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The author examines interactions in a forum community. Her paper focuses primarily on the negotiation of status, which is measured for example by the length of membership and the activity of the users in the communities. Using the example of the community 'The Student Room', she shows that newcomers first have to earn the right to perform certain verbal actions.
Face Work and Social Media
(2014)
On platforms such as Facebook and Twtter, on message boards, in blogs and commentaries, in short: in the Social Media, users interact as if they knew each other personally. Malicious verbal behaviour is found next to clapping and kissing emoticons, both indicative of users' relational work strategies. This book presents seventeen papers on face work in Social Media - theoretical reflections as well as corpus-based studies - thus opening the way to rethink linguistic pragmatics in computer-mediated communication.
Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora.
The authors discuss how mutual criticism is expressed in the CouchSurfing community. As this community is based on mutual trust and the willingness to provide overnight accommodation in their own homes, user ratings that contain criticism and negative judgement have to be formulated in a way to avoid further conflicts and to maintain a good host image. This is why many negative evaluations contain mitigating strategies that anticipate future interactions in the community and that can be judged as face work.
Political debates bearing ideological references exist for long in our society; the last few years though the explosion of the use of the internet and the social media as communication means have boosted the production of ideological texts to unprecedented levels. This creates the need for automated processing of the text if we are interested in understanding the ideological references it contains. In this work, we propose a set of linguistic rules based on certain criteria that identify a text as bearing ideology. We codify and implement these rules as part of a Natural Language Processing System that we also present. We evaluate the system by using it to identify if ideology exists in tweets published by French politicians and discuss its performance.
This paper presents the TWEETDICT system prototype, which uses co-occurrence and frequency distributions of Twitter hashtags to generate clusters of keywords that could be used for topic summarization/identification. They also contain mentions referring to the same entity, which is a valuable resource for coreference resolution. We provide a web interface to the co-occurrence counts where an interactive search through the dataset collected from Twitter can be started. Additionally, the used data is also made freely available.
In this paper, we propose an integrated web strategy for mixed sociolinguistic research methodologies in the context of social media corpora. After stating the particular challenges for building corpora of private, non-public computer-mediated communication, we will present our solution to these problems: a Facebook web application for the acquisition of such data and the corresponding meta data. Finally, we will discuss positive and negative implications for this method.
In this paper, the author reflects in the terms self, identity and face. She will give (psychological) definitions of the terms self and identity and differentiate the two terms before she details the concept of face. The author will exemplify the use of face in a qualitative analysis in the Spanish online forum "Crepúsculo" (Twighlight).
The author dedicates her paper to collective attacks against absent third parties. The users, who do not know each other, construct a shared concept of the enemy which they then male fun of, attacking it collectively in the form of so-called 'flaming'. Even if the person being attacked is unaware of it, this FTA has the effect of enhancing the shared face of the group of attackers.
The paper by Beatrix Kreß provides a contrastive study of face work in German and Russian online communication. She analyses users' comments in online newspaper and comes to the conclusion that Russian debates tend to have a more direct style, whereas German users more frequently apply humour to mitigate FTAs.
The difference between experts and laypeople is also the subject of the paper by Gesa Linnemann, Benjamin Brummernhenrich and Regina Jucks. In an experiment in pedagogical psychology, they examine efficient knowledge acquisition in e-learning contexts. In the experiment, tutors applied various strategies to criticise the learners' results, with different intensity levels of face threat. If mitigating strategies were used, the learners considered the tutors to be more credible.
The author shows, on the basis of Watts' model, that online communication on interaction platforms tends to be marked. Due to the media conditions, utterances can be misunderstood or ambigious. This often leads to a discussion about how a post is to be interpreted and about a third party who may also be reading - a potential FTA. To counteract this, verbal, paraverbal an non-verbal strategies aim at marking the posts through multiple codes by means of their user profile, with their avatar, signature, etc. - options many platforms provide and thus support such behaviour.
We present a first attempt at classifying German tweets by region using only the text of the tweets. German Twitter users are largely unwilling to share geolocation data. Here, we introduce a two-step process. First, we identify regionally salient tweets by comparing them to an "average" German tweet based on lexical features. Then, regionally salient tweets are assigned to one of 7 dialectal regions. We achieve an accuracy (on regional tweets) of up to 50% on a balanced corpus, much improved from the baseline. Finally, we show several directions in which this work can be extended and improved.
Martina Schrader-Kniffki analyses how such status attributions are developed in the French forum 'Francais notre belle langue'. Users of this community discuss language-related topics, usually on the level of laypeople in linguistics. However, the self-presentation of the participants plays an important role in the discussion, which is often the result of intentional subjectified speech acts. In this way, the users develop evidentially and constructed knowledge.
