This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-) automatic moderation , a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argu-ment/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious). Considering Argument Mining (AM) for Social Good implies a strong conceptual shift: the discourse exchange is not to be interpreted as a competition to be won by the most persuasive contribution 1 , but rather as a cooperative endeavor in which 1 In this paper, we use the term contribution to refer to a turn in a discourse exchange; more concretely a contribution is a textual unit in a discourse contex, e.g., a post in a forum, a tweet in a discussion thread; a speech in a parliamentary debate). each individual contribution represents a move towards a shared goal. If argumentative discourse is cooperation, it is not to be taken for granted that the perfect debater, most often the primary objective in AM research, is necessarily also the best team player. Building on this assumption, we review recent developments in the field of AM from the perspective of its application in socially relevant contexts. Our survey has a strong interdisciplinary perspective, putting the focus on the collaboration between NLP and the Social Sciences and, more specifically, in argumentation targeted at decision-making ( deliberation ). Deliberative discourse historically characterizes parliamentary debates; however, it pervades, more and more frequently, discussions in digital democracy forums and, beyond that, specific strands of discussions in generalistic social media. Looking at argumentation through the lens of deliberation has a 2-fold benefit. From a purely NLP perspective, the insights gained through modeling deliberative features can in turn be employed in applications targeting discourse in deliberative forums and social media more broadly, allowing systems to be more adaptable to real-world discourse settings. Social Sciences, in turn, can enormously benefit from the possibility of scaling up to a larger public with the support of NLP methods. The novelty of this survey with respect to literature (Cabrio and Villata, 2018; Lawrence and Reed, 2019) is precisely in its interdisciplinary focus, which leads us to a novel formulation of the widely debated notion of argument quality (Wachsmuth et al., 2017a,b), which we put in direct comparison to Deliberative Quality (Bachtiger and Parkinson, 2019). The take-home message of this comparison is that the quality of a contribution to an argument cannot only be quantified in terms of its textual (linguistic/logical) properties and the relation to the preceding contributions (as commonly done in argument quality), but also the relation to the cooperation challenge needs to be brought in the picture. In other words, a good contribution is one that ensures the discourse to unfold productively. 2 We conclude the survey by defining the conceptual coordinates and the practical challenges of (semi-) automatic moderation , a highly integrative application of AM for Social Good which represents a natural testbed for the integrated definition of quality discussed above. We propose to implement moderation as a form of discourse optimization, and spell out the objective of such optimization  that is to say, the desiderata for an NLP-based moderator. We discuss the concrete challenges related to the tasks of an NLP moderator, and review existing work that, albeit not targeted at NLP moderation directly, can be brought in as part of a puzzle which is both ambitious and worthwhile to pursue. Argument(ation) Mining (AM) is a field encompassing varying tasks that deal with the automated analysis of arguments from natural language text. Habernal and Gurevych (2017) defines AM as the general task of analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze the data at hand. The progress in the field of NLP in recent years has also influenced this research area: automatic recognition and identification of arguments has been enabled in various domains and different models for the analysis and representation of argumentative structure have been developed. Furthermore, there is a growing research interest in other aspects of AM, such as argument quality. Cabrio and Villata (2018) provide an elaborate overview of the AM framework in their data-driven analysis of the state of the art after five years of significant developments in the field of AM. Generally speaking, given a collection of natural language texts, the task at hand is implemented in two stages: Argument extraction The system first identifies the documents which contain the argumentative structure and the specific textual spans in which 2 The productive quality of a contribution can be defined in relation to Social Sciences literature (Steenbergen et al., 2003; Steiner et al., 2005), c.f. Section 3 argumentation is encoded. Once the textual boundaries are defined, subportions of the argumentative spans are assigned to a set of pre-established argument components (e.g. claims, premises, rebuttal, etc.). A variety of models were used for this including Naive Bayes (Moens et al., 2007), SVMs (Mochales and Moens, 2011), RNNs (Nicu-lae et al., 2017; Eger et al., 2017), Pre-trained Language Models (Chakrabarty et al., 2019; Lugini and Litman, 2020), and other supervisedlearning techniques (Ein-Dor et al., 2020). Relation assignment The goal of the second stage is to model the relations between the argumentative spans identified in the first stage. These relations can exist between different arguments (support, attack) as well as within an argument (connecting the premises with the