Social Media in Business and Society

Social Media in Business and Society intimately organizations t hold on to look upon tender media as a threat, where some as yet opt to outlaw the usage from the workplace altogether. The idea behind it creation that employees would be given the opportunity to waste time online, chat, and peradventure pose as a pledge threat to the organization. (Turban, 2011)(Smith, 2010) outlines risk of employees kind media use at work, these stand be both intentional or non and they could lead to legal and reputational risks for organisations. These shake off been reason as cardinal main problemsUse of complaisant media cannot be fully regulated, monitored or controlled thus organisations be giving up control.Social media is a oecumenical means of communication, once a invalidating post is online its only a matter of time till it goes viral thus reaching competitors, regulators and customers.Social media is aflame and employees can express their feelings of happiness and/or frust ration.Furtherto a greater extent, (Flynn, 2012) identifies the risks of having employees participating in social media by ca using reputational damage, trigger lawsuits, cause humiliation, crush credibility, destroy c beers, create electronic business records, and lead to productivity losses.(Dreher, 2014) argues that social media is not to be feargond, but rather embraced and seen as an opportunity where employees can act as corporate advocates and leaf blade ambassadors. If anything, it helps employees keep up to date with latest news related to the industry together with continuous knowledge development. n angiotensin-converting enzymetheless, even though there are some(prenominal) studies that point out the benefits of social media, there is still no clear-cut decision whether it can influence work per melodyance or whether it can provide the social capital of the employees and help in knowledge transfer (Zhang, 2016).However, it cannot be denied that e genuinely organisati on allowing social media at work will al ways have its new-fangled deal of challenges to belabor. (Eliane Bucher, 2013) speaks about the health issues that can be encountered. Starting off with stating that there is so some(prenominal) nurture available on social media that professionals may face information overload. Not to mention the mix of work life with private life overlapping with social media.New technologies should improve workers susceptibility and reduce stress levels put onherto practically the opposite occurs (Eliane Bucher, 2013). Technostress as referred to by (Brod, 1984). To be successful in the social media environment one needs to deluge the below 3 points other brisk technostress is formedTechno-overload Increase in workload which could be actual or perceived.Techno-invasion Social media enables people to be continually connected from almost e very(prenominal) device. This can lead to the feeling of the need to be connected or online causing reducti on in family time allowing work issues to beset the private life (Eliane Bucher, 2013).Techno-un realty Social media is constantly changing and therefore brings with it uncertainty as regards to what technologies and skills are needed to perform the job and what will they be in the future.Social media comes with many legal issues tied to it. These range from pre-employment to post employment. Wrong usage of social media will for certainly lead to waste of time, inefficiency, reputation issues and prohibit image for the organisation. Some of the laws are outlined below by (Lieber, 2011)Employment Laws by tagging co-workers in certain provocative photos or videos,Defamation and Libel Laws by stating certain comments on co-workers or employers thus effecting their reputation., As stated in (Trott, 2009) a Microsoft Survey found that 41% of employers found their decision of not hiring an applier ground on what they found online in relation to their reputation. This is also known as Netrep. This constitutes a legal risk of discrimination in itself if the recruiter is basing decision on the netrep.Fair Credit Reporting run by having interviewers friending an applicant on Facebook to acquire more information than is required for the job applied.Health Insurance Portability and right Act by having a medical professional LinkIn with a patient.Uniform Trade Secrets Act by having employees discussing or commenting on social media about party internal only discussions or non-public projects.Employers can monitor the use of social media at work if the employees are informed in advance. Disciplinary actions can be taken once any abuse is being noticed. Policies should include what is allowed and what is considered as abuse (Trott, 2009).If the employees post on their personal accounts outside of office hours and such posts are in relation to work having a nix impact in some way to the employer or the organisation then there is still curtilage for disciplinary ac tion even though employees try to advocate for respect for private and family life, home and correspondence (article 8) or freedom of expression (article 10) from the Human Rights Act 1998.As discussed above, social media has its advantages and disadvantages and seeing that social media is here to stay organisations have little choices but to accept the new reality, address it and learn how to capture strong use of it. (Lieber, 2011), among others, identifies the followers criteria