Questions related to Communication Theory
I am looking for French native speaker scholar on Communication theories (Political Communication, International Communication) to apply for a national funding program that support a book translation from Chinese to French (could be from Chinese to English to French). Please contact me if anyone is interested.
I am looking for free to publish communication theory journals that are peer reviewed with fast publication and peer review process. Please help! Thank you!
I am a needy student who is searching for resources for my dissertation about Theory of Communication. I am interested in works/articles/books about: communication models, human values, interpersonal communication. Is there anyone who's a specialist in the field and is able to help? I can explain in more detail what I'm writing about if necessary. Thank you!
Due to the development of information and communication technologies, the features of the communication parties have changed, and the Lasswell model is no longer representative of the communication process, especially with the emergence of social media and the multiplicity and diversity of uses. So I think it is worth asking about the theories that would be more qualified to serve communication research. That's why II posed the question:
What are the communication theories that you rely on in your research recently?
I have been pondering about the relationship between these two important topics of our data-driven world for a while. I have bits and pieces, but I have been looking forward to find a neat and systematic set of connections that would somehow (surprisingly) bind them and fill the empty spots I have drawn in my mind for the last few years.
In the past, while I was dealing with multi-class classification problem (not so long ago), I have come to realize that multiple binary classifications is a viable option to address this problem through using error correction output coding (ECOC) - a well known coding technique used in the literature whose construction requirements are a bit different than classical block or convolutional codes. I would like to remind you that grouping multiple classes in two superclasses (a.k.a. class binarization) can be addressed in various ways. You can group them totally randomly which does not dependent on the problem at hand or based on a set of problem-dependent constraints that can be derived from the training data. One way I like the most stays at the intersection point of information theory and machine learning. To be more precise, class groupings can be done based on the resultant mutual information to be able to maximise the class separation. In fact, the main objective with this method is to maximise class separation so that your binary classifiers expose less noisy data and hopefully result in better performance. On the other hand, ECOC framework calls for coding theory and efficient encoder/decoder architectures that can be used to efficiently handle the classification problem. The nature of the problem is not something we usually come across in communication theory and classical coding applications though. Binarization of classes implies different noise and defect structures to be inserted into the so called "channel model" which is not common in classical communication scenarios. In other words, the solution itself changes the nature of the problem at hand. Also the way we choose the classifiers (such as margin-based, etc) will affect the characterization of the noise that impacts the detection (classification) performance. I do not know if possible, but what is the capacity of such a channel? What is the best code structure that addresses these requirements? Even more interestingly, can the recurrent issues of classification (such as overfitting) be solved with coding? Maybe we can maintain a trade-off between training and generalization errors with an appropriate coding strategy?
Similar trends can be observed in the estimation theory realm. Parameter estimations or in the same way "regression" (including model fitting, linear programming, density estimation etc) can be thought as the problems of finding "best parameters" or "best fit", which are ultimate targets to be reached. The errors due to the methods used, collected data, etc. are problem specific and usually dependent. For instance, density estimation is a hard problem in itself and kernel density estimation is one of its kind to estimate probability density functions. Various kernels and data transformation techniques (such as Box-Cox) are used to normalize data and propose new estimation methods to meet today's performance requirements. To measure how well we do, or how different distributions are we again resort to information theory tools (such as Kullback–Leibler (KL) divergence and Jensen-Shannon function) and use the concepts/techniques (including entropy etc.) therein from a machine learning perspective. Such an observation separates the typical problems posed in the communication theory arena from the machine learning arena requiring a distinct and careful treatment.
Last but not the least, I think that there is deep rooted relationship between deep learning methods (and many machine learning methods per se) and basic core concepts of information and coding theory. Since the hype for deep learning has appeared, I have observed that many studies applying deep learning methods (autoencoders etc) for decoding specific codes (polar, turbo, LDPC, etc) claiming efficiency, robustness, etc thanks to parallel implementation and model deficit nature of neural networks. However, I am wondering the other way around. I wonder if, say, back-propagation can be replaced with more reasonable and efficient techniques very well known in information theory world to date.Perhaps, distortion theory has something to say about the optimal number of layers we ought to use in deep neural networks. Belief propagation, turbo equalization, list decoding, and many other known algorithms and models may have quite well applicability to known machine learning problems and will perhaps promise better and efficient results in some cases. I know few folks have already began searching for neural-network based encoder and decoder designs for feedback channels. There are many open problems in my oppinion about the explicit design of encoders and use of the network without the feedback. Few recent works have considered various areas of applications such as molecular communications and coded computations as means to which deep learning background can be applied and henceforth secure performances which otherwise cannot be achieved using classical methods.
In the end, I just wanted to toss few short notes here to instigate further discussions and thoughts. This interface will attract more attention as we see the connections clearly and bring out new applications down the road...
I put up this question at RG in order to find out what is being studied about the effects on people of social media memes as they attempt to find reliable information regarding social media memes.