claim). Recent approaches to argumentative relation classification investigate for example relational models (Traut-mann et al., 2020) or inject background knowledge by leveraging features from different knowledge bases (Kobbe et al., 2019). Detecting these relations is necessary to model the overall structure of the argumentation (discourse/debate). As this structure can be complex, the task is difficult, involving high-level knowledge representation and reasoning issues. After the relations are detected, the discourse structure can then be mapped to a graph representation, called argumentation graph, with the arguments as nodes and relations as edges. To simplify the problem, some approaches reduce the graph to a tree-structure representation (Peldszus and Stede, 2015; Stab and Gurevych, 2017). Different methods to generate the structure have been investigated, e.g. SVMs (Habernal and Gurevych, 2017; Niculae et al., 2017) or textual entailment (Cabrio and Villata, 2013; Cocarascu et al., 2020). Modeling the relations and argumentation flow within a debate is an important fac-tor when defining the notion of argument quality, which will be presented in Section 3. Consider the following example taken from an online debate about compulsory vaccinations 3 which demonstrates the framework quite clearly. Given a statement presenting background and context, participants are asked to discuss the question Does public health demand vaccinations? ( Claims are in bold, and premises are underlined.) 3 http://debatepedia.idebate.org/en/ index.php/Debate:_Compulsory_vaccination A 1 : A vaccine is the best way to prevent an outbreak of a disease or to reduce its negative effects. Vaccinated people become immune to a certain pathogen and do not develop a disease. Although there are occasionally side effects, these affect only a tiny number of people compared to the protection offered to the vast majority. A 2 : Many vaccines have serious and sometimes deadly side effects . With many vaccines the immunity is not lifelong. Sometimes the vaccines itself can cause a serious disease to develop as a side effect. If governments know that compulsory mass vaccination is likely to cause death or permanent disability in even a few cases, it is immoral for them to make it compulsory. Here, the argumentative text boundaries are first determined from the natural language discussion and the argument components (claims and premises) are extracted. Then, the relations between the two arguments are as follows: A 1 supports the argument while A 2 attacks it. However, consider another example, extracted from an online debate platform Kialo 4 . Here, the participants' contribution and the structure mirror a more direct and conversational dynamic to argumentation. The seemingly simple example of an online exchange shows how a more conversational environment provides vaguer boundaries of argumentation structure and components. Each argument is more direct, not necessarily consisting of a claim-premise configuration, and the strength and productive quality of each argument is particularly relative to the context, each contribution affecting the argument differently either at a local or global level. Note, however, that the relations between arguments and claim are still relatively clear (e.g. A 2 supports while A 5 attacks the main claim in A 1 ; A 3 attacks A 2 directly; and A 4 closes any further 4 https://www.kialo.com/explore/ featured discussion on A 3 's premise). Clearly, the environment and type of platform under consideration have a significant impact on a system's capacity to implement such a framework and on the degree of complexity found in the components and relations to extract, assign, and predict. Working in the realm of overtly argumentative text (such as persuasive essays (Stab and Gurevych, 2017)), while challenging of course, can be quite standardized. The language use is generally in line with natural language expectations and often standard (e.g. claim, premise and stance are clear), the structure and collective goal of the debate are rather controlled and topic-specific, and the collection of participants involved is often a closed or an easily-classified set (e.g. in parliamentary debates, news forums, etc.). In social media While overtly argumentative text, like those described above, represents the natural domain of application for AM, social media constitute a powerful source of large amounts of data (billions of words) despite facing particular challenges in AM. Social media plays an increasingly significant role in modern political and social discourse, yet resources built for conducting AM on this type of data structure remain limited for clear reasons. These platforms inherently collect and spread a wide range of content, including personal opinions, facts, fake news, and additional information of interest to users. Distinguishing between personal opinion, fact, and fake news, for example, is not always straightforward, as seen in recent work on fake news detection (Kotonya and Toni, 2020). Further, the language used on such platforms is infamously chaotic and often non-standard in comparison to the language use in more structured environments, like parliamentary debates. The combination of these aspects introduces the unique challenge of implementing AM to particularly heterogeneous, poorly annotated data. Recent work has aimed to tackle such challenges in social media. Dusmanu et al. (2017) apply a supervised classification approach to identify arguments on Twitter, focusing on the tasks of facts recognition and source identification. They