Dr Sheryl Prentice’s work on using technology to aid in the detection of terrorists has been gaining a lot of attention in the media this week! Sheryl’s discussion of the different ways in which technology can be used to tackle the issue of terrorism and how effective these methods are was originally published in The Conversation, and then republished by the ‘i’ newspaper on 23rd June 2016. You can read the original article here.
One of the objectives of Trinity College London investing in the Trinity Lancaster Spoken Corpus has been to share findings with the language assessment community. The corpus allows us to develop an innovative approach to validating test constructs and offers a window into the exam room so we can see how test takers utilise their language skills in managing the series of test tasks.
Recent work by the CASS team in Lancaster has thrown up a variety of features that illustrate how test takers voice their identity in the test, how they manage interaction through a range of strategic competences and how they use epistemic markers to express their point of view and negotiate a relationship with the examiner (for more information see Gablasova et al. 2015). I have spent the last few months disseminating these findings at a range of language testing conferences and have found that the audiences have been fascinated by the findings.
We have presented findings at BAAL TEASIG in Reading, at EAQUALS in Lisbon and at EALTA in Valencia. Audiences ranged from assessment experts to teacher educators and classroom practitioners and there was great interest both in how the test takers manage the exam as well as the manifestations of L2 language. Each presentation was tailored to the audience and the theme of the conference. In separate presentations, we covered how assessments can inform classroom practice, how the data could inform the type of feedback we give learners and how the data can be used to help validate aspects of the test construct. The feedback has been very positive, urging us to investigate further. Comments have praised the extent and quality of the corpus and range from the fact that the evidence “is something that we have long been waiting for” (Dr Parvaneh Tavakoli, University of Reading) to musings on what some of the data might mean both for how we assess spoken language and the implications for the classroom. It has certainly opened the door on the importance of strategic and pragmatic competences as well as validating Trinity’s aims to allow the test taker to bring themselves into the test. The excitement spilled over into some great tweets. There is general recognition that the data offers something new – sometimes confirming what we suspected and sometimes – as with all corpora – refuting our beliefs!
We have always recognised that the data is constrained by the semi-formal context of the test but the fact that each test is structured but not scripted and has tasks which represent language pertinent to communicative events in the wider world allows the test taker to produce language which is more reflective of naturally occurring speech than many other oral tests. It has been enormously helpful to have feedback from the audiences who have fully engaged with the issues raised and highlighted aspects we can investigate in greater depth as well as raising features they would like to know more about. These features are precisely those that the research team wishes to explore in order to develop ‘a more fine-grained and comprehensive understanding of spoken pragmatic ability and communicative competence’ (Gablasova et al. 2015: 21)
One of the next steps is to show how this data can be used to develop and support performance descriptors. Trinity is confident that the features of communication which the test takers display are captured in its new Integrated Skills in English exam validating claims that Trinity assesses real world communication.
There is great delight that the Trinity Lancaster Corpus is providing so much interesting data that can be used to enhance communicative competences in the classroom. From Corpus to Classroom 1 described some of these findings. But how exactly do we go about ‘translating’ this for classroom use so that it can be used by busy teachers with high pressured curricula to get through? How can we be sure we enhance rather than problematize the communicative feature we want to highlight?
Although the Corpus data comes from a spoken test, we want to use it to illustrate wider pragmatic features of communication. The data fascinates students who are entranced to see what their fellow learners do, but how does it help their learning? The first step is to send the research outputs to an experienced classroom materials author to see what they suggest.
Here’s how our materials writer, Jeanne Perrett, went about this challenging task:
As soon as I saw the research outputs from TLC, I knew that this was something really special; proper, data driven learning on how to be a more successful speaker. I could also see that the corpus scripts, as they were, might look very alien and quirky to most teachers and students. Speaking and listening texts in coursebooks don’t usually include sounds of hesitation, people repeating themselves, people self-correcting or even asking ‘rising intonation’ questions. But all of those things are a big part of how we actually communicate so I wanted to use the original scripts as much as possible. I also thought that learners would be encouraged by seeing that you don’t have to speak in perfectly grammatical sentences, that you can hesitate and you can make some mistakes but still be communicating well.
