensus by John Dryzek that addresses the issue of scale and inclusion. Second, we present a technical solution using Semantic Web technologies to enable the automated extraction of knowledge from online deliberation, which can be customised for a particular domain or particular types of discussions. The extracted knowledge structure supports measurement of discourse diversity and meta-consensus processes in policy proposal deliberations.

1. Introduction A cornerstone of the discourse of sustainable society is citizen participation, a principle repeated in the national strategies and development plans of European governments. In the past decade citizen consultation was viewed as enabling ownership and legitimacy of the political process hence supporting the effectiveness of policies targeting a sustainable future (Crozier, 2008; OECD, 2003). Today, in the context of the challenges of systemic crises and the opportunities for mass public engagement, this view has evolved to the recognition of citizens as key actors in producing collective intelligence to address complex social challenges.

As a result, collaborative innovations are

increasing, specifically with the aim of harnessing the collective knowledge, data and intelligence of many citizens towards social ends. New instances of such collective social engagement are emerging in the fields of citizen geo-tagging, tweeting for emergency services and climate warning processes. However, deployments of social media in the political sphere have been less extensive. On the one hand, citizens express a general distrust of political players and institutions of democratic representation as evidenced in the findings of Eurobarometer 75. These show that public confidence in national institutions is low, 32% of citizens in the EU trust their Government and 33% their Parliament (Eurobarometer, 2011). New forms of online political engagement are emerging that cross traditional political boundaries on which the notion of constituency and democratic representation

depend. Yet, some research has shown the potential in social media for transforming local political policy development. Bakker & de Vreese (2011), show how the use of certain types of new media is shown to increase political participation among the young. On the other hand, political actors and institutions generally are not knowledgeable on how to adapt representative institutions to such technology enabled developments in citizen behavior in a way that is inclusive and representative, thus leading to a low rate of innovation in this area. In addressing this gap, a new literature is emerging on civic experiments designed to include large scale citizens in policy deliberations. Landoli, Klein and Zollo (2007) outline a design for collaborative deliberation based on an argumentation approach. Miori and Russi (2011) demonstrate a model for integrating online and offline participatory budgeting in Municipio XI of Rome. Gordon and Manosevitch (2010) use the online virtual world Second Life to enable ‘augmented’ community deliberation on the planning of a park in Boston, Massachusetts. Despite these advances, online citizen engagement in the deliberative processes required for sustainable policies and social cohesion face a number of challenges. The most critical of these is political legitimacy.

Although online spaces provide a unique opportunity for integrating citizen

deliberation to the policy process on an on-going basis, they also provide a unique set of opportunities and constraints for legitimacy that have not been sufficiently theorised in the context of normative democratic theory. Concerns have been expressed that the use of the internet for policy making can reinforce existing inequalities and the dominance of the technologically elite (Albrecht, 2006; Zhang 2010). Other concerns include the impact of virtual identities on participant sincerity (Dahlberg, 2001a). Authors also point to polarization and hemophilic behavioural patterns all of which mitigate opportunities for inclusive policy making as shown in the case of the Obama Briefing book. The SOWIT (Social Web for Inclusive and Transparent Democracy) model addresses these questions by building on empirical findings on citizen deliberative policy behaviour and the theoretical innovations of Iris Young and John Dryzek on the politics of difference (2000) and discourse representation respectively. We show how a new concept of discursive representation, supported by meta-consensus procedures can enable more inclusive and effective citizen participation. Metaconsensus processes for structuring deliberations can stimulate constructive policy engagement between strongly opposing groups as it does not necessitate agreement on outcomes (Dryzek & Niemeyer, 2007). A necessary element of this representative approach is the grounding of deliberations in a representative random sample of social discourses. While SOWIT is a technically enabled deliberative process its elements can equally be applied in ‘real’ or ‘hybrid’ deliberative settings where SOWIT automated meta-consensus measures are deployed independently for technical support but not act as the communicative medium. This is particularly important in response to the current issue of the digital divide (O’Hanrahan, Irish Times, 2012) The paper is structured as follows. We begin in section two with a general overview of the theoretical framework of the SOWIT model. We then present the design for the e-deliberative forum in section three. In the fourth section we explain how the design can be considered representative and deal with challenges such as self-selection that currently pose an obstacle to e-deliberation. Finally, in

section 5 we present the technological aspects under development and conclude with an outline of next steps.

