#computer mediated communication

[allthingslinguistic]

How Twitter is helping the Scots language thrive in the 21st century

The platform has provided a forum for the written Scots language to evolve that wasn't possible before the advent of social media.

An article in The Conversation about Scots Twitter and Computer-Mediated Communication (CMC). Excerpt: 

Scots on Twitter is a fascinating source of evidence about how aware people are of the subtle ways their speech differs from other people’s, and the creative ways they find to represent this.

Depending on exactly where someone comes from, their (spoken) Scots will include different sounds, words and sentence structures. We see this represented in Scots CMC. In Scots dictionaries, the word equivalent to English “can’t” is generally spelled in one of two ways, reflecting a traditional pronunciation difference: “canna” in the north, and “cannae” in the south. Indeed, a search for “canna” on Twitter finds tweets from the north east.

However, the spelling “cannae” appears to be quite rare in the southern Scots CMC. Instead, spellings like “canny” appear to be more common.

In Sadie Ryan’s research on the CMC of Glaswegian pre-teens, other spellings used included “cany”, “canni”, “cani” and “kani”. 

Read the whole thing.

[allthingslinguistic]

How Information Got Re-Invented - Issue 51: Limits - Nautilus

With his marriage to Norma Levor over, Claude Shannon was a bachelor again, with no attachments, a small Greenwich Village apartment,…

An article about the invention of information theory in Nautilus. Excerpt: 

Information was something guessed at rather than spoken of, something implied in a dozen ways before it was finally tied down. Information was a presence offstage. It was there in the studies of the physiologist Hermann von Helmholtz, who, electrifying frog muscles, first timed the speed of messages in animal nerves just as Thomson was timing the speed of messages in wires. It was there in the work of physicists like Rudolf Clausius and Ludwig Boltzmann, who were pioneering ways to quantify disorder—entropy—little suspecting that information might one day be quantified in the same way. Above all, information was in the networks that descended in part from the first attempt to bridge the Atlantic with underwater cables. In the attack on the practical engineering problems of connecting Points A and B—what is the smallest number of wires we need to string up to handle a day’s load of messages? how do we encrypt a top-secret telephone call?—the properties of information itself, in general, were gradually uncovered. [...]

Each year that Shannon placed a call, he was less likely to speak to a human operator and more likely to have his call placed by machine, by one of the automated switchboards that Bell Labs grandly called a “mechanical brain.” In the process of assembling and refining these sprawling machines, Shannon’s generation of scientists came to understand information in much the same way that an earlier generation of scientists came to understand heat in the process of building steam engines. [...]

Where Nyquist used the vague concept of “intelligence” and Hartley struggled to explain the value of discarding the psychological and semantic, Shannon took it for granted that meaning could be ignored. In the same way, he readily accepted that information measures freedom of choice: What makes messages interesting is that they are “selected from a set of possible messages.” It would satisfy our intuitions, he agreed, if we stipulated that the amount of information on two punch cards doubled (rather than squared) the amount of information on one, or that two electronic channels could carry twice the information of one.That was Shannon’s debt. What he did next demonstrated his ambition. Every system of communication—not just the ones existing in 1948, not just the ones made by human hands, but every system conceivable—could be reduced to a radically simple essence.

• The information source produces a message. • The transmitter encodes the message into a form capable of being sent as a signal. • The channel is the medium through which the signal passes. • The noise source represents the distortions and corruptions that afflict the signal on its way to the receiver. • The receiver decodes the message, reversing the action of the transmitter. • The destination is the recipient of the message.

The beauty of this stripped-down model is that it applies universally. It is a story that messages cannot help but play out—human messages, messages in circuits, messages in the neurons, messages in the blood. You speak into a phone (source); the phone encodes the sound pressure of your voice into an electrical signal (transmitter); the signal passes into a wire (channel); a signal in a nearby wire interferes with it (noise); the signal is decoded back into sound (receiver); the sound reaches the ear at the other end (destination).

In one of your cells, a strand of your DNA contains the instructions to build a protein (source); the instructions are encoded in a strand of messenger RNA (transmitter); the messenger RNA carries the code to your cell’s sites of protein synthesis (channel); one of the “letters” in the RNA code is randomly switched in a “point mutation” (noise); each three-“letter” code is translated into an amino acid, protein’s building block (receiver); the amino acids are bound into a protein chain, and the DNA’s instructions have been carried out (destination).