In my original data set about the addictive power of memes to shape memory storage and alter personality, I was mainly looking at political memes.
It may be also important to study the effects of memes upon people's ability to find verifiable information. So please post any studies that you are aware of so that we can compile these in one place. I hope this inspires some study because I already know the power of memes from my past work on rhetoric, communication theory, and meme addictive behavior.
Here are an initial couple of links to studies which I have not read as yet, but which may be of interest. Check the bibliographies or Works Citeds, as well.
Social Media Reigned by Information or Misinformation About COVID-19: A Phenomenological Study
Social Sciences & Humanities Open Online journal:
MIT Psychologists study:
Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy nudge intervention
Peer-edited Polish Journal
SOMEBODY TO BLAME: ON THE CONSTRUCTION OF THE OTHER IN THE CONTEXT OF THE COVID-19 OUTBREAK
CfP: Communication Association of Eurasian Researchers Conference 'Building Bridges: Internationalizing Communication Theory, Practice, and Education', LCC International University 🇱🇹, June 27-29, 2020
Deadline: January 31, 2020
A colleague and I are working on developing a communication theory. This of course synthesizes prior literature, but we also have survey data we gathered to help us "make the case" for how the theory works and how it is applicable. Does anyone have a good example of how this is done? In particular, we're looking for how to do a "method section" when in theory development there typically isn't a formal section.
If you are familiar with the Lucky Me: HapagUsapan commercial series, I would like to study these ads from a speech communication perspective and how it is perceived by either nuclear families or single parent families.
Hi! I am currently in the process of creating a thesis topic and I'm kind of lost as to what theories I can use. I am majoring communication
So there is this 'two communities' theory / metaphor arguing that academics and policy makers are from separate communities, with distinct languages, values, and reward system, and that leads to limited knowledge use (Caplan 1979, Dunn 1980).
Although criticized by many (e.g. Bogenschneider and Corbett 2010, Jacobson 2007) it still in my opinion is a good story / starting point for analyzing determinants / context of knowledge use in public administration.
I wonder if you could point me to some other examples of alternative theories / metaphors that could serve the same purpose. Let me specify that I'm not asking for sources enlisting factors / determinants of knowledge / evaluation use or models consisting - again - of factors, but something more like a story / perspective (sth like two communities:)
I wonder if the communication model mentioned above is used outside Germany. In Germany, it is both well known and in widespread everyday use.
In wikipedia, one may find this reference:
I am asking because I want to connect our own model as presented at ESPCH conferences with the Four Sides model and up to now wonder, how to do....
Kind regards, and thanks in advance, should anyone be able to give me a hint,
Any suggestions with what communication theories to use on a reception analysis study of flood risk messages by a government council? I am also looking at the messages receivers' Knowledge, Attitude and Practice (with regards to their socio-demographics), on their motivations on acting upon the flood risk messages (i.e. preparation, evacuation). Thanks!
I'm not looking to code messages categorically (e.g., Brown & Levinson's typology of politeness strategies), but rather to have participants rate their own degree of politeness utilized in a given relationship over time. So far, I've not been able to track down any multi-item measures of self-reported global politeness. Any help or references would be appreciated!
My field is that of communication, so when I talk about reliability and credibility I talk about both concepts in light of a message, of a source or as an outcome of either.
Of course there is a clear distinction in theory. Do people (while participating in an experiment or filling out a survey) really make that theoretical distinction? And do they still in daily life? And is that distinction measurable, or are they simply two sides of the same coin, meaning that they are perhaps two parts of a multidimensional construct?
Requires some communication theories to be applied to study of English and Indian Literature
I am going to conduct Analog Communication theory Lab next semester for Btech students. In this lab we are designing Analog Communication Transmitter and receivers.
1. Amplitude Modulation TX / RX
2. Frequency Modulation TX / RX
and other such circuits.
As far as I have refered I could not find any good book/material which gives detailed design steps/derivations for these circuits.
Kindly let me know a good book/material for this.
I am planning to design simple circuits with transistors.
Let's use an example. We have a function y = f(x), in which x is the input (the probability) and y is the output (the entropy). If we change y in y', can we find an x' such that f(x') = y'?
In other words, I know that when p changes, H changes; is it possible the opposite, such that if H changes, p changes?
I am studying “AL JAZEERA AND THE ARAB SPRING - Framing of the Uprisings in Egypt and Bahrain on the Al Jazeera English and Arabic websites”.
I need a good academic book which has done a similar study on, let’s say CNN covering a conflict in the Middle East so that I can imitate it.
Transdisiciplinarity requires elements such as collective thinking, integration, collaboration, cooperation and participation of actors or scientists for knowledge generation and management that can solve real life and complex problems. What will be the ideal theory that this can be based on? Social capital theory? sociology of scientific knowledge? Actor network theory? Communication theory?
It might all be relevant to the study, but which can be the best fit.
My research is to investigate the best ways of dealing with sexual harassment at workplace and the sample universe is female journalists in electronic media. Which theory best suits the topic?