2. Theoretical framework 2.1

Definition of deliberation First articulated as theory of democratic legitimacy, deliberative politics has moved to the forefront

of political theory in recent decades. Yet, despite agreement on its normative ideals of supporting inclusion, equality and reasonableness (Mansbridge at al., 2010; Steiner, 2010; Held, 2006), there is no commonly agreed definition of deliberative democracy (Steiner, 2010; Mutz, 2008; Niemeyer & Dryzek, 2007). Noting this difficulty and warning that proliferation of the term can lead to ‘concept stretching’ (Steiner, 2008) Bächtiger et al. (2010, p. 33) distinguish between two main types of deliberation. Type I is rooted in Habermasian communicative rationality defined as rational discourse, deliberative intent and the distinction between communicative and strategic action. Type II emphasises deliberative institutions and outcomes, is grounded in the empirical realities of situated deliberation and involves more flexible forms of discourse. Carpini et al. (2004) distinguish between “public deliberation,” “discursive participation” and alternative ways in which citizens can voice their individual and collective views on public issues. Thompson (2008) calls for “a clearer conception of the elements of deliberation, the conflicts among those elements, and the structural relationships in deliberative systems.” It is precisely the contested nature of deliberation that lends this experiment creative space in which to imagine possibilities for the realisation of the normative ideals thereby enabling new understandings of deliberative democracy. As such we frame this paper within a broad definition of deliberation, drawing specifically on Niemeyer and Dryzek (2007) and Dryzek’s (2010) conceptual framework of meta-consensus and inter-subjective rationality. Deliberation, for the purposes of this experiment, is understood as a process by which citizens engage in reflective discussion on policy issues within an institutional context that is intended to have an indirect impact on policy by aiming towards free and reasoned meta-consensus among citizens.

2.2

From participation to deliberation In theorizing the challenges of citizen participation Young (2000) offers convincing arguments

that current forms of citizen participation in policy making have limited effectiveness because: 1. The premise of consensus towards a ‘common good’ is flawed and; 2. An ideal deliberative environment does not exist but is defined by structural inequality and cultural difference. She proposes instead that as groups derive from relationally constituted structural differentiations, participation should be reinterpreted away from notions of the common good towards harnessing as a resource, differences in socially situated interests, proposals, claims and expressions. Inclusion, she argues, enables information spread across social groups leading to social knowledge of other positions, and prevents policy processes being dominated by certain groups to the exclusion of minorities thereby supporting the potential of sustainable policy outputs.

Extending this resource perspective on social difference, Dryzek argues that deliberative processes can be more inclusive where diverse social discourses are explicity represented and where deliberative processes are structured according to a meta-consensus process. According to Dryzek discourse is ‘a set of categories and concepts embodying specific assumptions, judgements, contentions, dispositions, and capabilities’ (2008). Philips and Jorgensen broaden this understanding to “a particular way of talking about and understanding the world.” (2002). Inherent in our use of the concept of discourse is the principle that discourses are multiple, are expressed relative to specific issues and evolve and change over time. Discourse representation therefore involves representation of an array of discourses relevant to a specific policy issue at hand at a particular point in time. It differs from individual representation as each person subscribes to number of discourses which are activated in different contexts. Through discursive representation, Dryzek argues that the issue of deliberative scale can be overcome to a global level (see Dryzek, 2010). Dryzek’s concept of meta-consensus is particularly suited to policy development in contexts of social diversity and lines of conflict, as it involves agreement on the domain of reasons and considerations pertaining to the issue at hand as well as the nature of the choices to be made. It does not require agreement on outcomes. Indeed, where non-common values constitute the deliberative process, “acceptable outcomes can still be reached by reframing consensus on epistemic, normative and empirical dimensions” (Niemeyer & Dryzek (2007). Normative consensus describes the extent to which discourses and their values are recognized and accepted as legitimate in the process of the cooperative search for mutually acceptable solutions even where such value differences remain irreconcilable; Epistemic consensus refers to which facts and beliefs are accepted as credible and thus attention can be devoted to problem solving; Preference consensus refers to the degree of agreement on a) the dimensions along which choice can be structured or b) on the number of options available Dyrzek (2010). Niemeyer & Dryzek have shown its effectiveness in structuring deliberation on health policy for the gay community in California among anti-gay groups and gay rights movements Niemeyer & Dryzek (2007). A discourse perspective for scalable e-deliberation is particularly innovative because of its potential to democratise the wider public sphere thereby avoiding hegemony of particular discourses (van Dijk, 1993). Van Dijk argues that management of discourse access presents a ´crucial social dimension of dominance´. The current economic crisis highlights the benefit for a space for democratic and equal expression and deliberation of competing discourses.

3. SOWIT Model Adopting these perspectives, the SOWIT model aims to develop new understandings of citizen participation as a process of recursive citizen and representative deliberations, the content and structural composition of which feed into decision-processes within local government. The SOWIT model enables inter-group communication, networking and deliberation by local government and citizens and comprises three spheres:

a) A Public sphere which enables open cross-group and local authority communications; where issues are discussed, submissions are collaboratively created and where individuals are incentivized via a gaming mechanism to develop political skills and influence. b) A Deliberative sphere where discourse advocates emerging from the public sphere deliberate on a proposal/policy draft in a way that is representative of social discourses. c) A Decision sphere which represents Council officials and elected representatives and where final policy is produced. The output of the decision sphere is measured against the citizens’ deliberative draft. Where there are differences, the Council may provide an explanation to the public sphere. In this section we briefly outline the model’s components before we turn our attention to explaining how this scheme attends to the challenges of political legitimacy. For a full theoretical explanation of the model see (author self-reference).