Those six boxes are flexible enough to apply even to the messages the world had not yet conceived of—messages for which Shannon was, here, preparing the way. They encompass human voices as electromagnetic waves that bounce off satellites and the ceaseless digital churn of the Internet. They pertain just as well to the codes written into DNA. Although the molecule’s discovery was still five years in the future, Shannon was arguably the first to conceive of our genes as information bearers, an imaginative leap that erased the border between mechanical, electronic, and biological messages.

First, though, Shannon saw that information science had still failed to pin down something crucial about information: its probabilistic nature. When Nyquist and Hartley defined it as a choice from a set of symbols, they assumed that each choice from the set would be equally probable, and would be independent of all the symbols chosen previously. It’s true, Shannon countered, that some choices are like this. But only some. For instance, a fair coin has a 50-50 chance of landing heads or tails. This simplest choice possible—heads or tails, yes or no, 1 or zero—is the most basic message that can exist. [...]

The new unit of Shannon’s science was to represent this basic situation of choice. Because it was a choice of 0 or 1, it was a “binary digit.” In one of the only pieces of collaboration Shannon allowed on the entire project, he put it to a lunchroom table of his Bell Labs colleagues to come up with a snappier name. Binit and bigit were weighed and rejected, but the winning proposal was laid down by John Tukey, a Princeton professor working at Bell. Bit.

Read the whole thing.

[attorneyandlawyer]

COMPUTER MEDIATED COMMUNICATION AND ISSUES OF TRUST: A STUDY OF THE USE OF SOCIAL NETWORKING SITE AMONGST STUDENTS OF CANTERBURY HIGH SCHOOL, AJAH-LEKKI LAGOS

COMPUTER MEDIATED COMMUNICATION AND ISSUES OF TRUST: A STUDY OF THE USE OF SOCIAL NETWORKING SITE AMONGST STUDENTS OF CANTERBURY HIGH SCHOOL, AJAH-LEKKI LAGOS

COMPUTER MEDIATED COMMUNICATION AND ISSUES OF TRUST: A STUDY OF THE USE OF SOCIAL NETWORKING SITE AMONGST STUDENTS OF CANTERBURY HIGH SCHOOL, AJAH-LEKKI LAGOS

Abstract:

There are a number of barriers to trust in the online environment. Despite the awareness of online dangers, young adults take risk on posting personal information on sensitive personal and family information. Trust is crucial to…

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[sharmaheena850]

IT Ministry mulls third notice to WhatsApp; may insist on message trail

The IT ministry is mulling sending a third notice to WhatsApp asserting that it bring in 'traceability' of messages on its platform, even as the American firm has maintained that doing so will impact end-to-end encryption and violate privacy of users.

According to government sources, an internal discussion was held on the issue and a third letter could now be sent to WhatsApp in the next 7-10 days.

The IT ministry feels that technical solutions can be found to trace the originator of a message, without actually compromising end-to-end encryption, said a government official, who did not wish to be named.

Citing examples of potential technical solutions that could be brought to the table, the official said traceability can be brought in for a message that has been forwarded to, say, a high number of users within a short span of time. Read More

Article Source >> Business Standard

[chaberkowepole]

As a linguist I am so excited because of the new colour feature

Like, how will it affect the communication standards on this website? Will it develop special uses? Special grammar? Will it be used emphatically? Suprasegmentally? Will certain colour alignments develop meaning? So many questions and only time can tell which systems will or will not develop. Bring it on!