study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexitnews topics. Habernal and Gurevych (2017) provide an extensive analysis of the steps and the modeling strategies necessary to analyze social media data (e.g. forum posts) in terms of their argumentative structure, while Simpson and Gurevych (2018) tackle the issue of the scalability of AM algorithms. Despite the rising attention and developments to AM in social media, one of the major challenges currently facing the field is the lack of consensus on how exactly to analyse argumentative user-generated texts such as online comments (Bauwelinck and Lefever, 2020). On the one hand, the amount of annotations available for the scale of this heterogeneous data remains limited. Recent work by Schaefer and Stede (2020), among others, have aimed to construct large Twitter corpora annotated for argument components, including argumentative spans within tweets. On the other hand, annotation guidelines are not necessarily clear, and the theoretical motivations underlying the proposed guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. Bauwelinck and Lefever (2020) introduce a pilot study and aim to provide a clear justification of the theories and definitions underlying the design of a set of guidelines. The linguistic, structural, and logistic complexity and openness of such platforms clearly present unique challenges. However, being able to work well with argumentative text from social media and discussion forums is essential considering the continuously growing impact on the political and social framework of modern times. Multilingual argument mining Multilinguality is an important area of research in NLP that has gained more attention recently because of the cross-lingual transfer potentials of Pre-trained Language Models (Devlin et al., 2019; Conneau et al., 2020) and because of the potentials for a societal impact at a global scale. The latter is particularly important when considering AM for Social Good since language should not be a barrier for participation if the goal is to allow any productive contribution. Various recent studies have investigated multilinguality for AM. Eger et al. (2019) discuss a series of experiments on using machine translation and annotation projection for AM, specifically argument components extraction and classification in German, English, and Chinese. A similar approach to build training data in other languages using machine translation is done in Toledo-Ronen et al. (2020), which use a pre-trained multilingual BERT (Devlin et al., 2019) for modeling. This approach is shown to perform well for classifying argument stance and detecting evidence, but not for predicting argument quality scores. Multilingual stance detection in political social media text (Vamvas and Sennrich, 2020) is also investigated in Lai et al. (2020) using stylistic, structural, affective and contextual features from text and analysing the scenarios in which each of these features is effective. Other work has also dealt with building non-English datasets (Lindahl, 2020; Bauwelinck and Lefever, 2020; Schaefer and Stede, 2020; Zotova et al., 2020), but there still seems to be a focus on Indo-European languages (and sometimes Chinese) with a lack of datasets and analysis extending to other languages. This is a general issue in NLP research that extends to performance bias in favor of standard dialects for example in English (Blodgett et al., 2016) and bias that could target certain user groups instead of protecting them as was shown for Hate Speech Detection (Davidson et al., 2019). This is an important limitation to address in AM as well for more inclusivity and towards a more positive societal impact. The second stage in the framework of AM is defined as relation assignment (c.f. Section 2.1); a complex task that aims to predict the relations holding between the arguments defined in the first stage. Being able to model the relations between arguments and components within the structure, for example in argument graphs (Besnard and Hunter, 2014; Craven and Toni, 2016), allows us to actually work with the argumentative text in an application-based setting, understand the stance and context of arguments, and develop a story for the consequential impact of arguments on the discourse, among other things. Generally speaking, we can use this task as an approach to analyze argument quality (AQ). However, within the AM community, an open question concerns the adequate definition and op-erationalization of the notion of AQ. Despite this, to move forward with the task of AQ analysis and to create large corpora with crowd-sourced annotations, some approaches rely on the relative assessment of quality: Given two arguments, which is more convincing? (Habernal and Gurevych, 2016; Toledo et al., 2019; Gretz et al., 2020) Thus the natural way of quantifying the success of an argument is in terms of its persuasiveness. Indeed, plenty of previous work has explored the many factors which contribute to the persuasiveness of a message: the linguistic features employed by the authors (Persing and Ng, 2017), the semantic type of claims and premises (Hidey et al., 2017), the different sources of evidence produced to support an argument (Addawood and Bashir, 2016), the effects of the personality traits and prior beliefs on persuasiveness (Lukin et al., 2017; Durmus and Cardie, 2018; Al Khatib et al., 2020), the interaction with other participants (Ji et al., 2018; Egawa et al., 2020), the use of argument invention when debating about unknown topics (Bilu et al., 2019), the structure of the arguments (Li et al., 2020), and the effect