3.1

Public Sphere: Knowledge based social network

There are three primary objectives of the public sphere: First; it aims to enable broad inclusion of citizens in the policy development process, by creating a context in which discourses can be formed and articulated and the legitimacy of other discourses accepted - a necessary condition for deliberation (Habermas, 1984). Second: it enables cross-group social learning by providing a view of other discourses and citizen sentiment across time. Third; the public sphere develops as a learning environment over time which informs the policy development process. The public sphere has open membership and includes the presence of elected representatives. Using a game mechanic, members are incentivized to develop deliberation skills within their discourse as set out in Steiner et al.’s Discourse Quality Index (2004) (e.g. respectful discussion, willingness to change one’s mind). Such measurable evidence of deliberative capacity is salient for election as potential discourse speakers for the deliberation sphere.

3.2

Deliberative Sphere and Meta-consensus The deliberative sphere is a feature that is enabled in addition to the public sphere when policy

development is at stake or a political issue is open for consultation. It represents an informal chamber of discourse as conceptualised by Dryzek (2010). The aim of the deliberative sphere is to produce a deliberated proposal on an issue/policy that reflects the diverse discourses in the public sphere. Council officials are required to input all the relevant data, observe and support the deliberation process. This output is submitted to Council as input to the policy development process. The citizens’ draft is scored against the final policy output and where there is significant divergence Council explains divergences within the public sphere. The deliberative sphere only proceeds when relevant discourses to the policy draft in question or where there is sufficient discursive diversity on a general issue. The challenge of the deliberative sphere is to reconcile thought diversity with movement towards a level of meta-consensus that can be brought to bear on the policy negotiations in Council. Because discourses are strong compelling narratives, regular consensus processes are unsuited for

the process of inclusive policy making as it can either lead to discourse dissipation or groups can remain with irreconcilable conflicts at a fundamental level. A wide literature also points to problems such as polarization, discourse dissipation, cascade effects of contributions, and the withholding of information that can occur during deliberation (Witschge, 2004; Landoli et. al., 2007). Approaching consensus by reframing its objective and disaggregating it into component parts (meta-consensus) can provide a mechanism for structuring deliberations and stimulating creative policy proposals. Therefore, to support inter-group dialogue towards policy solutions that attend to the various concerns of the represented discourses, a meta-consensus deliberation structure is implemented in the deliberative sphere. Members’ discussion stream is analysed and compared against other discussion streams for ‘distance’ along each of the three dimensions of meta-consensus using Qmethdology. The distance between deliberating discourse pairs we term discourse resonance and the aim of deliberation is to narrow the distance between all deliberating partners by scoring a discourse resonance index. This proposed measure as an implementation of Q-method factor analysis describes the extent to which discourses resonate with each other along the three dimensions of metaconsensus set out above. Figure 1 outlines the SOWIT e-deliberation process.

Figure 1: SOWIT e-deliberation process

3.3

Policy Development Sphere - Council

The policy development space is where policy is officially formed. Outputs from the public and dialogue sphere are provided to the local Council. Through engagement with the deliberative sphere, representatives are better informed, not only to the preferences in their constituency and beyond but also to the rationales for those discourses. Because of the visibility of the deliberative spheres, this brings added accountability on the part of officials and representatives to ensure as far as possible that final policy output resonates with the discourses of the public sphere. The score of similarity with the citizens deliberated document acts as a direct measure of political system responsiveness. Figure 2 below illustrates how the SOWIT process integrates with current policy processes.

Figure 2: Integration of e-deliberation to policy development process

Proposed SOWIT policy process Preliminary consultation

Public Consultation

Draft compiled

Manager’s report

Revised policy

SOWIT Public Sphere

SOWIT Public Sphere

SOWIT Deliberation Sphere

Council

Feedback

SOWIT Public Sphere

4. The question of legitimacy Having described the model briefly we now turn to an explanation of how this approach can address those issues most relevant to enabling citizen e-deliberation on public policy.