[tumblinguistics]

In this study, I aim to discuss the innovative linguistic practices present on the popular social media website, Tumblr. I take both a qualitative and quantitative approach in doing this, firstly discussing linguistic variation that I have seen on

Some folks seem to be unable to find my dissertation on Tumblrspeak so here it is, for the curious. Citation info is on the academia.edu site. I still get a LOT of questions regarding how to cite it so: Author: Harley Grant Year: 2015 Institution: University of Glasgow

[tech-know-lo-gy]

ENGL 4740 COMPUTER APPLICATIONS IN LANGUAGE STUDIES CMC RESEARCH PAPER

Title: An Analysis of Negotiation of Meaning in Twitter Interactions

Introduction

Social networking sites have become the most used communication tool and have shown an increased growth for the past decade. A prime example of this growth is a micro-blogging service, Twitter. Twitter, which was created in 2006 and was launched in the same year has become one of the most popular microblogging services. In just a few years, it has over 200 million users and produces 110 million tweets (i.e., 140-character text messages) every day (Lin, Han, Ramsay & Fang, 2012).

However, recently, the text limit has been upgraded which enables Twitter users to “tweet” up to 280 characters. This microblogging site allows users to write short messages and upload them to be viewed by their online friends, known as followers, in real time. Compared to traditional blogging, microblogging focusing more on speed and conciseness, allows users to update on things happening “right now” (Lin, Han, Ramsay & Fang, 2012). Based on several studies done, microblogs users mainly use social media as a tool to describe their daily routines, voicing out their everyday thoughts, feelings, carrying out conversations, reporting news, and sharing information.

Purpose of Study

There is some research that has been done on Twitter concerning many aspects of life. Some of the aspects are the usage, the positive and negative effects, personality traits, relational factors, politics-cross ideology and many more. To explore more on Twitter in relation to computer-mediated communication, we focus our study on the negotiation of meaning happening in the interaction between Twitter users. The purpose is to find out the pattern of negotiation of meaning among the Twitter users. It is also to find out whether there is a clear understanding of meaning among Twitter users.  

Description of Framework for Analysis

The framework for analysis will be on the negotiation of meaning which it can be defined as an approach or a process of a speaker to achieve a clear understanding on the interactions with other speakers. Based on Patterson and Trabaldo (2006) taxonomies that have been developed and adopted by them, there are 10 types of negotiation of meaning for coding purposes. The taxonomies that can be applied on the interaction data are confirmation check, clarification request, vocabulary request, comprehensive check, reply confirmation, reply clarification, reply vocabulary, reply comprehension, elaboration and lastly correction or self-correction. Three categories were then added by Akayoglu and Altun (2009) which are confirmation, elaboration request and reply elaboration to facilitate in understanding discourse clearly.  

Research Questions

Throughout this research, two research questions were stated as follows:

What are the types of negotiation of meaning that emerge in Twitter interactions?

Which types of negotiation of meaning dominate the most in Twitter interactions?

Description of Corpus/Data

We have taken a corpus from the fourth most used social media in the world which is Twitter. As we were assigned to analyze a corpus that has at least 1000 words, we decided to use a thread from @aqileizzat Twitter status update which contains 1788 words. The corpus taken was written in both English and Malay.  It was uploaded on 28th October 2017 and has gained almost 10,000 retweets and 6,000 likes from Twitter users. As the problem on Lesbian Gay Bisexual and Transgender (LGBT) is getting rampant all over the world, @aqileizzat voiced out his opinion by posting a status “why orang Islam still nak support LGBT when it's clearly contradict with your own faith Haram tetap Haram no matter what your reasoning is”. 

Within a day, this Twitter status alone has attracted many responses from other Twitter users which both criticizing and praising his tweet. Based on this tweet, we have taken responses from 52 Twitter users. This two-way conversation consists numbers of questions, clarifications as well as exclamations which enable us to analyze it using Patterson and Trabaldo theory on negotiation on meaning.

Significance of Study 

This study is designed to contribute to the understanding of negotiation of meaning in Twitter interactions as users are using Twitter on-the-go and continuously. Thus, they can never neglect the use of negotiation of meaning through the interaction via Twitter and both of them are inseparable. Based on this theory by Patterson and Trabaldo, this study provides an insight for the future researchers upon conducting a research on any type of computer-mediated communication. 

Students, as well as teachers, are the most likely users of this mode of computer-mediated communication (CMC). Therefore, pedagogically, in analyzing negotiation of meaning through Twitter interactions, it can be conducive to both students and teachers whose studying in this area of linguistics. Discussion can be held on Twitter when teachers tweet a discussion related to the subject being taught. Since students nowadays are very much engaging with Twitter, they are most likely will be interested to join discussions on Twitter.