of the style of the text in achieving persuasion (El Baff et al., 2020). Persuasiveness is, however, not the only way to define whether an argument is good  at least not from a deliberation point of view. A good contribution to a debate is one which uncovers a previously unnoticed aspect of a problem, thus generating a perturbation in the discourse (controversies can be productive!). Or else, a good contribution is one that settles an issue, by stating the differences between opposing views and allowing the discourse to stabilize in a series of clusters (convergence on just one position is not necessarily a good outcome). Most recent research projects (Wachsmuth et al., 2017b) aim to address the challenge of redefining the notion of AQ, away from persuasiveness and towards a more situated definition which has to do with the needs of argumentation in a real-world scenario. This new definition has been the basis for the creation of new corpora from different domains (Ng et al., 2020), where feature-based (Wachsmuth and Werner, 2020) and neural models were tested for automatic prediction (Lauscher et al., 2020). Other aspects of AQ have become the subject of AM research such as the relevance and impact of arguments (Durmus et al., 2019), the verifiability (Park and Cardie, 2018), local acceptability (Yang et al., 2019) and the best deliberative move (Al-Khatib et al., 2018). We argue that this shift is necessary for two reasons: (1) Working with real-world applications of AM naturally forces us into the more heterogeneous realm of data structures, such as social media, in which language, structure, and content are less uniform and confined to the classic notion of logical debate; and (2) In order to encourage deliberation from an open audience of citizens, we need to redefine our concept of AQ and productive discourse such that there is equal worth and participation granted to each contributor of the argument. Deliberative Quality We therefore propose adapting the definition of quality to integrate the abundant research on the topic from the field of Social Sciences. Here, the quality of a discourse has been investigated in the context of deliberation with the focus on inclusivity : how can the interplay of the different participants in the discourse lead to an optimal outcome for the collective? The focus here is not on the quality of the individual contributions. Instead, an overall quality of the discourse is determined by the fact that the individual quality dimensions are distributed among different contributions (e.g some participants do more rational reasoning, others share personal experiences). We would like to integrate those aspects that focus on inclusivity and cooperation. Similar to Wachsmuth et al. (2017b), social scientists have developed a taxonomy, the discourse quality index (DQI), that describes the different desirable aspects of a discourse (Steenbergen et al., 2003). This taxonomy has been used to analyze the quality of deliberation in different contexts, ranging from more formal contexts, such as parliamentary debates (Steiner et al., 2005), to informal discussions in online forums (Trenel, 2004). Both implementations integrate logical coherence as one dimension, cogency in Wachsmuth et al. (2017b), justification in the DQI. Some aspects of inclusivity are also being touched upon in the rhetorical and dialectical dimension of Wachsmuth et al. (2017b), such as using appropriate language ( Appropriateness ) or whether an argument supports conflict resolution ( global relevance ). We concentrate on the following dimensions from the DQI, which particularly focus on the collaborative aspect of discourse. Respect : this dimension includes respectful tone, respect for other social groups/backgrounds, and openness towards other opinions. Equality / Participation : it is not desirable that some dominant participants make the bulk of contributions while many others remain passive. All participants should have equal opportunities to contribute and all topics, including those that DQI (Steenbergen et al., 2003) AQ (Wachsmuth et al., 2017b) Description Logical coherence Local acceptability Argument should be sound, rationally worthy Justification level Local sufficiency (Enough) premises should support the claim  Local relevance Premises should be suitable to support claim Personal experiences Emotional appeal Argumentation should increase empathy Emotional balance Appropriateness Suitable language and amount of emotions  Credibility Is the participant credible? may only affect minorities, are equally relevant. Interactivity : beyond simply sharing opinions, acknowledging other viewpoints and interacting with other participants through listening and responding lead to new perspectives arising  compromises can emerge. Testimoniality / Report of personal accounts : sharing stories and personal narratives as an alternative form of communication can involve more people in the discourse, especially those who cannot identify themselves with rational argumentation. It can also make other participants aware of other perspectives as it generally increases empathy. Especially when traditional or universal norms need to be questioned, narratives are particularly well suited, as their ambiguity and vagueness creates room for interpretation. This is particularly important when new ideas or perspectives are introduced, since they cannot yet be rationally articulated. Table 1 establishes a direct comparison between discourse quality dimensions of the DQI (Steenber-gen et al., 2003; Steiner et al., 2005) and argument quality dimensions as defined in Wachsmuth