4.1

The issue of random sampling A core challenge facing online deliberative forums is the extent to which the output of

deliberations can legitimately be regarded as representing citizens’ views. Citizen assemblies and deliberative polls overcome this issue through random sampling of citizens (e.g. ‘We the Citizens’ Ireland; G1000, Belgium; Citizens Assembly Ontario). The assumption is made that the observed population is representative of the entire population (Smith, 2006). However a number of authors have critiqued the assumptions on which the random sampling method depends for legitimacy. Davies, Blackstock and Rauschmayer (2005) argue that the assumption that individuals hold perspectives attributed to them by their structural group characteristics has not been tested and does not necessarily hold. They specifically identify a `recruitment problem', `composition problem', and `mandate problem' with the sampling method and call for a focus on argument representation instead of individuals. We add to this analysis that random sampling of individuals reproduces a specific conception of representation that does not account for the fact that citizens occupy multiple discourses which are activated in different contexts (e.g. feminism, environmentalism etc.). Indeed, deliberative processes to date have not analyzed the extent to which selected individuals represent the landscape of discourses that characterize competition and conflict within the given society. The importance of the question is summed up by Philips and Jørgensen (2002) who state: “Changes in discourse are the means by which the social world are changed. Struggles at the discursive level take part in changing as well as reproducing the social reality”. Indeed, analysis of the distribution and contestation of discourses within citizens deliberations is strikingly absent from relevant evaluations and publications. Random sampling of citizens is thus is not suitable as a basis for political legitimacy within SOWIT which is founded on discourse representation. As such an explicit process for identifying discourses and estimating the representativeness of the discourse deliberation process is essential. For this reason we employ Q-methodology as suggested by Dryzek (2009) and recently emerging in the literature (Ellis, Barry and Robinson, 2007; others) including that on deliberative experiments. The Q –method is particularly relevant as it is oriented towards including marginalized voices.

4.2

Discursive Representation using Q-method The Q-method has four key stages. In the first stage, the researcher creates a ‘concourse of

communicatability’ (Brown, 2006) of a wide variety of statements within a particular policy area or domain of inquiry. The concourse is drawn from semi-structured interviews and statements in the media, written reports etc. with the aim of maximum diversity in perspectives and policy positions. In the second stage, these are reduced to a manageable number in a process known as the Q-sort by identified respondents. Respondents are selected using purposive sampling and the statements are sorted into a semi-normal distribution. The third stage requires finding structure in the Q-sorts using correlation and factor analysis. The final stage requires researchers to interpret the factors and validate the outcomes using semi-structured interviews with respondents that have similar factor loadings. Each factor is then categorized as a ‘discourse’ though as Brannstrom (2011) points out ‘Naming and interpreting the factors remains “the most problematic phase” of Q-method’.

Q-methodology is valuable in enabling the inclusion of marginalized discourses. However, as Dryzek and Niemeyer (2008) note, discursive representation should be a political process and not a social science process. To this end, we explain how Q-method is used within SOWIT as a political process for inclusive deliberations.

4.2.1 Concourse Identification To support maximum diversity in the perspectives to be considered for deliberations, SOWIT augments the Q-method of interviews with purposively sampled stakeholders and print media reports by also scanning online ‘communicability streams’ on internet blogs, discussion forums and newspapers tagged with geographic data. This provides a base to which further statements are added using interviews and local print statements. The SOWIT public sphere will enable citizens to add statements which they feel are missing and to ‘link’ them to statements that are close in meaning. From this concourse, a smaller set of statements will be generated by SOWIT using three methods for triangulation: researcher observation, a diversity index measure and citizen evaluation. The aim of this process is to maximize diversity and comprehensiveness of the concourse which will be quantitatively (diversity index) and qualitatively validated (researchers and citizens). The diversity index also serves to eliminate redundancy where it exists in the concourse. In the public sphere citizens can comment on the consolidated list of statements for the Q-sort. The diversity measurement process is visualized and explained. The role of the SOWIT public sphere in this stage of the Q-method is to 

Provide the space for individuals to have their statements included and to learn of other discourses so that new discourse statements can be generated



For people to learn/approve the process stages as well as critique and improve it.

4.2.2 From Perspectives to Discourses: Q-Sort This stage requires the selection of people to sort these statements into discourses for representation in the deliberation sphere. The SOWIT public sphere enables citizens to interactively conduct the Q-sort. This process is supported by a purposive sample of off-line participants to mitigate self-selection effects and maximize the likely range of discourses that can emerge on the policy issue. The sorted statements are analysed using factor analysis for clusters. The outcome is validated by semi-structured interview with participants that load highly on that factor or discourse. This sampling process will be specifically targeted at marginalized groups who are not online and who are excluded from the political process. Those that sort the statements to enable discourse patterns to emerge are not necessarily those that are selected to represent discourses although they could be. 

The role of the SOWIT public sphere is to enable people to interactively fill out the Q-sort (which is backed up by gender/social and economic data and their network data.)



