Social Media – Term Paper

MEETING THE CHALLENGE: INTEGRATING GIS IN COURSE MODULE

Some of the applications discussed in the foregoing are already in practice. For example, web search and information retrieval are based on geo-spatial data. Another good example is the emerging field of social sciences known as computational social sciences. Last but not least, Geographic Information Systems (GIS) has become a central interest currently. It is this final application that finds resemblance in the current recommendation because it also forms the basis of computational social sciences. Thus, GIS as a course is central to bringing social media module close to being wholesome. There are several challenges with the geo-social data discussed in the foregoing that primarily requires GIS knowledge to circumvent. Here are a few:

Some social media uses present imprecise geo-spatial information while yet another group provides fine-grained geo-spatial coordinates.

Using geo-spatial data to select sample during researchers likely to cause bias against the general population of social media users.

The interspersion of geo-social information into a mix of daily activities and contexts necessitates reading patterns for a specific geo-spatial information.

Since the interest is also on the profiles of social media users, the strength of social media relationships is an issue that requires an in-depth approach to finding out how this may affect the kind of information shared and how it impacts an individual’s social media presence (Caverle et al , 2013).

The challenges above mentioned call for a noble approach to tackle the possible cause of confusion. Most of these challenges can be solved through GIS course. However, having GIS as a separate course will be quite tricky as it calls for faculty-wide adjustments and coming up with modules for the new course. But it is much easier, more convenient and cost-effective to integrate key aspects of the course in social media module. What needs to be bridged here is not much though very important in this context. The challenges discussed above are attributed dynamics of people and ideas and can be resolved through a roadmap that has factored in GIS knowledge. For example, dynamics of ideas results in imprecise geo-spatial data and interspersion of geo-social data into the mix of everyday activities. Resolving these challenges require peak analysis and geo-spatial constraints of information sharing (Caverle, et al., 2013). These two areas call for knowledge on Geographic Information System as a discipline. The integration of the knowledge within its immediate application context, the better for a graduate going into the field of social science research.

Similarly, dynamics of people pose such challenges as what interests a particular profile and how can social scientists overcome the bias against the remaining population of a particular community while using those users who provide geo-spatial information. These two call for a predictive analysis which tries to establish the likelihood of a particular topic being the point of interest for a given population from a given location at a future date. Once again, GIS information underscores this predictive analysis (Caverle, et al., 2013). It goes without saying. Therefore, that will be of incredible contribution to incorporate GIS studies in the social media module for the sake of keeping abreast of changes brought by social media presence. The opportunities underlying geospatial information are immense, from the study of information to the mobility of people across geographical space. This movement of people and the integration of various social systems brings forth many and diverse social impacts that are worth studying from an expertise approach. This knowledge and expertise could be enhanced potentially through GIS basics.

Essay on social media

Second Topic: Social Topic Modelling

The topic discovery was an important research area since the early 90s. However, the interests in this area are growing exponentially in the current times, and this growing interest is attributed to the rise of social media. Social media is changing the way people share information and even provide a platform for sharing this information. While in the earlier contexts information sharing was based on the kind of information exchanged between two or more people in a small geographical setting over a given period, the advent of social media is revolutionizing all these. To begin with, information is no longer shared among people from a small geographical setting, but rather with the entire globe. This is the change in the social context. The frequency with which this information is shared among individuals is also fast changing. With the rise of social media, information is constantly produced round the clock and shared across this production period constantly. In this rapid change also comes the change in the plethora of vocabulary used to describe an event (Kalyanam, et al. , 2015). These changes thus make it possible for more difficult information to quickly understand data from such a diverse view. There must be at least an equally sophisticated way to make meaning out of a long range of vocabulary for a single event. As the corpus grows larger so is the need to devise a method to cater for all the many irregularities in the shared information.

Classical Topic Discovery and Evolution (TDE) approaches aim to detect the underlying topics from the kind of contents shared in a text. In these approaches, therefore, an important focus is lost. This is the social context within which this information is shared. It is quite appropriate to argue that the information shared on social media is contextualized socially on the basis of available data on the individuals sharing this information. With a person’s geographical location embedded in the posts shared, time of post, and also user profile availability, there is more meaning to data shared in the social context per se as opposed to the missing link in the earlier similar studies (Kalyanam, et al., 2015). Thus, these additional data is given more robust way to come with topics about a given social media community and make more quality studies about any given media community. While it was difficult to detect the community of users in the earlier contexts of topic discovery studies, the availability of extra data on the members sharing information brings forth a greater opportunity to contextualize information using shares, comments and authorship of posts to map out communities of users. Once these communities have been mapped out, it becomes much easier to pull out interesting topics. It is expected that a community of social media users who share on a particular topic must be interested in particular issues. Apparently, it may also be that such users originate from given geographical locations, which adds more meaning to this study. In summary, the new approach of trying to understand the social context underlying textual corpus is the basis for a new model in the topic discovery and evolution. It is no doubt that by leveraging information about social media communities, topics that would otherwise be uncovered are now discoverable.

Argumentative essay on social media

There are two factors that pose challenges as far as using information from social media to define topics is concerned. These challenges have to do with information volatility and varying vocabulary. A  good example provided by Kalyanam et al.(2015) is celebrity gossip, a highly volatile topic that may easily take unexpected turns with each passing minute or may even change contexts in terms of a celebrity being discussed, thus becoming more diverse in terms of vocabulary usage. This volatility brought about by the quick shift of topics, and celebrity-based vocabulary makes topic discovery a misery. However, in these large variations, there is a dedicated community of social media users who constantly reference the celebrity gossip making such a topic discoverable. It is such topics that Kalyanam et al. (2015) decided to call “stable community topics”(p.3). Another kind of topics, “stable content topics”(Kalyanam, 2015:3), have been mapped out by the authors as those topics that do not change much over time. It is actually here that these researchers find it not plausible to use community of users as a piece of information leading to topic discovery and instead recommend a new methodological approach. The new discovery is the center of focus in this section. It does not only revolutionize topic discovery as an important aspect of community studies but brings an overhaul to the social media studies and the underlying importance of this in making a long-lasting impression out of social media studies for undergraduates.

Discovery of topics could be much easier with social media presence. However, what remains is that the current approaches are either combining content and link information, but forgetting the natural time evolution and change aspects inherent to this, or still, track this information along time projectile but failing to factor in content and link information. This way, a conclusive data may not be easy to reach concerning social topics discovered of a community social media users. It follows that a new method that overcomes the shortcomings of the two approaches must be designed and modeled properly. While this has been done by Kalyanam, et al. (2015), the computations underlying the modeling of the new integrated approach requires that a graduate is acquainted with some basic computational skills in this area. It is only through this set of skills that a graduate will properly handle social issues emanating from social media usage, presence, and experience. While it may not be a very good idea to suggest a change in the current approach, it is equally important to note that our graduates may be ill-equipped at the moment when they cannot use their knowledge on social media to handle socially impacting issues. Needless to say is that understanding of the topic discovery and evolution backed up by best-practice is the basis of making more out of this knowledge background. It is therefore recommended that this study on topic discovery using the integrated approach suggested by Kalyanam et al.(2015) should be considered imperative in the social media module.