Trinity College London commissioned me to write a series of short worksheets, each one dealing with one of the main research findings from the Corpus, and intended for use in the classroom to help students prepare for GESE and ISE exams at a B1 or B2 level.
I started each time with extracts from the original scripts from the data. Where I thought that the candidates’ mistakes would hinder the learner’s comprehension (unfinished sentences for example), I edited them slightly (e.g. with punctuation). But these scripts were not there for comprehension exercises; they were there to show students something that they might never have been taught before.
For example, sounds of hesitation: we all know how annoying it is to listen to someone (native and non-native speakers) continually erm-ing and er-ing in their speech and the data showed that candidates were hesitating too much. But we rarely, if ever, teach our students that it is in fact okay and indeed natural to hesitate while we are thinking of what we want to say and how we want to say it. What they need to know is that, like the more successful candidates in the data, there are other words and phrases that we can use instead of erm and er. So one of the worksheets shows how we can use hedging phrases such as ‘well..’ or ‘like..’ or ‘okay…’ or ‘I mean..’ or ‘you know…’.
The importance of taking responsibility for a conversation was another feature to emerge from the data and again, I felt that these corpus findings were very freeing for students; that taking responsibility doesn’t, of course, mean that you have to speak all the time but that you also have to create opportunities for the other person to speak and that there are specific ways in which you can do that such as making active listening sounds (ah, right, yeah), asking questions, making short comments and suggestions.
Then there is the whole matter of how you ask questions. The corpus findings show that there is far less confusion in a conversation when properly formed questions are used. When someone says ‘You like going to the mountains?’ the question is not as clear as when they say ‘Do you like going to the mountains?’ This might seem obvious but pointing it out, showing that less checking of what has been asked is needed when questions are direct ones, is, I think very helpful to students. It might also be a consolation-all those years of grammar exercises really were worth it! ‘Do you know how to ask a direct question? ‘Yes, I do!’
These worksheets are intended for EFL exam candidates but the more I work on them, the more I think that the Corpus findings could have a far wider reach. How you make sure you have understood what someone is saying, how you can be a supportive listener, how you can make yourself clear, even if you want to be clear about being uncertain; these are all communication skills which everyone needs in any language.
We are proud to announce collaboration with Markus Dickinson and Paul Richards from the Department of Linguistics, Indiana University on a project that will analyse syntactic structures in the Trinity Lancaster Corpus. The focus of the project is to develop a syntactic annotation scheme of spoken learner language and apply this scheme to the Trinity Lancaster Corpus, which is being compiled at Lancaster University in collaboration with Trinity College London. The aim of the project is to provide an annotation layer for the corpus that will allow sophisticated exploration of the morphosyntactic and syntactic structures in learner speech. The project will have an impact on both the theoretical understanding of spoken language production at different proficiency levels as well as on the development of practical NLP solutions for annotation of learner speech. More specific goals include:
- Identification of units of spoken production and their automatic recognition.
- Annotation and visualization of morphosyntactic and syntactic structures in learner speech.
- Contribution to the development of syntactic complexity measures for learner speech.
- Description of the syntactic development of spoken learner production.
The Trinity Lancaster Corpus of Spoken Learner English is providing multiple sets of data that can not only be used for validating the quality of our tests but also – and most importantly – to feedback important features of language that can be utilised in the classroom. It is essential that some of our research is focused on how Trinity informs and supports teachers in improving communicative competences in their learners and this is forming part of an ongoing project the research team are setting up in order to give teachers access to this information.
Trinity has always been focused on communicative approaches to language teaching and the heart of the tests is about communicative competences. The research team are especially excited to see that the data is revealing the many ways in which test takers use these communicative competences to manage their interaction in the spoken tests. It is very pleasing to see that not only does the corpus evidence support claims that the Trinity tests of spoken language are highly interactive but also it establishes some very clear features of effective communicative that can be utilised by teachers in the classroom.