All citizens given a chance to align themselves with a discourse / range of discourses

4.2.3. Selecting Discourse Representatives Criteria Once the range of discourses is indentified from the Q-sort, the key political issue is how to select representatives of those discourses for the deliberation sphere. This process is fundamental to ensuring inclusion, quality deliberation as well as legitimacy of the deliberative process. One criteria proposed by Dryzek & Niemeyer is that discourse representatives should load highly on the discourse ‘factor’. They also note that an individual can load on multiple discourses which mitigates selection for extremism (2008).

We support this approach but note that although a

person is strongly aligned with a particular indentified discourse it does not necessarily follow that he/she will make an effective discourse representative. In other words representatives may well be strongly aligned to a discourse but not on average reflect the socio-structural aspects that can influence the pattern of discourse transformation during deliberation. Hence, discursive representation must ensure inclusion of the social structure element to avoid elite gate/keeping of the deliberative process. We propose therefore that discourses are represented by two representatives selected for their diversity on another relevant demographic variable identified by participants. While one criterion is identifying with a discourse (or multiple relevant discourses) a further important criterion is deliberative capacity. The role of a discourse representative is significant in that they are invested with the authority to negotiate towards a policy proposal during which discourse transformation is likely to take place. Yet, Habermasian models of ideal communicative action have been argued to be exclusionary (Young, 2000). We have shown elsewhere that the public sphere enables all viewpoints on an issue and all types of communication. We address this issue by tying direct deliberation to a feedback loop where deliberation induced transformation is discussed by adherents to the discourse following stage 1 deliberation in the public sphere and where these inform the final stage 2 deliberations in the deliberative sphere. This approach integrates Bachtiger et al. (2010) sequential approach as well as Dryzek and Niemeyer’s (2008) concept of discursive accountability. This holds that represented discourses will transform during the course of deliberations but that these shifts should be explicable to discourse holders in terms relevant to that discourse. Deliberative capacity is thus intended not as an exclusionary element in SOWIT but a citizenship skill that is incentivized through the SOWIT system so that all citizens can develop the competence to effectively engage in discursive deliberation.

Selection Discourse ‘speakers’ are elected. There are two steps to establishing the range of candidate choice: 1) random sampling for citizens who score high single/multiple discourses loadings and who are willing to deliberate and 2) candidature registration. During the selection process the Q-sort of all candidates is available on the issue as well as evidence of their deliberative willingness. This capacity is particularly important because as

Niemeyer notes (2004), “Without open-mindedness and active use of judgmental capacities the deliberative process remains amenable to manipulation, even in information-rich environments.” Thus, SOWIT public sphere participants will have scores that indicate their capacity for deliberation and inter-group dialogue. Non-SOWIT participants will not have this added information but will provide other sources of evidence of their deliberative capacity. Citizens vote for two diverse discourse representatives for each discourse. In this voting process, deliberative capacity may or may not be as salient for voters, as discursive credibility of the candidate. However, it is likely to be an added advantage and incentivize ordinary citizens to develop these skills through the SOWIT game mechanic political influence mechanism. Candidates for selection (do we mean election - yes) for each discourse belong to a temporary discourse articulation circle, where the candidates for each discourse articulate a joint written discourse for the deliberation sphere. This written submission acts as a reference point for the two elected candidates for the deliberation sphere.

4.2.4. e-deliberation and Inclusion The deliberative sphere includes elected representatives. This aspect of the political legitimacy is fundamental. Elected representatives will also engage in the Q–sort and deliberations. The deliberation process, which is facilitated, informs Councillors in their later debates in Council chamber. It should be noted that the number of representatives in the dialogue will be determined by the number of discourses identified from the Q-sort. We conclude that Q-method needs to suit discursive ends, specifically with respect to mainstreaming inclusion through the methodology. Thus while the concourse aspect requires identification of stakeholdersi, it is important that excluded voices and not just those active/perceived to be affected are included. We argue that our above approach specifically attends to Q-method as a political process and as a strong enabler of inclusion. As Dryzek and Niemeyer (2008) state: “Representation of marginal discourses is especially important from the point of view of democratic equality to the degree dominant discourses embody privilege and power”. The public sphere in this stage acts as a space in which citizens can put themselves forward as discourse representatives and where they can build their profiles on deliberation and political influence to add to their candidature for representing a particular issue. It also acts as a space where people can select the candidates and where they are present to observe the deliberations and participate in the feedback cycle with deliberators.

