News comments on facebook – a systemic functional linguistic analysis of moves and appraisal language in reader-reader interaction

1. Introduction

Computer-mediated communication (CMC) has a short history, but its influence on people’s communication in general and use of language in particular is substantial (Greiffenstern 2010). Among the CMC platforms, participatory websites — commonly referred to as Web 2.0 — have arguably become one of the most influential ones. These websites have their distinctive features, such as topical discussions among large, dispersed groups, varying levels of interactivity among users, and a central authorial message source, the combination of all of which “marks the evolutionary departure of Web 2.0 systems from previous forms of online messaging systems and websites” (Walther and Jang 2012, 3). On a broader scale, Walther and Jang classify contents on Web 2.0 into four types based on the source of the contents, namely proprietor or page-owner-generated content, visitor-generated content, deliberate machine-generated statistical representation of the users, and unintentional machine-generated statistics. Of these four, these authors remark that the visitor-generated content is the “defining feature of participatory websites and distinguishes them from the traditional web” (Walther and Jang 2012, 4). Reader comments now have become “the norm” of online news (Stroud, Scacco, and Curry 2015) and have given rise to a new form of interpersonal interaction, the reader-reader interaction.

Perceived as an interesting and complex phenomenon, this emerging form of reader-reader interaction has attracted increasing scholarly interests with quite a few studies done on different types of participatory sites, including the original participatory news websites (Stroud, Scacco, and Curry 2015, Ksiazek 2018), Facebook comments (Tagg and Seargeant 2016; Cionea, Piercy, and Carpenter 2017; Larsson 2017), Facebook instant messaging chats (Meredith 2017), YouTube video comments (Bou-Franch and Blitvich, Pillar 2014), Google groups (Lewiński 2010), news groups (Marcoccia 2004), chat rooms (Weger and Aakhus 2003), discussion board (Lander 2014), and Twitter (Mellor 2018). There have also been multiple studies that highlighted the similarities and differences across platforms like those done by Hille and Bakker (2014) and Rowe (2015) comparing interaction in news websites and Facebook news pages, by Ben-David and Soffer (2018) regarding conventional news websites, news websites with Facebook comment plugin, and Facebook page of the news media, and by Halpern and Gibbs (2013) contrasting YouTube video comments and Facebook news comments.

Such studies have provided several crucial insights into reader-reader interaction. First, it is generally agreed that this form of interaction seems to be short and underdeveloped with only a few exchanges and often ends incomplete or unresolved (Marcoccia 2004; Bou-Franch and Blitvich, Pillar 2014; Halpern and Gibbs 2013; and Lander 2014). In the terms of journalism, reader-reader news discussions regardless of the platforms are of low argumentation content (Ksiazek 2018; Larsson 2017). The interaction in such discussions reveals polarization of groups’ ideologies rather than the weighing of diverse positions and persuasion that is characteristic of deliberation (Halpern and Gibbs 2013). Moreover, in terms of language, interaction in news reader comments has been found to have high level of hostility or conflict among interactants (Tagg, Seargeant, and Brown 2017). For example, YouTube video comments are notorious for aggression, incivility, and sometimes even hate speech (Halpern and Gibbs 2013), and Facebook news comments, with less anonymity and thus apparently less aggression, are also found to contain a lot of confrontation (Rowe 2015).

Despite their contributions, these papers, most of which originate in the field of journalism, have yet to provide a detailed, systematic description of news readers’ communicative actions when they engage in news discussion (Bou-Franch 2014; Herring, Stein, and Virtanen 2013). To be specific, although conversation analysis has been adopted to untangle the direction of interactions (Lewiński 2010) and turn taking (Hutchby 2014; Giles and Paulus 2017) in online reader comments, little is known about how interactants perform specific moves to navigate the complex many-to-many polylogues (Forbenius and Harper 2015). In an attempt to fill this gap, the current paper will examine the interactional patterns and linguistic realization of user-generated responses to a news post on Facebook, arguably the most popular social networking site in the world with approximately 2.19 billion monthly active users as of the first quarter of Statista n.d.).