The strategies which test takers use to communicate successfully include:
- Asking more questions
Here the test taker relies less on declarative sentences to move a conversation forward but asks clear questions (direct and indirect) that are more immediately accessible to the listener.
- Demonstrating active listenership through backchannelling
This involves offering more support to the conversational partner by using signals such as okay, yes, uhu, oh, etc to demonstrate engaged listenership.
- Taking responsibility for the conversation through their contributions
Successful test takers help move the conversation along by by creating opportunities with e.g. questions, comments or suggestions that their partner can easily react to.
- Using fewer hesitation markers
Here the speaker makes sure they keep talking and uses fewer markers such as er, erm which can interrupt fluency.
- Clarifying what is said to them before they respond
This involves the test taker checking through questions that they have understood exactly what has been said to them.
Trinity is hopeful that these types of communicative strategies can be investigated across the tests and across the various levels in order to extract information which can be fed back into the classroom. Teachers – and their learners – are interested to see what actually happens when the learner has the opportunity to put their language into practice in a live performance situation. It makes what goes on in the classroom much more real and gives pointers to how a speaker can cope in these situations.
More details about these points can be found on the Trinity corpus website and classroom teaching materials will be uploaded shortly to support teachers in developing these important strategies in their learners.
Also see CASS briefings for more information on successful communication strategies in L2.
With tensions over the current EU migrant crisis increasing, we at CASS thought it would be timely to highlight the importance of the language used in the debate about this humanitarian crisis. In this paper, by Paul Baker and Costas Gabrielatos, the authors analyse the construction of refugees and asylum seekers in UK press articles.
For readers who do not have access to Sage, you can find a final draft of the paper here free of charge. Please note that this version of the paper has the tables and figures at the end of the paper.
Analysing narratives in the Corporate Financial Information Environment. Transparent and effective communication between firms and the investment community is a key determinant of corporate success. Audited financial statements and associated narrative disclosures are among the main methods that firms use to communicate with investors and analysts. These disclosures combine with information from financial journalists and other market commentators to form the Corporate Financial Information Environment (CFIE). While a considerable body of work exists on financial narratives, research has been limited by the methods used for measuring the characteristics and quality of such disclosures. In particular, the need to hand-collect relevant data from firms’ annual reports and the subjectivity of textual scoring based on manual methods has restricted progress. Recent advances in computational and corpus linguistics provide a basis for undertaking more sophisticated analyses.
New resources are being added regularly to the new CASS: Briefings tab above, so check back soon.
Corpus Linguistics 2015 – CL2015 – is the largest conference of its kind and this year drew over 250 attendees from all over the world to present work outlining the state of Corpus Linguistics (CL) at large, leading-edge technology and methods, and setting the agenda for years to come.
Of particular interest to me was a small but important streak of enquiry running through the conference, which is also becoming more prevalent in CL as a whole. That is, a focus on corpora collected from online source such as blogs and social media (Elgesem & Salway 2015; Grieve, et al. 2015; Hardaker & McGlashan 2015; Knight 2015; Longhi & Wigham 2015; McGlashan & Hardaker 2015; Statache, et al. 2015). The Internet now enables great opportunities for the collection and interrogation of large amounts of data – big data, even – and the rapid compilation of specialised corpora in ways previously impossible.
I focus here on social media data, specifically data collected from Twitter. Sampling data from Twitter, like a lot of other online sources, offers the opportunity to collect what people are saying (the content of their posts; tweets) but also a huge amount of metadata about the date, time, user, shared content (e.g. hyperlinks, retweets), interactional information, etc. relating to those posts. As Corpus Linguists, we therefore get the data we sample for – posts containing the thing(s) we are interested in – as well as other social information about the content creators and their social networks that we may or may not be interested in. Indeed, concerns about the kinds of metadata included and attached to online post is an issue that has sparked a great deal of debate about the ethics of collecting and using publicly posted online content, though these concerns are not discussed here. Instead, the potential for online ethnography is explored. In order to do this, I pair familiar CL research methods with methods from Social Network Analysis (SNA) that are more explicitly focussed on social networks and examining the myriad ways people affiliate with each other.