5. Harnessing Internet Technologies 5.1

Information Extraction for enabling Concourse Mapping in Q-method

In this section we explain how existing knowledge on the Web could be used to support the sampling of discourses which need to be present in the deliberation sphere. This requires identification of

statements of opinion on a policy issue across Web and media spheres. The most common method for establishing the concourse is personal interviews supplemented by print and online news sources (Branstromm, 2011). For SOWIT we will harness conversations within the SOWIT public sphere, and, using state-of-the-art technology, augment this set by identifying statements on the policy issue in the wider Web. These statements will be used in addition to interview based data from purposive stakeholder sampling in order to generate a structured Q-sample. To this end, statements can be retrieved from a myriad of online sources: from the Web in general, from blogs, from social media, or from a specific subset of these. This can be decided on within the context of the public sphere, depending on the required characteristics of the statements, for example, should they be pertaining to a particular location, from an official source, from a qualified person, etc. Information Extraction techniques can be applied to extract structured information from unstructured or natural-language text (Maynard & Funk, 2011). Named Entity Recognition (NER), a type of Information Extraction, could be utilized to build a concourse of statements from unstructured information available on the Web. NER involves identifying particular entities in unstructured text, based on target phrases, synonyms of the target phrases and related phrases. A target phrase may include one or more keyword. For example, if a concourse on the topic of ‘wind farms’ was required, NER would be performed on the corpus (source information) to identify sentences with the target phrase ‘windfarms’, but also with variations of the target phrase from a predefined dictionary, e.g. ‘windfarm’, ‘wind farm, ‘turbine’, ‘windmill’, ‘environment’, ‘green’, ‘energy’, etc. If there were further restraints on the statements required, for example, only statements about windfarms in Ireland are valid, additional terms could be added to the dictionary. Alternatively, when dealing with Web data, much of the time metadata about the unstructured information is available. A website will be associated with a domain and IP address that is indicative of its location. A blog-post will have a creator, a creation date. Social media profiles often have personal information associated with them, such as location, date-of-birth and workplace. Microblogs, such as Twitter, often have geo-tags associated with them by default. All of this metadata can be exploited to narrow down the resultant sentences to only those that are relevant to the policy topic. Once a list of sentences have been identified that refer to the policy topic, they are pruned, so that only those that are valid q-method statements remain. For example, a sentence may refer to the policy topic, but it may only contain factual information about the topic and not an opinion that can be included in a discourse. The pruning of sentences to valid discourse statements can be achieved using Sentiment Analysis. Sentiment Analysis, or Opinion Mining, extracts opinions from the source information by examining the language used by the author (Liu and Zang, 2012). Emotive or expressive phrases can be indicative that an opinion is being expressed. However care must be taken as in informal Web discussions, slang and shorthand may be often used. Also, the context of the target phrases must be examined to ensure the correct sentiment is assigned. For example, take the statement ‘I am delighted the new law on windfarms was passed’. Although ‘delighted’ is generally a positive sentiment, the sentiment in this statement actually depends on whether the law that was passed was pro- or anti- windfarms.

5.1.3 Proof of Concept In order to investigate the potential of Information Extraction (IE), and in particular Sentiment Analysis, 1

a simple IE Pipeline in IBM LanguageWare Resource Workbench was built (Bontcheva et al., 2009). This pipeline aimed to detect and classify q-statements from unstructured sources, for example from Twitter.

Figure 3: Screenshot from the IBM LanguageWare Resource Workbench

In the IE Pipeline, each statement from the dataset is converted into an annotated candidate statement for analysis. The annotated statement is then parsed according to predefined rules to see if it (a) is about the desired topic, (b) has a positive or negative polarity and (c) has an associated type. For example, take the statement ‘Windmills have no effect on the climate’. According to the parsing rules the following elements of the statement are identified as follows: 

‘Windmills’ – is a topic (target object)



‘no effect’ – has a polarity of -1 (negative)



‘climate’ – is an environment trigger phrase

This in turn leads to the following annotation, as can be seen in

Figure 4:

1



Topic = windfarms



Polarity = -1



Type = Functional

http://www-01.ibm.com/software/globalization/topics/languageware/

Figure 4: Example Annotation of Type Q-Statement in IBM LanguageWare with relevant features

Figure 5: A dictionary entry in sentiment lexicon (inflections generated automatically)

In order to create the parsing rules with IBM LanguageWare, the following process was followed: 

Create domain specific dictionaries for trigger phrases/terms of topics/ government organisations/ environmental trigger phrases



Create a sentiment lexicon of adjectives and trigger phrases associated with positive and negative sentiment.



Write Shallow Parsing rules which match annotations generated from dictionary lookup, taking into account general linguistic context such as part of speech tags, number of tokens and end of sentence boundaries. Figure 5 shows an example of a dictionary entry in a semantic lexicon that contains trigger

phrases associated with negative sentiment.

Figure 6: Pattern matching over annotations 6 shows the annotation of a statement using the parsing rules of the IE pipeline.