To research online discourse, some researchers advocate developing brand new and dedicated methods (Rogers 2009, as cited in Bou-Franch 2014). This approach certainly has its merits, but developing new digital methods takes remarkable time and effort as well as extensive testing to ensure their relevance and rigor. Hence, Herring (2004) proposes adapting tools from conventional conversation analysis to study online discourse, an approach she refers to as computer-mediated conversation analysis (CMCA). In this study, following CMCA approach, we have adapted frameworks of conversation analysis from Systemic Functional Linguistics (SFL) to analyze the Facebook news reader-reader interaction. This theoretical framework has been chosen because as Eggins and Slade (1997) argue, SFL is suitable for analyzing casual spoken interaction, to which news reader comments on Facebook bear striking resemblance. To lay the foundation for this study, a brief introduction about SFL will be provided in the next section.

2. Theoretical framework

As stated above, to investigate Facebook news inter-reader interaction, this study has adapted the SFL conversation analysis framework as outlined in Eggins and Slade (1997). In their book, these authors propose a detailed network of speech functions to label individual moves in a casual conversation as an adaptation of the previous works of Halliday, Eggins, and Martin (Eggins and Slade 1997, 193–214). Despite its ground-breaking nature (Martin 2009), this network was originally devised to analyze face-to-face conversations among a limited number of interactants rather than online news readers’ polylogues. Hence, its linear representation of interaction structure and staging of moves found in conventional conversation analysis and genre studies may not be able to capture and show the true extent of complex many-to-many interaction among news readers. Thus, we have employed a more visual method involving mind maps to show the complex development of multiple strands of interaction within the Facebook news polylogues, to reflect the temporal distribution of messages left by news readers, and to present at the same time the parallel, horizontal expansion as well as the linear vertical direction and the polarization of viewpoints expressed in such comments.

The schematic structure of texts is seen by Eggins and Slade (1997, 57) as the “overall staging patterning of texts” that includes individual moves, “a stretch of spoken or written discourse which achieve a particular purpose in a text” (Cortes 2013, 35). In this study, a move is defined as a specific stage in the whole structure of texts. To meet the overall communicative purpose of a text, each move has its own communicative purpose and can be compulsory or optional in the move staging pattern. Identifying the schematic structure of texts of a certain genre, including the specific moves and their order, as well as their lexicogrammatical realization, is central in understanding a genre (Eggins and Slade 1997; Henry and Roseberry 2001; Swales 2004). How a move is identified depends on whether texts are long, well-structured, with a specific “pragmatic” purpose such as a research paper, or whether texts are spoken interactions with short exchanges for interpersonal purposes (Eggins 2004, 5).

As noted earlier, analysis of moves is often accompanied by examination of the lexicogrammatical realizations of such moves. For this purpose, the current study has adopted the Appraisal theory developed by Martin and White (2005) to answer the third research question on the linguistic realizations of moves in the Facebook news comments. Martin and White (2005) argue that appraisal has three domains of attitude, engagement, and graduation. The main focus of this study is the first domain of attitude, which is subdivided into affect, judgment, and appreciation, but we also looked at graduation language to further understand levels of attitudinal meaning in the Facebook news reader comments. Figure 1 provides a summary of Martin and White’s system of attitude in appraisal theory.

Figure 1.

The system of appraisal.

In short, following CMCA approach, two frameworks from the Systemic Functional Linguistics tradition, namely the speech function network and the system of attitudinal appraisal language, have been employed as analytical frameworks to answer the three following questions.

RQ 1: What communicative moves are performed in Facebook news comments to show readers’ levels of agreement and disagreement?

RQ 2: How does interaction develop within Facebook news comment exchanges? (more specifically, what, if any, are the patterns of communicative moves in the interaction?)

RQ 3: How is attitudinal language used to realize different communicative moves?