that any organisation willing to harness social media must addressThe creation and enforcement of solid social media policies within the organisations personnel addressing fair use, access during work time and general behaviour on social media (even during personal time).Directly using social media for the benefit of the organisation such as for recruitment, marketing and investigating competing organizations.Monitoring of detect social networks to information mine information regarding your organisa tion (and potentially others as well), possibly using automated algorithms and parcel for maximum efficiency and accuracy.From the above-mentioned criteria, the first two deal with human resource aspect of social media where organizations lay out guidelines to their employees on how to use them, and they as the organizations can use social media directly for recruitment, marketing etc. However, as the third criteria suggests, to make most use of social networks organizations must make genuine that any information/data being released on such platforms, is gathered and utilise effectively.It is important that an organization is always aware of what the average exploiter is verbalize about their brand, effectively getting the general feel or mood season analysing the trends across time. The same principle could be applied to monitor competitors possibly for example identifying any weak products which the competitors have and having your own similar product take advantage of the situation.Effective monitoring comes from generating good data. entropy excavation involves the following steps to make data marrowful for monitoring (Raghav Bali, 2016)Removing unwanted data and noiseTransformation of the raw data into data that can be employ for further processingStudy the data and come up with patterns that can give further insight to our dataRepresend the data in a way that is useful to companies or to who the data intended for.There are different data mining techniques which can be used to monitor social media use. Social media is a form of real time communication therefore an effective monitoring withall needs to monitor and provide alerts as things happen. Most text edition mining irradiations make use of look engines to go through with(predicate) social media sites and collect information related to the keywrangle or interests. (Mark My Words article)Text Analytics (Text/Data Mining) Text analytics involves a complex and elaborate number of steps t o strip down conversations into separate words and analyse the way these words are being used, positive or negatively charged and even derive patterns from collected data.When we search for a movie and receive some other movie recommendations that technique is using text mining.Text Mining is made up of Data Mining (Information retrieval, Natural Language Processing Machine learning) + Text Data (Emails, Tweets, News Articles, Websites, Blogs etc.) issue 1 Text Mining (Charu C. Aggarwal, 2012)As indicated in Figure 1, Stop Word Removal and Stemming eliminate the generic and less meaningful words form a phrase, this helps categorizing different words with same meaning as see, seen and being seen.Bag of Words (BOW) is having words separated from the sentence and each word having a numerical value which represents its importance.Limitations(Charu C. Aggarwal, 2012) outlines several prepareations that can be observed and future in-depth research is requiredThe real-time posts on soci al media are a very important resource as mining data in real time as it is being posted can yield many advantages. This just remains a challenge for when these posts are not conducted from work computers or from outside work.Social media is very uncrystallised and some applications like twitter even limit the amount of characters per post. This brings about problems of text recognition when short length words are used like gnite gr8 etc.Social media allows different ways to express opinions or emotions these could be through images, videos and tags making the text analytics much more complex and difficult in its pre-processing stage.Method 1Keyword Search (Rappaport, 2010)Organisations can decide which keywords they want to monitor, these may be chosen based on what is important for that company, it could be their products or emotional states. Social media is a very unstructured place containing noise and unwanted data for our data mining process. This form of search is good to c apture keywords and try and form a meaning of these words and the frequency used however its very hard to come up with what is the users intent. For that reason, we then consider a more complex search method called pattern Analysis.Method 2Sentiment Analysis and Emotion AnalysisSentiment Analysis is the process of identifying design in text and analyse it. There are three types of opinion digest (Walaa Medhat, 2014)Document Level Analyse the entire document as one topic and form an opinion or purview on the entire documentSentence Level Analyse sentiment in each sentenceAspect Level Analyse sentiment in respect to entities as you can have more than one aspect in a sentence for the same entity.For this study, we are focusing our research on Sentence Level abbreviation using semantic search.Semantic SearchSemantic search goes beyond the traditional keyword search by providing a meaning to a phrase and makes use of a wide range of resources to interpret the phrase and thus pro viding