Theory & Methods: Corpus-assisted Community Analysis (CoCoA)
Corpus-assisted Community Analysis (CoCoA) is a multimethodological approach to the study of online discourse communities that combines methods from Discourse Analysis (DA), CL, and SNA.
Corpus-assisted Discourse Analysis
I predominantly draw on Baker (2006) in my approach to corpus-assisted DA, seeing discourse in a Foucauldian sense as, forms of social practice; “practices which systematically form the objects of which they speak” (Foucault 1972: 49). Particularly, I am interested in the incremental effect of discourse. Baker suggests, “a single word, phrase or grammatical construction on its own may suggest the existence of a discourse” (2006: 13). However, in order to investigate how quantitatively typical or pervasive discourse is within a discourse community, numerous examples of linguistic instantiations of discourse are required to make a claim about its cumulative effect (ibid.). Following Baker, I argue here that corpora and CL techniques enable this kind of quantitative examination of discourse.
Social Network Analysis
SNA implements notions from graph theory for the formal modelling and describing the properties of relationships between objects of study such as people and institutions. A graph (or ‘sociogram’) is a representation of people or institutions of interest as ‘nodes’ and the relationships between them as a set of lines known as ‘edges’; a graph is built by representing “a set of lines [‘edges’] connecting points [‘nodes’]” (Scott 2013: 17). To interpret graphs, graph theory contributes “a body of mathematical axioms and formulae that describe the properties of the patterns formed by the lines [‘edges’]” (Scott 2013: 17). One of these axioms is ‘directionality’. Directed graphs can encode both symmetric and asymmetric relations (D’Andrea, et al. 2010: 12). Directed relationships are where nodes are connected by an edge that has a direction of flow from one node to another is known as asymmetric, as illustrated by the relations between A and C, and C and B in Fig. 1. Symmetric relationships are those in which an edge connects two nodes but is bidirectional – the direction of relation flows both ways – as illustrated by the relationship between A and B in Fig. 1. Directed relationships on Twitter include followership relations and the act of mentioning – i.e. including the handle (e.g. @CorpusSocialSci) – in tweets.
Undirected graphs represent identical, symmetric relationships between nodes which might be the result of nodes sharing reciprocal attitudes or “because they have a common involvement in the same activity” (Scott 2013: 17). Fig. 2 contains gives a graphical representation of an undirected graph.
Directed and undirected (‘ambient’) kinds of affiliation are both understood here as being distinct forms of discursively constructed social practices. Furthermore, I adopt the term ‘ambient affiliation’ from the work of Zappavigna on the use of social media in the formation of community and identity (Zappagigna 2012; Zappagigna 2013). Ambient affiliation is about the functionalities of social media platforms that enable users “to commune with others without necessarily engaging in direct conversational exchanges” (Zappagigna 2013: 223-4). Therefore, ambient affiliation is about people exhibiting the same behaviours or sharing the same qualities but without directly interacting with each other. This notion closely approximates to the notion of an ‘undirected’ graph. In developing the theory of ambient affiliation Zappavigna draws on Page’s work on hashtags. Page refers to hashtags as “a search term” (2012: 183). Hashtags – a string of characters (usually a word or short phrase) unbroken by spaces or non-alphabetic/non-numeric characters (excl. underscores ‘_’) preceded by ‘#’ (e.g. #YOLO) – are used a metadiscursive markers of the topic of a tweet. Page goes onto argue that, “the kind of talk which aggregate around hashtags […] involve multiple participants talking simultaneously about the same topic, rather than individuals necessarily talking with each other in dyadic exchanges that resemble a conversation” (2012: 196). As such, Page suggests that hashtags destabilise conventional adjacency pairs characteristic of many forms of human dialogue and give a new way for humans to interact on a topic of mutual interest.