3. Methods

According to the review of Naab and Sehl (2016), quantitative analysis of big data dominated studies on user-generated contents. Acknowledging such imbalance in research methods, journalism and communication scholars have emphasized the importance of case studies and called for more in-depth treatment of data (Lewiński 2010; Herring 2013; and Giles and Paulus 2017). In light of this recommendation, this study has been designed as an exploratory case study to offer insight into specific levels of language use and emerging patterns rather than generalizability based on large sample size and feature counts.

4. Results

4.3. Research question 2: development of interaction

Although there was no fixed move order that applied to all the exchanges of comments, some patterns were observed in how the exchanges developed and how this development was realized through the choice of certain moves.

4.3.1 Incomplete exchanges

As can be seen in Table 2, the majority of initiating moves (19 out of 27) were done through statements of opinion. With regard to the closing moves, generally there were more Rejoinder moves to sustain interaction than Respond moves to conclude the interaction, which left most of the exchanges of studied comments incomplete, or unresolved. Fourteen of the exchanges were obviously incomplete since they ended with Rejoinder moves that require responses from previous interactants, who did not return to the discussion. Many of the closing Rejoinder moves were of confronting type, which means the confrontation in these exchanges was not completely resolved.

4.3.2 Branches of exchanges

All of the exchanges examined in this study, with six or more comments within each, contained at least one sub-cluster of exchanges that branched out from them. In other words, although the Facebook interface showed the initiating comment and all the subsequent comments replying to it as one long conversation, such conversation was further developed into different directions based on some of the reply-comments. The typical branching structure of an exchange containing a sub-exchange within it is illustrated in Figure 2 below. Similar to branches of a tree, these sub-exchanges had the potential to extend, and the further they grow, the less they depended on the initial comment they branched from in terms of content.

Figure 2.

The “branching” structure of Facebook news comment exchanges.

4.3.3 Vertical versus horizontal development of interaction

Horizontal direction describes exchanges in which three or more comments were aimed at the same initiating comment in a parallel manner and apparently independent of each other in terms of content. In other words, the attention was mainly given to the initiating interactant and was spread throughout the whole exchange. Meanwhile in vertical direction, each comment was added in response to the one right before it, and three or more comments developed in this manner create a line of argument. Lewiński (2010) has made a similar observation of these two distinct directions of argument development in Google group interaction. However, as the exchanges in this study contained within themselves multiple strands of interaction, the relationship between horizontal and vertical interaction was more complicated.

More specifically, first-level analysis of the exchanges revealed that more of the main exchanges developed in horizontal direction (16 exchanges) than vertical one (9 exchanges). The most extended discussions, exchanges E53 and E54 containing 42 and 29 comments, respectively, also unfolded in horizontal manner with most of the comments replying directly to the initiating ones. While horizontal exchanges were characterized with large number of commenters and their parallel comments, vertical exchanges in the data engaged only up to six participants and comments. Moreover, as horizontal interaction involved more readers and generated more exchanges of ideas, some of their comments became the departure point for smaller, more narrow-scoped, vertical interaction between a small number of readers. In other words, many shorter vertical exchanges were contained within large horizontal ones, making it impossible to exclusively categorize an exchange as either horizontal or vertical.

4.3.4 Polarization of viewpoints

4.3.4.1 “Support” exchanges

Regarding the level of support and/or confrontation between the interactants, the exchanges in this study showed clear signs of polarization of opinions. In one extreme where there was unanimous agreement between the interactants, most or all of the moves done were Respond-Support ones such as Develop and Agree moves, which show positive reaction to previous moves without sustaining the discussion. Four of the exchanges in this study were labeled “Support” exchanges for possessing such move pattern. Three of such exchanges grew horizontally, as illustrated in the structure of exchange E20 (see Figure 3). This exchange started with Craig’s article-comment, which received five replies containing supporting moves. One of such replies made by Brigit was further supported by Kay and then Megan, making a vertical branch exchange.

Figure 3.

The structure of horizontal “support” exchange E20.