Figure 6: Pattern matching over annotations

5.2

Scoring and reducing statements for maximum diversity The aim of Q-method is for maximum variety in the ‘concourse of communicability´ (Brown,

2006). Thus the concourse should identify those expressions that are online as well as those that are offline. Offline statements will be gathered through interviews with a purposive sample of respondents that are relevant to the policy issue at hand (selected for socio-economic and gender diversity). This process includes measuring all off and online statements for diversity so that there is an objective process for narrowing down the wide range of statements to a manageable number which should not exceed 60. Due to the issues outlined in section 5.1, estimating diversity of statements is a complex issue. Sentiment Analysis aims to determine the polarity of a statement, however determining the diversity between statements is a further step. One approach is to determine the sentiment position of a statement on a continuous scale from agree to disagree. This could be done in an automated way, however it would require a lot of pre-

processing, rule definition and training. Its success would also lie on the consistency of the data; if the data was dynamic or messy, the automatic analysis would have to be retrained. Another approach to determining the similarity of any two statements is to again use NER. Similar to how the statements were originally identified; common statements could be recognized through their inclusion of similar target phrases. New approaches are constantly being developed and decisions on the approach will be informed by both technical criteria as well those emanating from theoretical innovations in deliberative democracy.

Conclusion In this paper we have presented a model for deliberation which aims to overcome issues of inclusion and political legitimacy by rooting the model in the representation of discourses rather than individual preferences as suggested by Dryzek and Niemeyer. The model thus builds on the most recent developments in democratic theory, specifically those that take a constitutive view of the role of representation and a substantive view of democracy. To demonstrate its potential for e-deliberations we have outlined a proposed e-model that harnesses technology specific capacities for incentivizing deliberative skills and co-operative behavior. These are; game mechanics on which a new literature is dedicated with respect to its influence on behavior; and the process of dynamically extracting statements from deliberation sphere and making them available for dynamic Q-sorting to support the goal of meta-consensus. We have also linked into the most recent developments in internet data extraction technologies to show that the technology required to support comprehensive and automatic extraction of Q-statements from real speech online are being developed. Our initial results show promise for future capacity in this area. The SOWIT model outlined here is a first step in a larger research programme. It is currently being developed in consultation with a local authority and its citizens in Ireland. Next steps include collaborative validation of the model; research on the factors affecting the adoption of such an innovation (e.g. Susanto and Goodwin, 2010); factors affecting strategic deliberative behaviour, incentives, and system buoyancy and technical design. It is our view that, given due attention to incentives and context, the deliberative and discourse resonance approach of this model is relevant to contentious contexts for achieving decisive and sustainable deliberative outcomes.

References Albrecht, S., 2006. Whose voice is heard in online deliberation? A study of participation and representation in political debates on the internet. Information, Communication & Society, Volume 9 pp 62-82. Amgoud, L., Belabbes, S., and Prade, H., 2005. Towards a formal framework for the search of a consensus between autonomous agents, Proceedings of the fourth international joint conference on Autonomous agents and Multiagent Systems, ACM :New York Amgoud, L., Yannis, D., Moraitis, P., 2007. A unified and general framework for argumentation-based negotiation, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, ACM :New York Bakker, T. P., and de Vreese, C.H., 2011. Good News for the Future? Young People, Internet Use, and Political Participation, Communication Research, Volume 38, pp 451-470. Bontcheva, K., Davis, B., Funk, A., Li, Y., Wang, T 2009. Human Language Technologies. In John Davies, Marko Grobelnik, Dunja Mladenic (ed.), Semantic Knowledge Management: Integrating Ontology Management, Knowledge Discovery and Human Language Technology. Springer, 2009. Brannstrom, C. 2011. A Q-Method Analysis of Environmental Governance Discourses in Brazil's Northeastern Soy Frontier, The Professional Geographer, Volume 63, Number 4, Caminada, M, G. Pigozzi, and M. Podlaszewski (2011), Manipulation in group argument evaluation, Proceedings of the Twenty Second International Joint Conference on Artificial Intelligence, Catalonia, Spain Caminada, M., and Y. Wu (2011) On the Limitations of Abstract Argumentation, Proceedings of the 23rd Benelux Conference on Artificial Intelligence, Gent, Belgium Crozier, M., 2008. Listening, Learning, Steering: New Governance, Communication and Interactive Policy Formation, Policy and Politics, Volume 36 Issue 1 pp 3-19 Dahlberg, L., 2001a. Computer-Mediated Communication and the public sphere: a critical analysis. Journal of Computer Mediated Communication, Volume 7 pp 1-17. Dovi, S., 2007. The Good Representative Oxford: Blackwell Publishing. Dryzek, J. S., 2010. Foundations and Frontiers of Deliberative Governance, Oxford: Oxford University Press. Dryzek, J. S. & Niemeyer, S. J., 2006. Reconciling Pluralism and Consensus as Political Ideals. American Journal of Political Science, Volume 50 pp 634–649. Ellis, G., Barry, J., and Robinson C. 2007. Many ways to say ‘no’, different ways to say ‘yes’: Applying Q-Methodology to understand public acceptance of wind farm proposals, Journal of Environmental Planning and Management, Vol. 50, No. 4, pp 517 – 551