I collected all tweets and retweets including the official hashtag of the Corpus Linguistics 2015 conference – #CL2015 – posted from the date of the first pre-conference workshop (20/07/2015) through until the final day of the conference (24/07/2015). To do this, I used the R based Twitter client ‘twitteR’ to access the Twitter API. The resulting data amounted to:
The tweets corpus contained around ~10,000 words in total.
The data contained some ‘noise’ mainly caused by other people using the same #CL2015 hashtag to talk about another event occurring during the period of the conference. However, as I will show in the analysis, the methods enable researchers to focus only on the communities they are interested in.
Tweets – what was being talked about?
To find out what people were talking about day-to-day, I created daily tweet corpora. With each of these daily corpora, I performed a keyword analysis using a reference corpus compiled using the remaining other days. So, for the tweets sent during the pre-conference workshop day (20/07/2015) I used the tweets sent during the rest of the conference (21/07/2015-24/07/2015) as a reference corpus, and so on. The resulting top 10 keywords for each day are given in the table below.
The keywords shown in each column outline the most distinctive topics tweeted about during the conference. Italics used here relate back to keywords in the table.
On day 1, the pre-conference workshops, including @antlab‘s pre-conference corpus tools brainstorming session and @stgries’s pre-conference #R workshop were popular topics of conversation in the smallest subsample of tweets for the week.
Top favourited tweet from day 1:
Conclusion from this morning's discussion – we need an R for Corpus Linguistics MOOC #cl2015
— Charlotte Taylor (@_ctaylor_) July 20, 2015
On day 2, more diverse topics start to emerge. Change became a theme, relating to Andrew Salway’s talk on discourse surrounding climate change but also relates to a talk given by Doug Biber on historical linguistic change in ‘uptight’ academic texts. Fireant, a new user-friendly tool for efficiently dealing with large databases developed by Laurence Anthony, was also unveiled to the CL masses on day 2, which prompted a flurry of excited tweets [keep track of Laruence’s Twitter page for release]. DOOM and misogyny also became topical following talks by Claire Hardaker and Mark McGlashan on the Discourse of Online Misogyny project. Finally, some excitement followed a paper given by Robbie Love and Claire Dembry about the new Spoken BNC2014. For those interested, keep track of the CASS website for spoken data grants later in the year.
Top favourited tweet from day 2:
— Paul Baker (@_paulbaker_) July 21, 2015
Day 3 saw another topic change focussing most prominently on Alison Sealey’s talk on the discursive construction of animals in the media, Sylviane Granger’s plenary on learner corpora, a talk on the public’s online reactin to the #HeForShe campaign given by Rosie Knight, and Jen Hughes’ talk on the application of EEG (‘Electroencephalography’) to the study of collocation as a cognitive phenomenon.
Top favourited tweet from day 3:
— Amanda Potts (@WatchedPotts) July 22, 2015
After 3 days of incredibly interesting talks, corpus linguists were about ready for their gala dinner on day 4. But before all the cheesecake, the CL2015 were excitedly tweeting about the all important poster session, Alison Duguid’s talk on class, the Geoffrey Leech tribute panel which included Charlotte Taylor’s paper on mock politeness and ‘bitchiness’ as well as Lynne Murphy and Rachele de Felice’s talk on the differential use of please in BrE and AmE, Alan Partington’s plenary speech on CADS; and papers given by Ruth Breeze, Amanda Potts, and Alex Trklja, on the application of CL methods to the study of a broad range of legal language.
Top favourited tweet from day 4:
— Paul Rayson (@perayson) July 23, 2015
Day 5 brought #CL2015 to a close but the number of tweets remained steady with health on the agenda with talks from Ersilia Incelli and Gillian Smith who both focussed partly on the construction of mental illness/health in the news. News also featured Monika Bednarek’s talk on news discourse and Antonio Fruttaldo’s analysis of news tickers. Other key topics related to Sylvia Jaworska and Anupam Nanda’s paper on the Corpus Linguistic analysis of Corporate Social Responsibility (CSR), Michaela Mahlberg’s work on the literature of Charles Dickens, and discussion of a corpus of Yahoo answers in the week’s penultimate panel on triangulating methodological approaches.