4.3.4.2 “Confrontation” exchanges

The opposite of “Support” exchanges are “Confrontation” ones. All the nine confrontation exchanges identified in the data were incomplete and ended with Rejoinder-Confront-Counter moves that offer alternate positions to the preceding comments and require the previous interactants to respond to. Throughout these confrontation exchanges, interactants constantly disagreed with each other and challenged and rechallenged each other. Exchange E48 (see Figure 4) illustrates horizontal interaction in which four Facebook users disagreed with Tony’s initial comment in parallel comments. Tony then replied to these in confronting moves and received in return some more confrontation. Exchange E55 (see Figure 5), on the other hand, showcases a vertical exchange. The initial comment by Jules only had three replies, but one of them developed into a vertical line of debate between Jules and John who disagreed with her. At the same time, Jules also replied to the other confronting moves with more confrontation.

Figure 4.

The structure of horizontal “confrontation” exchange E48.

Figure 5.

The structure of vertical “confrontation” exchange E55.

4.3.4.3 “Alternation” exchanges

The other 12 exchanges were labeled “Alternation” to acknowledge the presence of opposing viewpoints and the switching of turns between commenters of each viewpoint who supported like-minded people and confronted the opposite side. In alternation exchanges, polarization of opinions could be further observed as many commenters at the same time either lent support to a fellow reader or confronted them and two opposing schools of thoughts gradually emerged from the interaction. Between the two extremes of “Support” and “Confrontation,” the 12 “Alternation” exchanges in the data showed mixture of agreement and disagreement among interactants, resulting in the co-existence of supporting and confronting moves in these exchanges with complex organizations.

An exchange like E32 (see Figure 6 below) with an apparently high level of agreement among its seven commenters and horizontal direction of development was still categorized as “Alternation” instead of “Support” because one particular commenter (Jud) showed disagreement with Mark’s initiating comment and thus attracted five more comments with both confronting and supporting moves from the other commenters who shared viewpoints with Mark and disagreed with Jud. Jud further replied to some of those comments, making this sub-exchange a vertical one branching from the horizontal main exchange E32 initiated by Mark. But for this branch of vertical exchange starting from Jud’s, E32 would have been labeled “Support” for the unanimous agreement that the commenters showed toward Mark’s initiating comment through supporting moves.

Figure 6.

The interaction structure of “alternation” exchange E32.

Another illustration of “alternation” is exchange E35. In this exchange initiated by Wendy, four commenters disagreed with her and performed confronted moves in response to her comment in parallel manner, making the interaction a horizontal one. Only Jud, the fifth commenter, showed support for Wendy and thus motivated two other commenters to participate in a branch exchange in a vertical direction with mostly supporting moves. Similar to exchange E32, the interaction developed in horizontal order with most comments coming from one side of the argument, and a branch developed from the main exchange in vertical order focusing on the other side given by an “odd-one-out” commenter.

In some very horizontally expanded Alternation exchanges like E53 and E55, a large number of parallel reply-comments (42 and 16, respectively) aimed at the initiating one, consisting of both confronting and supporting moves. There was no clear domination of either side of argument, and some particular reply-comments were further developed into branch exchanges with a few contributors. On the contrary, in the much less developed exchanges, the initiating comments received only one or two replies, but these replies further expanded horizontally or vertically with mixture of supporting and confronting moves, earning these exchanges the “alternation” label. Thus, even in exchanges of only six or seven comments in total, it was still possible to observe polarization of viewpoints.

The features of these three categories of exchanges in terms of their move patterns, levels of expansion, and directions of development are summarized in Table 3 below (see Appendix C for the full script of the example exchanges). Overall “Support” exchanges where most moves performed were of supporting nature tended to develop horizontally, were the least frequent in the data, and contained the smallest average number of comments, with also the smallest average word count per comment. Compared with “Support” exchanges, “Confrontation” ones were dominated by confronting moves, were more frequent in the data, developed in both horizontal and vertical directions, contained much more comments, and had the greatest word count per comment — over three times larger than that for “Support” exchanges and almost twice as that for “Alternation” ones. Lastly, the combination of both supporting and confronting moves at roughly equal proportions made “Alternation” exchanges the most salient in the data of this study. Unsurprisingly, this last type of exchanges also engaged far more interactants than the other two, had much more comments per exchange, and tended to expand horizontally.