a more accurate result.Some examples of semantic search in our daily livesConversational searchesFigure 2 Conversational Search (Google, 2017)Auto Correct spelling mistakesFigure 3 Auto Correct (Google, 2017) pageant information in graphics formatFigure 4 Information in graphics format (Google, 2017)(Charu C. Aggarwal, 2012) outlines some challenges that are encountered when going through mining. These are the difficulty in recognising opinions, subjective phrases and emotions.Opinion mining challenges.When using semantic search method on a post one needs to clear that the post can contain all the followingPositive opinions I like the computer I bought, it has a very clear screen disallow opinions however my wife thinks its too expensive diametric targets The targets in the positive opinions relate to the computer and the screen whereas the targets in the negative opinions are the priceDifferent opinion holders The positive opinions are mine however the negative opinions are of my wifeSubjectivity mining challengesPosts are also made up of nonsubjective and subjective comments. Subjective expressions like opinions, desire, assumptions amongst others may not contain opinions or may not express any positive or negative comments.Emotions mining challengesEmotions (love, joy, anger, fear, sadness, happiness and more) fall under a form of subjective expression. Sometimes emotions give no opinions in a phrase.To observe the usefulness and ideal shape up towards the digest of social media related posts and messaging, a software algorithm was designed and partially developed to illustrate this scenario. The idea behind this software is to have the user write inside a textbox, mimicking an actual employee typing using a company machine, while the system monitors such text and acts per what it registers. Therefore, this apparatus will be presented as a standalone software/algorithm concept, emulating an actual activity of a possible employee, and as such mus t be adapted accordingly to make use of it in a real-life situation.The basic principle of the solution proposed is made up of three modulesThe key logger that monitors the users input at runtime and effects certain rulesThe keyword and semantic analysis on the data gatheredThe storage of produced analysis and logThe following flowchart outlines the lifecycle of said solution, followed by a detailed analysis of each component mentioned above, as well as possible ways on how it can be further enhanced to produce even more accurate results.The flow of the proposed solution.Created using draw.io (https//www.draw.io/)Collecting and Processing DataIn this solution, key logging is used to monitor the data inputted by the user, which is a constant monitoring of the keystrokes registered by ones activity, and registered as a stream of text ready to be dissected and analysed as required. The main advantage of using such a strategy is that data is collected and used in real-time, making it id eal for scenarios where an alarm (for example a negative post related to work) needs to be raised as quickly as possible to the relevant personnel, providing a detailed log of what the employee has typed (through the key logger) eliminating the need to monitor and access the relevant social media to check what has been posted.Note there are other strategies one can pursuit to monitor the users activity, such as firewall policies or general network surveillance, however in real-life situations such solutions can prove rather difficult to setup due to the expertise required while web encryption and proxy operate makes it even harder to effectively monitor the traffic generated by the users. A key logger, even if effective, generates a lot of unneeded garbage beyond the scope of social media. For example, an employee working on his station would be constantly registering keystrokes which the logger is then adding them up to its own text stream. This could prove to be very problematic for three main reasonsThe logger would begin to amass a significant amount of storage space, unless the key logger is given a limit of how much information it can hold and removing old data to make up space for the new data, but than some information can get permanently lost.The analysis of the text stream generated can be quite intensive, which can significantly affect the performance of the machine doing the analysis, especially when considering that the analysis is assumed to be bear on on the users machine which most probably isnt very well suited for such intensive work. Furthermore, following the previous point, the garbage log is being analysed too needlessly.The chances are that an employee would spend very little time on social media, thus logging and analysing the work-related activity is quite pointless for such a scope.To overcome the above-mentioned issues, the proposed solution makes use of predefined social media trigger keywords i.e. a list of social media websites such as Facebook, Twitter, LinkedIn etc., where depending on such triggers being hit or not, the key logger will have two states, inactive monitoring and active monitoring.When the tool is running normally, the key logger is in a passive state keeping only the last 30 characters in its memory, without processing the stream. The only thing it does however, is to constantly check the stream read from the textbox in the tool against the trigger keywords, and if any of the keywords is found to