Top favourited tweet from day 5:
— UCRELResearchCentre (@UCREL_Lancaster) July 24, 2015
Approaching tweets in this way, it was possible to find out the most salient topics of each day. However, I was also interested in the retweeting behaviour of attendees.
Retweets – what was being talked about?
I looked at the top 10 most frequently retweeted tweets during the conference. Due to the intertextual nature of retweets – they are simply identical reposts of the same content – methods familiar to CL such as word frequency lists may not be as useful in their study. For example, if a few retweets are particularly frequently reposted, the most frequent words will be skewed by the content of the most frequent retweets. Instead, I suggest that retweets themselves should be conceptualised as being individual types in and of themselves that require more qualitative approaches to their interpretations (at least in this context). The top 10 most frequently retweeted tweets including the #CL2015 hashtag are given below:
|1||RT @EstrategiasEc: Concluimos este viernes con exitoso proceso de postulación @ECLideres VI Prom. #CL2015 con auspicio de @ucatolicagye. ht…||22/07/2015||218|
|2||RT @perayson: To access the new HT semantic tagger from the @SAMUELSProject see http://t.co/5LFWH8YGAH and http://t.co/BPxcC8pNNK #CL2015||23/07/2015||15|
|3||RT @UCREL_Lancaster: The #CL2015 abstract book is now available to download from the conference website http://t.co/px9hh3mMNe||21/07/2015||13|
|4||RT @duygucandarli: Important take-away messages about corpus research in Biber’s plenary talk at #CL2015! http://t.co/xm87Uo1umZ||21/07/2015||11|
|5||RT @lynneguist: Alan Partington looking at how quickly language changes in White House Press Briefings… #CL2015 http://t.co/jeVjvC8Ym3||23/07/2015||10|
|6||RT @CorpusSocialSci: .@_paulbaker_ reflecting on a number of approaches to the same data at the Triangulation panel at #CL2015 http://t.co/…||24/07/2015||10|
|7||RT @CorpusSocialSci: .@vaclavbrezina introduces Graphcoll, a new visualisation tool for collocational networks #CL2015 http://t.co/PM5FxS5N…||22/07/2015||9|
|8||RT @_ctaylor_: It’s a myth that reference corpora have to larger than target corpus says @antlabjp #cl2015||22/07/2015||7|
|9||RT @Loopy63: #CL2015 Call for papers for Intl. Conference on statistical analysis of textual data 2016 in Nice, France: http://t.co/3JpcAa…||23/07/2015||7|
|10||RT @vaclavbrezina: A great use of #GraphColl by @violawiegand – #CL2015 poster presentation @TonyMcEnery @StephenWattam http://t.co/uwlMGUYâ€¦||24/07/2015||7|
The most frequent retweet was regarding a Latin American Youth Leadership programme that shared the same #CL2015 hashtag [nb. For next year, Corpus Linguistics conference organisers…]. As you will notice, this retweet occurred on 22/07/2015 but as retweets and tweets are dealt with exclusively, the retweet does not interfere with the keyword analysis done for the same day on the tweets.
What do the most frequent retweets highlight? Free tools (GraphColl, HT semantic tagger), free resources (abstract book), plenary talks and more conferences.
With a general idea of what people are talking about and sharing using the #CL2015 hashtag, I was interested to examine the overall activity around #CL2015 and the emergence of discourse communities.