Table 3.

Categories of exchanges of Facebook news comments based on move patterns.

Description	Support exchanges	Confrontation exchanges	Alternation exchanges
Number of exchanges	4	9	12
Average number of Facebook users per exchange	6	5.4	10.3
Average number of comments per exchange	7.75	10.22	13.67
Average number of words per comment	15.77	54.57	28.42
Level of agreement	Strong agreement	Strong disagreement	Mixed agreement and disagreement
Typical types of moves	Response-Support moves (Develop, Agree)	Rejoinder-Confront moves (Rebound, Counter, Refute)	Both Confront and Support moves
Direction of interaction	More horizontal (3 out of 4 exchanges)	Both directions (4 horizontal, 5 vertical exchanges)	More horizontal (8 out of 12 exchanges)

5. Discussion

The findings from the current study add to the general consensus that there is more confrontation than support, or more disagreement than agreement, in the online news reader comments across platforms as revealed in Section 1. To be specific, based on the SFL’s speech function network (Eggins and Slade 1997), this paper indicates that at the specific level of move, the Facebook news comments contained more confronting moves than supporting ones. The most frequently performed moves in the data in decreasing order of frequency were Counter, Develop, Rebound, Refute, and Agree moves, three of which are of confronting nature. At the more general level of interactional patterns, there were also more Confrontation exchanges than Support ones in the data. Moreover, the paper also deepens our understanding of how such moves were realized linguistically. Further lexical analysis showed that Facebook news readers used more appraisal language in confronting moves than in supporting ones, which implies higher levels of interpersonal engagement in the former move type. All these findings point to the conclusion that Facebook news comments seem to show more conflict than support among interactants. On the one hand, this reflects the nature of news consumption, in which the more controversial and political topics attract wider readership. On the other hand, the act of constantly challenging each other is also a sign of equal power status among Facebook interactants, similar to that observed between friends or close family members’ casual face-to-face conversations (Eggins and Slade 1997). Thus, these findings lend support to the notion of user-generated contents as space for public democracy, which is often discussed in recent studies of journalism and politics (Rowe 2015).

Many of the descriptions of Facebook news comments in this study resonate extant literature on online news reader response. In Bruce’s (2010) corpus of 1000 reader comments on news websites, the average word count was 97 words. In our study, the average length of comment was only half as long, of roughly 47 words. Similarly, Ben-David and Soffer (2018) also found news comments on Facebook significantly shorter than those following articles on official news websites. In Bou-Franch and Garcés-Conejos Blitvich’s examination of YouTube comments, almost 70% of participants contributed a single comment and an average of 2.1 comments each. The current study produced very similar results, with 65% of Facebook users leaving only one comment, and on average each contributed 2.2 comments.

However, the study also highlights some features of news readers’ interaction that are more prominent in Facebook than on some other platforms thanks to Facebook affordances. The concept of affordances in CMC refers to how and the extent at which some platform design features can facilitate or hinder mediated communication (Hutchby 2001). Rowe (2015) compared news comments on the Washington Post news website and its Facebook page and found that interactivity, among many features, was much higher in the former than the latter. However, it should be noted that in 2013 at the time of his data collection, Facebook had not launched the “Reply” function in the comment section, and any comment with at least one reply in his study was coded as “interactive.” In our current study, as Facebook news users could choose a particular comment to reply to and tag the involved people in their comments, interaction was quite developed, with at least six turns of comment per exchange and 20 exchanges of six to ten comments. As users scroll down the Facebook comment section, the “top” comments, those that are most liked or replied to, tend to be shown first by default in many news page, and thus they are more likely to attract even more replies and reaction. From the perspective of technological affordances, such design features of Facebook help to explain why many Facebook news comments are left alone and even hidden from initial view, while some particular comments become the centre of attention and are considerably expanded with remarkable numbers of interactants and messages. Moreover, while Facebook company emphasizes the interpersonal relationships and constantly updates Facebook functions to facilitate interpersonal interaction (Tagg and Seargeant 2016), news media tend to rely more and more on third party like Facebook to facilitate the reader response section to avoid the complex task of comment management and to focus more on news production and report (Ben-David and Soffer 2018). Thus, it can be argued that higher levels of interactive and interpersonal news commenting will possibly be observed on Facebook than on the conventional news websites.