have been registered then the key logger would go into active state. While in this state the key logger would sum up its maximum capacity, and begin to log every keystroke while constantly analysing the feed. The key logger will go back to passive state when the predefined character limit is reached or enough time has passed.Following this logic, only a set of keystrokes would be registered, reducing the chance of collecting and processing unneeded information while maintaining the workload and storage use of the machine to a minimum.Note in this approach once the key logger goes into active state, it is monitoring and analysing the feed at runtime locally, and this could prove to be quite intensive depending on the parameters set and the overall performance of the users machines. Organizations implementing this solution can opt to have the log analysed after the key loggers goes back into passive state and therefore analysing the data only once. Better yet, since the solution assumes that the key logger is analysing the data locally, instead the logs can be sent to a common server and be analysed as a scheduled task.Once this data is captured through the key logger the feed can be processed by means of the methods discussed earlier (Method 1 and 2). Based on the outcome we store the data in our information system and align the data based on the organisations social policy.Approaching data analysis using keyword and semantic methodsThe designed software makes use of two di fferent types of analysis algorithms, keyword based and semantic based, and are used together to try and cancel each others limitations and thus providing much more accurate results.Keyword based analysisThe more traditional keyword analysis algorithm consists of having a list of keywords i.e. a predefined set of texts, and hit the data to be analysed against that list to determine whether any keywords have been hit and at what frequency. For example, having a text (representing the data) analysed against a list of negative texts (the keywords) would provide a set of statistical information which could be used to evaluate how negative the text is, which is conceptually what a social media monitoring tool should be trying to achieve.However, the major flaw of this analysis algorithm within the context of social media monitoring, is that keyword based analysis is far too broad and prone to false alarms if not controlled. Having the data gathered from the key logger (therefore filtered to social media activity) analysed against a set of negative texts, the statistical information produced may not be relevant to the organizations interest. An employee could simply be posting a feed about how bad the weather is and how much s/he hates it, which the keyword analysis algorithm would recognize as negative and story accordingly.In the proposed solution, the keyword based algorithm uses two different sets of keywords against the gathered data, with the pay back to filter the batches of logged texts by relevance. The first set consists of a list of works related text, such as work, job, company, company name etc. i.e. every keyword that could somehow link the user to the organization implementing the solution. In the second set, a list of keywords/texts associated with negativity are stored, such as world-weary, unhappy, hate, dull, sick and tired etc.When the data passed along through the key logger reaches the keyword analysis module, it would first check the log ag ainst the first set and therefore determine whether the data fed is of any relevance to work, and if not simply do nothing. On the other hand, if any of the keywords from the first set is hit, it means that the data inputted is relevant and therefore must be analysed further. In this occurrence, the tool would analyse the entire log within the key logger (which is currently in an active state as described in the previous section) and take away the statistical information with regards to the second set.The flow of the full keyword based algorithm adapted in the toolCreated using draw.io (https//www.draw.io/)ExamplesKeywords to assumeFirst Set (work) WORK, moving inSecond Set (negative) worldly, UNHAPPY, SAD, HATE, DULL, TIRED, SICK AND TIRED, ANNOYED, FED UPExample 1InputHate this weather, its gravely effecting my mood. Constantly feeling tired and sad.OutputNoneExample 2InputAt work and bored. Wish I could find a recrudesce job, this one is just so annoying.OutputBORED x 1full logExample 3InputNever a dull moment at work. At the end of the day, the guidance brought in pizzas, fresh doughnuts and beer. In a couple of hours, the food was gone leaving everyone too tired to move. Got to love this company, always making sure their employees are never bored and unhappy.OutputDULL x 1TIRED x 1BORED x 1UNHAPPY x 1full logFrom the examples above one can note a few limitations concerning the keyword based analysis algorithm.In example 2 the logged text is alarming, which most probably would require the full attending of the responsible personnel, but due to the limited keywords, only a single piece of text was hit which would make the output seem not so alarming. Furthermore, the logged text had the word annoying which in the negative keyword set is listed as annoyed, but still this was not captured. Therefore, this means that this algorithm is highly dependent on the keywords lists and possible deviations of each text.In example 3 the output looks very