In terms of tweets the gif below shows how relationships developed over the course of the conference. Every node represents a Twitter account that posted a tweet containing #CL2015 during the period of data collection. The size of these nodes is dictated by their ‘degree’, or its number of edges. More edges = larger node. The colour of the nodes is determined by ‘betweenness centrality’, which indicates how central a node is in a network. Nodes with high betweenness centrality help the speed of transfer of information through networks as they help create the shortest distance between other nodes in the network. Nodes with high betweenness centrality are coloured red, a medium betweenness centrality is yellow, and low betweenness centrality is blue. Nodes with intermediary colours (orange, green) represent those that have a betweenness centrality somewhere between low and medium or between medium and high. Finally, the colour and size of edges is dictated by ‘weight’. In this example, weight is dictated by the frequency of tweets that exist between nodes. Thick red edges between nodes represent nodes that send tweets to each other frequently, or one node mentions another frequently. Thin blue edges represent low frequency mentioning relationships. Yellow are medium. Again, blended colours represent intermediary frequencies and thus, in this case, weight.
The tweets network shows that @CorpusSocialSci was – perhaps unsurprisingly – the most prolific and central account in the #CL2015 network. It had the most connections and joined the most individual accounts together. But other users were very active in helping to disseminate information more widely, which are shown by those nodes in yellow and orange. The accounts on the periphery of the network are good examples of ambient affiliation. They use #CL2015 to affiliate but do not directly engage with others by mentioning other users. Moreover, the gif attempts to show the evolution and growth of the network over time but also shows that each day new topics and networks of interaction relating to those new topics emerged daily. As talks (and news of talks in the network) became topical, people tweeted and shared ideas and notes relevant to those talks. An example of this is the emergence of fireant on 21/07/2015. When introduced to delegates, an ad hoc online discourse community formed to spread the news of a new tool, add new information and to channel their enthusiasm back to source.
|RachelleVessey||2015-07-21 16:50:43||Excellent end to the first day of #CL2015- FireAnt looks like a fantastic programme @antlabjp @DrClaireH can’t wait to try it out!|
|SLGlaas||2015-07-21 16:54:13||Stupidly excited about #Fireant from @antlabjp #CL2015|
|CorpusSocialSci||2015-07-21 16:54:43||Everyone is eagerly wondering when FireAnt will be available. @antlabjp’s answer is hopefully within the next few months. #CL2015|
|Rosie_Knight||2015-07-21 16:56:40||Amazing talk about FireAnt- can’t wait to use this on my #HeForShe data! @DrClaireH @antlabjp @Mark_McGlashan #CL2015|
The retweets network again shows that @CorpusSocialSci was – and, again perhaps unsurprisingly – at the centre of #CL2015 retweeting activity. The retweet network gif shows 2 discrete networks. The right hand network shows activity at the CL conference, the left hand network shows the retweeting behaviour of the Latin American Youth Leadership programme mentioned above. Avid conference tweeters may have noticed when keeping track of the #CL2015 hashtag. The left hand network – a graphic representation of the most retweeted tweet containing #CL2015 shown above – shows 218 users retweeting a single central account. In this network there is no interaction between the users engaged in retweeting this user. This kind of network formation is extremely typical of users retweeting news stories on Twitter. The right hand network, however, shows a great deal of mutual retweeting, whereby users are engaged on a prolonged basis in sharing each others’ tweets and forming a network of sharing and resharing.
Integrating methods from CL and SNA offers some really interesting possibilities for the analysis of large amounts of social data. Here, I have used keyword analysis to find the most salient topics for each day of the conference, used those topics to find and visualise small but coherent discourse communities, and situated those communities within the wider #CL2015 social network.
m.mcglashan(Replace this parenthesis with the @ sign)lancaster.ac.uk
Baker, P. (2006) Using Corpora in Discourse Analysis. London: Continuum.
D’Andrea, A., Ferri, F. & Grifoni, P. (2010). An overview of methods for virtual social network analysis. In: A. Abraham, A.-E. Hassanien, & V. Sná el (eds.). Computational Social Network Analysis. London: Springer London, pp. 3–26.
Elgesem, D. & Salway, A. (2015) Traitor, whistleblower or hero? Moral evaluations of the Snowden affair in the blogosphere. In Formato, F. & Hardie, A. (Eds.) Corpus Linguistics 2015 Abstract Book. Paper presented at Corpus Linguistics 2015, Lancaster. Lancaster University. pp 99-101
Foucault, M. (1972) The Archaeology of Knowledge. London: Tavistock.