The findings from our study also echo some general remarks on the quality of deliberation found in online news comments. Similar to Bou-Franch and Blitvich, Pillar (2014) and other scholars’ conclusion, we found the Facebook news comments under scrutiny a form of public deliberation, though possibly not in its ideal form. Firstly, despite the interactant’s willingness to prolong discussion through their choice of Rejoinder moves, most of their exchanges ended incomplete in unresolved conflict, which rarely happens to face-to-face argumentation (Lander 2014; and Cionea, Piercy, and Carpenter 2017). Secondly, the Facebook news comment exchanges in this study tended to expand horizontally in size rather than vertically in depth. Generally, there were more horizontal exchanges than vertical ones in the data, with the former having more interactants and more comments than the latter. However, a closer look revealed that when many of the main exchanges expanded horizontally with multiple interactants adding parallel replies, some of such comments became the starting point for branch exchanges of smaller scale and often in vertical direction. In addition, most interactants in our study did not contribute more than twice, and thus it was likely that their arguments were not given enough time, space, and thought to be thoroughly developed.

However, what Facebook news comments lack in depth, they can compensate in quantity. As the majority of exchanges found in our data develop around disagreement and mutual confrontation, the Facebook news readers were exposed to a range of different or even opposing viewpoints and personal narrations and were motivated to present their positions in fairly rational manner, as seen in another finding from our study. Lexical analysis using the SFL Appraisal framework showed there were marked differences in interactants’ use of attitudinal language across move types and exchange patterns. Typically, there was more appraisal language in Rejoinder moves to prolong interaction than Respond ones to conclude it, and more in Confront moves to show disagreement than in Support ones. In the two extreme exchange patterns of unanimous support and strong confrontation, interactants used much more appraisal language than in the more moderate Alternation exchanges. Support exchanges contained more Affect language, but these exchanges were the least common type in our data, while Confrontation ones had more Appreciation and Graduation appraisal items. Moreover, when all exchanges were seen as a whole regardless of their move patterns, there was a tendency toward greater use of appraisal language to show judgments and appreciation and much less expression of affection. These seem to indicate Facebook news readers’ choice of language to avoid emotional reactions and to express themselves in more impersonal ways, thus possibly enhancing the quality of their argumentation. Thus, in effect, it can be inferred from the results that Facebook news comments act as a form of mass public deliberation that exposes individuals to ideologies and motivates them to discuss political topics, but it also reinforces group polarization and thus may not be the ideal place for careful deliberation.

Regarding Affect appraisal language in particular, our finding that Facebook news comments were not very emotional somehow contradicts that by Ben-David and Soffer (2018) who found that news comments on Facebook platform regardless of the topics were more emotional than those on the official news websites. Thus, much more research is needed to understand this particular interpersonal aspect of online news comments.

The comments analyzed in this study also demonstrated a mixture of features typical of both speech and writing. While the exchanges of Facebook comments were sometimes highly interactive with very small time lapses in between and with very short, even one-word clauses like in spoken conversations, they were at other times more similar to written emails with delayed or even no feedback, and at some other times they resembled short argumentative essays with multiple moves done in the introduction, body, and conclusion. Therefore, the findings of this study support the view that the division between spoken and written genres is no longer relevant to genre identification and methods and tools to analyze face-to-face spoken conversations must be considerably adapted to meet the hybrid and changing nature of interaction in user-generated contents.