alarming since the negative keywords list was hit 4 times, but the input is very positive. The algorithm was unable to take into consideration the context of how the negative words were used and simply counted the number of times they were encountered within the log, hence raising a false alarm.To overcome such limitations, other algorithms must be used in conjunction with the keyword based, where in this solution the semantic based approach is used to compliment the algorithm and try to provide more accurate results.Semantic based analysisAs explained in previous sections, semantic analysis introduces a certain degree of understanding when analysing a given text, and this is achieved by giving meaning to what it is fed. In this proposed software algorithm, this type of analysis is used to evaluate the sentiment and emotion behind the fed input, and therefore can determine whether the users work related activity on social media is negative or positive, which by citation may be able to over come the limitations of keyword based approach.Basic forms of semantic based algorithms used to analyse text in relation to sentiment and emotion, often providing a single value output denoted by a percentage, where 0% means that the text is absolutely negative and a 100% would indicate that without a doubt it is positive. However, semantic analysis is capable to go beyond a simple value, where some of which can produce a fully detailed report indicating the level of emotions for multiple types, such as anger, fearfulness and joy. The following is an example of such a report produced by the tool Tone Analyser offered by (Cloud, 2017).Example report of a semantic based algorithm offered by IBM Watson Developer CloudApplying such an algorithm which produces a very detailed report, may be well beyond the scope of monitoring work related activity on social media. In the end, what the proposed solution is trying to achieve is to detect negative activity which would harm said organization s, that when detected, the log of that activity is passed along to the corresponding personnel with perhaps a brief report of the analysis.Another drawback to be considered in this scenario, is that light weight semantic algorithms are much less intensive than algorithms which consider different types of emotions when analysing a text, and given that in the solution such an analysis will be triggered almost constantly, having a heavy algorithm being triggered would result in a very negative experience to said users. This is wherefore in the proposed solution a lighter semantic analysis is considered, that is the API provided by (ParallelDots, 2017).Note one could argue that using a semantic analysis algorithm which produces a detailed report, could replace the entire algorithm which is using both the keyword based analysis and the light weight semantic based analysis. However, performance wise the latter would operate much smoother, and from a technical point of view considerably e asier to setup. Note in the proposed solution, the semantic analysis will be qualified to whether the keyword based algorithm is triggered or not, and therefore subject to the filter which is detecting whether the activity on social media is related to work or not.Examples using the sentiment analysis demo provided by (ParallelDots, 2017), which outputs single value percentages 0% being negative, while 100% being positive.Example 1InputHate this weather, its severely effecting my mood. Constantly feeling tired and sad.Output0%Example 2InputAt work and bored. Wish I could find a better job, this one is just so annoying.Output6%Example 3InputNever a dull moment at work. At the end of the day, the management brought in pizzas, fresh doughnuts and beer. In a couple of hours, the food was gone leaving everyone too tired to move. Got to love this company, always making sure their employees are never bored and unhappy.Output79%Classifying severity based on score and frequency of wordsThus far, the algorithm detected negative activity on social media relating to work, using both keywords and semantic analysis. However, the term negative can be rather broad and it may be the case that the organization would not want to be alerted for every minor negative activity, since that will become counterproductive. As such the proposed algorithm has a threshold mechanics which determines whether to send in alerts or not.The threshold settings are two. The minimum number of negative words the activity must contain, and the minimum percentage of negativity to be considered. Right after the key logger is finished monitoring the social media activity, if work related activity is logged, the system evaluates the log based on the threshold set by the administrators of the system, and proceed accordingly.Using same parameters of previous example for keyword and semantic based approaches. The thresholds are set as follows Minimum Keywords 1, Minimum Semantic Percentage 30%.Example 1In putHate this weather, its severely effecting my mood. Constantly feeling tired and sad.OutputNone (not work related)AlertNoExample 2InputAt work and bored. Wish I could find a better job, this one is just so annoying.OutputKeywords hit 1Semantic 6%AlertYesExample 3InputNever a dull moment at work. At the end of the day, the management brought in pizzas, fresh doughnuts and beer. In a couple of hours, the food was gone leaving everyone too tired to move. Got to love this comp