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Hardaker, C. & McGlashan, M. (2015) Twitter rape threats and the discourse of online misogyny (DOOM): from discourses to networks. In Formato, F. & Hardie, A. (Eds.) Corpus Linguistics 2015 Abstract Book. Paper presented at Corpus Linguistics 2015, Lancaster. Lancaster University. pp. 154-6
McGlashan, M. & Hardaker, C. (2015) Twitter rape threats and the discourse of online misogyny (DOOM): using corpus-assisted community analysis (COCOA) to detect abusive online discourse communities. In Formato, F. & Hardie, A. (Eds.) Corpus Linguistics 2015 Abstract Book. Paper presented at Corpus Linguistics 2015, Lancaster. Lancaster University. pp. 234-5
Page, R. (2012). The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags. Discourse & Communication. 6 (2). p.pp. 181–201.
Scott, J. (2013). Social Network Analysis. 3rd Ed. London: Sage.
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Zappavigna, M. (2012). Discourse of Twitter and social media. London: Continuum.
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Hate Speech: Crime against Muslims. The notion of ‘hate crime’ might conjure up an image of premeditated violence perpetrated by a bigoted thug. But in reality, a majority of so-called ‘hate crimes’ are committed with little aforethought by very ordinary people in ordinary circumstances and involve a verbal assault rather than physical attack. This briefing provides the key research findings from the project as it provided important groundwork for a CASS research project launched in 2014 on The management of hateful invective by the courts.
New resources are being added regularly to the new CASS: Briefings tab above, so check back soon.
How wonderful it is to get to the inner workings of the creature you helped bring to life! I’ve just spent a week with the wonderful – and superbly helpful – team at CASS devoting time to matters on the Trinity Lancaster Spoken Corpus.
Normally I work from London situated in the very 21st century environment of the web – I plan, discuss and investigate the corpus across the ether with my colleagues in Lancaster. They regularly visit us with updates but the whole ‘system’ – our raison d’etre if you like – sits inside a computer. This, of course, does make for very modern research and allows a much wider circle of access and collaboration. But there is nothing like sitting in the same room as colleagues, especially over the period of a few days, to test ideas, to leap connections and to get the neural pathways really firing.
It’s been a stimulating week not least because we started with the wonderful GraphColl, a new collocation tool which allows the corpus to come to life before our eyes. As the ‘bubbles’ of lexis chase across the screen searching for their partners, they pulse and bounce. Touching one of them lights up more collocations, revealing the mystery of communication. Getting the number right turns out to be critical in producing meaningful data that we can actually read – too loose and we end up with a density we cannot untangle; the less the better seems to be the key. It did occur to me that finally language had produced something that could contribute to the Science Picture Library https://www.sciencephoto.com/ where GraphColl images could complement the shots of language activity in the brain. I’ve been experimenting with it this week – digging out question words from part of the corpus to find out how patterned they are – more to come.
We’ve also been able to put more flesh on the bones of an important project developed by Vaclav Brezina – how to make the corpus meaningful for teachers (and students). Although we live in an era where the public benefit of science is rightly foregrounded, it can be hard sometimes to ‘translate’ the science and complexity of the supporting technology so that it is of real value to the very people who created the corpus. Vaclav has been preparing a series of extracts of corpus data that can come full circle back into the classroom by showing teachers and their students the way that language works – not in the textbooks but in real ‘lingua franca’ life. In other words, demonstrating the language that successful learners use to communicate in global contexts. This is going to be turned into a series of teaching materials with the quality and relevance being assured by crowdsourcing teaching activities from the teachers themselves.
Meanwhile I am impressed by how far the corpus – this big data – is able to support Trinity by helping to build robust validity arguments for the GESE test. This is critical in helping Trinity’s core audience – our test takers – to understand why should I do this test, what will the test demonstrate, what effect will it have on my learning, is it fair? All in all a very productive week.