The current study can serve as an illustration of such necessary adaptations. We employed the SFL speech function network used for face-to-face conversation among a limited number of interactants to understand online news readers’ polylogues. Although the speech function network was originally devised to analyze casual conversations, its detailed classification of moves and the mapping of interactive relationships between these moves make the network also suitable for coding other forms of interaction involving multiple participants. Since the descriptions of specific moves and tests to identify those moves are data driven, more linguistic examples and subsequent generalizations based on them can and should be added to more accurately describe the language of online user-generated contents. Furthermore, our study also showed that the linear representation of interaction structure and staging of moves found in conventional conversation analysis and genre studies may not be able to capture and show the true extent of complex many-to-many interaction among news readers. Thus, we employed a more visual method using tree-like maps to show the complex development of multiple strands of interaction within the Facebook news polylogues, at the same time presenting the parallel, horizontal expansion as well as the linear vertical direction and the polarization of viewpoints.

6. Conclusion

To recapitulate, the current study was carried out to help fill the gaps in the literature related to online user-generated interactive discourses, more specifically news readers’ comments on Facebook. The SFL approach has provided the study with the two frameworks of Speech functions and Appraisal language for a two-stage analysis of the communicative moves and interpersonal language found in the interactive exchanges of Facebook news readers’ comments. The results from these two types of analysis offer an initial overview of these exchanges of comments in terms of their development patterns and language to express interactants’ attitudes.

The findings from this study have added more evidence to the existing description of online news response, including its shared features with both traditionally spoken and written language, and its occasionally high level of confrontation and less affective expression between interactants, its polarization of viewpoints, its inclusion of multiple small exchanges within a larger one typical of polylogues, and its tendency to expand horizontally without ever coming to a conclusion. The findings have also helped to draw some distinction between the interaction typical of Facebook news comments and that found in conventional news websites thanks to the concept of affordances.

Because of the exploratory nature and small scale, this study has some limitations that future research can help to address. Firstly, the small size of a purposefully sampled corpus from only one news source prevents the results from being generalized. Therefore, throughout the paper, attempts have been made to compare our findings with those of previous studies whenever relevant to generate more insightful understanding of the data. It must also be acknowledged that it is hardly possible to find news sources with neutral political inclination, and the Facebook page chosen for this study may not be an exception.

With a broader scope of study in a more relaxed time frame, the aforementioned limitations could be significantly overcome. Since the language in CMC in general as well as online social networks in particular is a recent area of research, there is plenty of room for extending and modifying this study in very different directions. The directions subsequently outlined in this section are only some most closely related to the current study. From a more quantitative approach, further studies can compile a larger corpus with criterion-based selection of varied news topics, news sources, time periods, and different demographic groups. Such a sample will enable the researchers to statistically measure the correlation between the occurrence of specific moves and their linguistic realizations as well as between exchange patterns and interactants’ choice of specific moves.

Qualitative techniques to investigate Facebook users’ perception can also be used to complement the researchers’ analysis. Information provided by the interactants themselves can offer valuable insight into the context of language use and insiders’ explanations of the phenomena observed by the outsider analysts. Moreover, researchers can consider involving multimodal data present in the comments such as images, videos, and hyperlinks as both the context of and contents for the analysis.

Another way to extend the scope of the current study is by the inclusion of standalone comments, which can also vary greatly in length, structure, and linguistic realizations. The exchanges of comments and the replies attached to them in this study are a prominent feature of the news comments but by no means sufficient to represent general online reader response. Thus, when the standalone comments and the comments with attached replies are examined together, patterns different from those found in this study may emerge.

Lastly, for a more comprehensive picture of interpersonal interaction in the Facebook news comments, other aspects of interpersonal meaning could be studied. The current study focused on the Attitude branch of the Appraisal theory, but further studies can include analysis of other features such as emoticons and various categories of involvement language including the use of names, slangs, swearing, and humor.