et al. (2017b). Apart from the potential theoretical insights, the existing guidelines can be applied to annotate new or enrich existing corpora for AM. Despite the small size, the data already annotated based on the DQI can be made usable and extended for NLP. In addition, some of the quality dimensions can be further quantified or approximated using statistical methods. For example, interactivity or equality can be assessed with frequency-based methods, such as frequency of posts by distinct participants and response rate. Summing up The overview of the definitions of AQ along with the discussion of the potential of the integration of Deliberative Quality features into an AM framework has one strong take-home message: The need for the scope of the investigation to go beyond (a) the persuasiveness of a an argumentative text (speeches, forum posts, tweets), and (b) their relation to the immediate preceding discourse. Instead, we pointed out the need to also assess the potential of the impact of that argumentative text on the upcoming discourse: this dimension of quality, inherently related to the interpretation of argumentation as a cooperation challenge, is currently lacking in current approaches to AQ. Grounding AQ in a discourse perspective which quantifies team-playing and its impact on discourse dynamics is a clear challenge, both theoretically, in the Social Sciences and Argumentation Theory, and concretely, as the empirical quantification of discourse-grounded AQ will require large annotation efforts, real-time implementations, and thorough evaluation strategies. We propose to make a first step in tackling this challenge by mapping it into a concrete application: (semi-)automatic moderation implemented as a form of discourse optimization , or, as it is commonly referred to in the Social Sciences, facilitation (Kaner et al., 2007; Trenel, 2009). To illustrate the dynamics of moderation, let us start from concrete examples from a deliberation platform, RegulationRoom . This discussion forum has been employed by public institutions to gather citizens contributions on discussions targeting very heterogeneous issues (more details can be found in Appendix). Let us consider the following example from a discussion on the distracted driving by commercial vehicle operators (e.g., truckers and bus drivers). The posts we selected (arrows indicate comment nesting) are from the discussion sub-thread: Texting  what are the risks? 5 User 1 : In 2004,... the driver failed to move out of the low-clearance lane while talking on a cellphone. This ac-cident happened in 2004! He was TALKING on a CELLPHONE! IMO, Turn Off Cell B/4 Driving! should have become law long B/4 NOW!! All these years have gone by, hundreds of LIVES have been lost, & our society is just NOW starting to work on this issue? AND we think we need to start with small steps like banning TEXTING (& sometimes in just commercial vehicles?)? [...]  User 2 : A driver in California recently caused an accident because he spilled his coffee. Another driver almost wrecked because he was trying to light a cigarette. The bottom line is that ANY distraction while driving a car can cause an accident. Where do we draw the line? Also, there are millions of people out there who are completely capable of using their cell phone AND driving, at the same time. Are we proposing that they should be punished, for the inabilities of others? For people who spend much of their time in the car, this time might be their only chance to communicate with loved ones, do business, or make important calls. If they are physically capable to use their phones safely while driving, why restrict their freedoms? Moderator : It's true that any distraction can cause an accident. The agency decided that texting was particularly unsafe, in part on the basis of the VTTI study that we reference lower on the page. Click the graphic to get a sense of the safety risks associated with different activities. A question: do you think that this rule imposes an undue burden on personal communication? What alternative restrictions on texting, if any, would you propose to impose on professional drivers? The example involves two users who clearly differ in their argumentation style and position. User 1 has a clear position on the topic (claim in bold: not just texting, but all cellphone interactions should be banned), which she/he supports with personal reports (underlined text) an emotional tone, and a style which is typical of social media text. User 2 replies, opening the post on a sarcastic note, which serves as the first premise to her/his (implicit) claim which is encoded in three rethorical questions (in bold): there should be no restrictions at all, because imposing them would be unfair. This is the case because (premises underlined): any distraction can cause an accident, some people are capable of using their phone while driving, people who spend lot of time in the car for professional reasons still need 5 archive.regulationroom.org/texting/ design-and-operation/index.html to communicate with loved ones. A moderator then joins the discussion to (a) provide a clarification as to why the focus is on texting and a link to further information on the matter, and (b) ask User 2 to elaborate on the personal communication issue, and to propose alternatives. In the Appendix we report another example from the same topic and thread, where the user acts as a problematizer, challenging the scope and definition of the rule under discussion and the moderator acts as a discourse traffic director, pointing out that the user should read and contribute to different