Literature Review

Findings (Answers to the Research Questions)

Based on the first research question, we have found 8 types of negotiation of meaning which are clarification request, confirmation, correction, elaboration, reply clarification, reply confirmation, reply vocabulary and vocabulary request.   Meanwhile, the types of negotiation of meaning that dominates the most in Twitter interactions are clarification request and elaboration. The frequencies of each type are 24 and 15 respectively.

Discussion & Conclusion

Based on our findings, it has been stated that clarification request is the most type of negotiation of meaning that is being used by the Twitter users. According to Patterson and Trabaldo, clarification request is used to ask for clarification from the given statement of a speaker. It shows that the understanding of meaning in a two-way communication can be achieved with this type of negotiation of meaning.

Apart from that, ‘elaboration’, being the second frequent type of negotiation of meaning being used in the analyzed corpus, also contributes to the understanding of the meaning of interaction between Twitter users. Elaboration is used by a speaker to elaborate his or her previous statement, either he or she is being asked or not being asked by other users.

The other types of negotiation of meaning also help in achieving the understanding of meaning in Twitter interaction. For instance, the use of correction helps the users to correct their statements, vocabularies or spelling.  Other types of negotiation of meaning that has been mentioned in our findings also contribute to the continuous discussion, as well as helping in the understanding of meaning among Twitter users.

In parallel to our framework, when there is interaction, there is a negotiation of meaning. The communication among Twitter users will be meaningless without the negotiation of meaning, as it is the tool for the meaning to be understood. Without negotiation of meaning, the message that is trying to be portrayed by a speaker might be misinterpreted or worse yet, the meaning is unable to be portrayed.

References

Qiu, L., Lin, H., Ramsay, J. and Yang, F. (2012). You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, [online] 46(6), pp.710-718. Available at: http://www.sciencedirect.com/science/article/pii/S009265661200133X.

Junco, R., Heiberger, G., and Loken, E. (2010). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, [online] 27(2), pp.119-132. Available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2729.2010.00387.x/abstract.

Liang, H. and Fu, K. (2016). Information Overload, Similarity, and Redundancy: Unsubscribing Information Sources on Twitter. Journal of Computer-Mediated Communication, [online] 22(1), pp.1-17. Available at: http://onlinelibrary.wiley.com/doi/10.1111/jcc4.12178/epdf.

Himelboim, I., McCreery, S. and Smith, M. (2013). Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter. Journal of Computer-Mediated Communication, [online] 18(2), pp.40-60. Available at: http://onlinelibrary.wiley.com/doi/10.1111/jcc4.12001/full.

Samani, E., Nordin, N., Mukundan, J. and Samad, A. (2015). Patterns of Negotiation of Meaning in English as Second Language Learners’ Interactions. Advances in Language and Literary Studies, [online] 6(1). Available at: http://www.journals.aiac.org.au/index.php/alls/article/view/607/535.

Marriott, R. and Torres, P. (2009). Handbook of research on e-learning methodologies for language acquisition. Hershey: Information Science Reference.

Hong, L., Convertino, G. and Chi, E. (2011). Language Matters in Twitter: A Large Scale Study. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. [online] Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.5773&rep=rep1&type=pdf [Accessed 12 Nov. 2017].

[welcometothefuturetense]

In class activity response

My partner and I we were given an excerpt from a Cambridge University Press booklet targeting reading writing skills. We were task to enhance the activities in the booklet by incorporating a Computer Mediated Communication activities.

My partner and I discussed that for our first task we would use a blog as a means of developing both reading and writing skills. The students would have to write about what their dream job was when they were a kid and why. They would then write about what their dream is now and why? Students would then have to comment on one chosen post and respond. We thought the use of blogspot, wordpress or even tumblr may work for this task. 

We then decided that task two would be a skype session focusing on listening and note taking skills. The Skype session would focus around Guinness World Record holders in the student's respective countries. Students must research prior and summarise what they have learnt. They will then report back to their partner while they listen and take notes. 

The third and final task would be a group effort with each group creating their own grammar worksheet to share with the class. This task would focus on communication between the groups and comprehension of grammar.