Eurobarometer 75, (Spring 2011). Public Opinion in the EU, European Commission, DG Communication, Brussels Everett, C., 2011. Gamification for the public good, Guardian, [online] 27 June, Available at: . Accessed: [12 January 2012] Gordon, E., & Manosevitch, E., 2010. Augmented deliberation: Merging physical and virtual interaction to engage communities in urban planning. New Media and Society, Volume 13 pp 75–95. Habermas, J., 1984. Theory of Communicative Action. Boston, MA: Beacon Press. Landoli, L., Klein, M., and Zollo, G., 2007. Can We Exploit Collective Intelligence for Collaborative Deliberation? The Case of the Climate Change, MIT Sloan School Working Paper No. 4675-08 Kriplean, T., Toomim, M., Morgan, J.T., Borning, A. and Ko, A.J., 2011. Reflect: Supporting Active Listening and Grounding on the Web through Restatement, Proceedings of the Conference on Computer Supported Cooperative Work, Hangzhou, China Liu, B., 2010. Sentiment Analysis and Subjectivity, To appear in: N. Indurkhya and F. J.Damerau, eds. 2011.Handbook of Natural Language Processing, 2nd Edition, Chapman & Hall / CRC Press Liu, B. and Zhang, L. 2012: A Survey of Opinion Mining and Sentiment Analysis. Mining Text Data 415-463 Maynard, D. and Funk, A 2011. Automatic Detection of Political Opinions in Tweets. ESWC Workshops, 88-99 McGonigal, J., 2010. Gaming can make a better world, TED talk, [online] Available at: Accessed: [12 Jan 2012] Miori, V and Russo, D. (2011) “Integrating Online and Traditional Involvement in Participatory Budgeting” Electronic Journal of e-Government Volume 9 Issue 1 pp. 41 – 57, available online at www.ejeg.com Niemeyer, S. 2004. Deliberation in the Wilderness: Displacing Symbolic Politics, Environmental Politics, Vol.13 No.2 pp 347 – 372 OECD, 2003. Open government, Paris: OECD. th

th

O’Hanrahan, B. Excluded by not being ‘online’. Irish Times, Feb 20 2012. Accessed Feb 26 at: http://www.irishtimes.com/newspaper/letters/2012/0220/1224312050350.html Philips, L. and Jørgensen, M.W., 2002. Discourse Analysis as Theory and Method, London: Sage Publications Reeves, B.and Read, J.L., 2009. Total Engagement: Using Games and Virtual Worlds to Change the Way People Work and Businesses Compete, Mass: Harvard Business Press Schneider, J., Groza, T., Passant, A., 2011. A Review of Argumentation for the Social

Semantic Web. Semantic Web – Interoperability, Usability, Applicability (Accept with major revisions) Susanto, T. D. and Goodwin, R., 2010. Factors Influencing Citizen Adoption of SMS Based e-Government Services, Electronic Journal of e-Government Volume 8 Issue 1 2010 pp55 –71, available online at www.ejeg.com Steiner, J., Bachtiger, A., Sporndli M. and Steenbergen M. R., 2004. Deliberative Politics in Action, Analysing Parliamentary Discourse, Cambridge: Cambridge University Press Tan, C., Lillian L.,, Tang, J., Jiang, L., Zhou, M., Li, P., 2011. User-level sentiment analysis incorporating social networks, Published in Proceedings of the 17th ACM SIGKDD international conference on Knowledge Discovery and Data-mining Urbinati, N., 2000. Representation as Advocacy: A Study of Democratic Deliberation, Political Theory, Volume 28 No 6 pp 758 - 786 Witchsge, T., 2004. Online Deliberation: Possibilities of the Internet for Deliberative Democracy. In P.M. Shane, ed. Democracy Online: The Prospects for Political Renewal Through the Internet, New York: Routledge Van Dijk, T., 1993. Principles of Critical Discourse Analysis Discourse and Society Volumen 4 Issue 2 pp 249-283 Zamir, N., and Oxley, A., 2011. Building a Corpus-Derived Gazetteer for Named Entity Recognition, Communications in Computer and Information Science Volume 180 Part 1 pp 7380 Zhang, W., 2010. Technical capital and participatory inequality in e-deliberation, Information, Communication & Society, Volume 13 pp 1019-1039 Žižka, J., Dařena, F., 2010. ‘Automatic Sentiment Analysis Using the Textual Pattern Content’ Similarity in Natural Language, In Text, Speech and Dialogue, Volume 6231/2010 pp 224-231, Berlin: Springer

Endnote i

This identification process has been achieved to an extent at local government level in Ireland through the partnership processes in the SPCs, the Community and Voluntary fora etc.