threads in the discussion. The guidelines for human moderators in RegulationRoom have been defined in advance in a 'moderator protocol' (eRulemaking Initiative et al., 2017) which reflect the moderator actions mentioned in the examples. In the protocol the moderator roles were divided into two main classes. Supervision functions include general moderator actions that do not necessarily target the specific content of the posts, e.g., greeting participants, monitoring compliance with netiquette (policing), or helping with technical difficulties. Substantive moderator functions aim to improve the quality of comments and promote fruitful discourse. As the examples above clearly show, this can both mean that the moderator encourages exchanges between discourse participants and participation in other posts (broadening the scope of the discus-sion), or helping users to improve the content of their posts (requests for clarification, focusing on one topic, substantive reasoning, sharing personal experiences). RegulationRoom represents an excellent example of the beneficial role of the moderator in maintaining productive argumentation from participants. However, to the best of our knowledge, there is little to no NLP work targeting moderation modeling. Park et al. (2012) used data from RegulationRoom and conducted an annotation study to empirically categorize the types of moderator interventions specified in the moderator protocol. Classification experiments were conducted using SVM to predict the type of action a moderator would perform, given the previous comment. However this work is limited as it only focuses on two types of moderator interventions (broadening the scope of the discussion, improving argument quality) and as it does not predict whether the moderator should intervene, building on the assumption that a given comment has already been flagged as in need for moderation. Besides the concrete example of RegulationRoom, moderation and discourse facilitation have been, and still are, a crucial topic in digital democracy. 6 The know-how of digital democracy experts is an invaluable starting point for the application of AM to moderation, as current research targets both the integration of digital solutions to facilitate online campaigns, and a critical reflection of the effects of such innovations on the deliberation outcomes. Digital innovation supporting deliberation Argument maps (Walton, 2005) are widely employed to support online discussions, as an emerging optimization of the deliberation. Given a specific topic, for example possible reactions to climate change, users who wish to contribute to the discussion are requested to structure their contribution by producing an item in a conceptual map and optionally writing an accompanying post. Their contribution to the argument maps is often reviewed by a moderator. So in a sense, the argument map for a given deliberation process is the outcome of a process that comes both from below (the user) and above (the moderator). Thanks to argument maps, the overall discourse picture can be overviewed and it is easier for the group of contributors to express support for one (or many) of the available options, without having to read a large number of long posts. An example of this approach is represented in Deliberatorium 7 , an e-deliberation platform which has been extensively employed in many reference studies on the effect of digital innovation on deliberation (Klein, 2011). Another example of a digital deliberation platform which integrates argument maps and offers an option for moderation is COLAGREE (Yang et al., 2021; Ito, 2018). Among the studies testing the impact of such digital platforms on online deliberation, Spada et al. (2015) tests the effect of Deliberatorium 's argument maps on an online discussion among the supporters of the Italian Democratic party concerning the desired features of electoral law to be proposed by the party to the Parliament. This study compared the discussion of users employing Deliberatorium and a control group using a traditional forum format which was then encoded into argument maps. The comparison showed that 6 See Dahlberg (2011) for an outline of positions in deliberative democracy. the argument map modality did not discourage participation, and while it appeared to make users less creative (fewer new ideas as compared to the traditional forum), it also reduced the rate of claims without further discussion. Yet, the need for trained moderators tends to be a significant bottleneck (both in terms of time and of costs) in digital deliberation. Moreover, empirical research on the effect of moderation on deliberation has uncovered the risks of biased moderation. For example, the experiment in Spada and Vreeland (2013) tests the extent to which moderators can influence participants' behavior by expressing their views during the moderation process. NLP-supported moderation represents a clear solution to the bottleneck problem affecting facilitation in digital democracy. Automatic tools can take over some of the tasks that human moderators typically perform when monitoring online discussions. For example, in Social Sciences, one of the most discussed issues in crowd-scale deliberation is flaming, i.e., aggressive and disrespectful communicative behavior (Lampe et al., 2014). Here, moderators could benefit from hate-speech and trolling detection methods in NLP. NLP methods to support deliberative decision-making have already been applied for the real-time visualisation of argument maps (El-Assady et al., 2017). Deliberation in real-time applications has the clear potential of structured arguments extraction from the news media (Daxenberger and Gurevych, 2020), the identification of the argumentative structure in deliberative contexts (Liebeck et al., 2016), as well as automatic argument summarization (Lawrence et al., 2017). Beyond the real-time support to users (and moderators) provided by the methods described above, further tasks specific to AM which are part of the role of a human or (semi-)automoated moderator include: detecting fallacies (Habernal et al., 2018b), reasoning and common-sense (Habernal et al., 2018a), relevance estimation (Potthast et al., 2019). In addition, detecting and highlighting parts of an argument that are a good target for attacks (Jo et al., 2020a) can help the moderator to motivate more participation and argumentation from opposing sides of a discussion. Another important source is the detection of implicitly asserted prepositions (Jo et al., 2020b) which has a counterpart in the framing detection task (Card et al., 2015; Akyurek et al., 2020), as framing is a manipulation strategy which highlights specific aspects of an issue under discussion to promote certain interpretations. Further NLP tasks which can play a crucial role in ensuring a healthy interaction are, for example, Hate Speech Detection (Warner and Hirschberg, 2012; Waseem and Hovy, 2016; Schmidt and Wie-gand, 2017), Fact Checking (Vlachos and Riedel, 2014; Kotonya and Toni, 2020), Facts recognition and source identification (Dusmanu et al., 2017). How to represent discourse? Thus far, we have discussed the main ingredients of a rich NLP-informed approach to deliberative discourse. These components, together with the deliberation-augmented definition of AQ sketched in section 3 are the features that the NLP moderator takes as an input. One question remains open: How to represent the argumentative discourse within a contribution (e.g. a forum post) and across contributions (e.g. an entire online deliberation campaign)? We can approach also this question from an interdisciplinary perspective. Reference work in political science aims at modeling the mechanisms of political discourse in forms of discourse networks, as defined in Leifeld (2017). A discourse network is a bipartite graph, containing two classes of nodes: actors (e.g. Angela Merkel; the left-wing party; etc.) and claims (e.g. housing opportunities should be established for refugees); Edges between actors and claims indicate the support or opposition of a certain actor to a specific claim. Discourse coalitions (Hajer, 1993) and argumentative clusters are the projection of the affiliation network on the actor and claim sides of the network (Leifeld and Haunss, 2012; Haunss et al., 2013). Recent NLP research has targeted integration machine learning in the discourse network analysis workflow (Pado et al., 2019; Haunss et al., 2020). Crucially for AM, discourse networks can integrate claims and actors with a third class of nodes, the frame nodes, which encode the reason put forward by an actor to support or reject a claim. This type of representation is perfectly compatible with a graph-based approach on argument representation which has already been established as to be preferred to a tree-structure representation both empirically (Niculae et al., 2017) and theoretically (Afantenos and Asher, 2014). network: participant inclusion, can be enforced by ensuring that the contributions of peripheric actor nodes receive the deserved salience; argument mapping and summarization can be modeled by identifying hot sub-graphs in the network; the impact of a contribution (the grounded notion of AQ we have been advocating thus far) can be quantified as the perturbation introduced in the network, with its long term effects on convergence or polarization. Who moderates the (NLP) moderators? The problem of biased moderation obviously relates to the issue of bias in NLP (Blodgett et al., 2020; Caliskan et al., 2017; Bolukbasi et al., 2016; Spli-ethover and Wachsmuth, 2020) and it has a clear implication in the application of NLP methods to moderation. For example, we would not want our NLP models to infer a negative impact on AQ from cues which just reveal that the user belongs to certain groups. This is a real risk when quality is equated to success, in turn quantified in terms of likes, replies, retweets. The public of a forum may be sensitive to such cues, but the moderator should be unbiased with respect to them. Another source of bias is the degree of literacy of a contribution: while users who express themselves poorly are likely to be less popular with the forum public, their contributions may still be a very good move in the cooperation challenge  one that moderators (NLP or humans, online or in-person) have to ensure will not be left unexploited. While there are clear social drawbacks to working with data and approaches to AM that limit the participation of the argumentation/deliberation, opening the floodgates to unregulated, evenly weighted contribution of all arguments also presents a dilemma. We present an interdisciplinary formulation of the notion of argument quality, which is more apt to work with heterogeneous data and platforms, such as discussion forums and social media. With the goal of ensuring a productive development of the discourse, we propose NLP-supported moderation to facilitate argumentation and deliberation in digital democracy. We acknowledge funding by the Bundesministeri-umfur Bildung und Forschung (BMBF) through the project E-DELIB ( Powering up E-deliberation: towards AI-supported moderation ).
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