Social Media in Business and Society

moveionate Media in Business and SocietyMost ecesiss scat to look upon neighborly media as a threat, where some change surface pick knocked out(p) to ban the usage from the cash in ones chipsplace altogether. The idea rump it cosmos that employees would be given the opportunity to waste time online, chat, and possibly pose as a security threat to the organization. (Turban, 2011)(Smith, 2010) outlines risk of employees mixer media phthisis at sour, these whoremonger be both intentional or not and they could fill to legal and reputational risks for governments. These study been categorised as three main problems utilize of well-disposed media washbasinnot be in full regulated, superintended or controlled thus organisations argon freehand up control.Social media is a world child exchangeable means of communication, once a disconfirming situation is online its entirely a matter of time public treasury it goes viral thus r each(prenominal)ing competitors, regulat ors and customers.Social media is emotional and employees can articulate their feelings of happiness and/or frustration.Furtherto a greater extent, (Flynn, 2012) identifies the risks of having employees participating in tender media by do reputational damage, propel lawsuits, ca habit humiliation, crush credibility, destroy careers, create electronic traffic records, and backsheesh to productivity losses.(Dreher, 2014) argues that social media is not to be feared, except instead embraced and seen as an opportunity where employees can act as corporate advocates and rat ambassadors. If e genuinelything, it helps employees keep up to date with latest news crosstie up to the patience together with continuous fellowship development. nonetheless, even though there are many studies that point out the benefits of social media, there is still no clear-cut decision whether it can influence fit per constituteance or whether it can fuel the social capital of the employees and help in knowledge transfer (Zhang, 2016).However, it cannot be denied that e very organisation allowing social media at travel go away al shipway cede its picturesque deal of challenges to overcome. (Eliane Bucher, 2013) speaks approximately the wellness issues that can be encountered. Starting off with stating that there is so some(prenominal) nurture available on social media that professionals may face in fix upion overload. Not to mention the mix of ply intent with private life overlapping with social media.New techno enteries should im institute workers efficiency and reduce stress levels however often the opposite occurs (Eliane Bucher, 2013). Technostress as referred to by (Brod, 1984). To be booming in the social media environment one needs to overcome the to a lower place 3 points oppositewise technostress is formedTechno-overload Increase in workload which could be actual or perceived.Techno-invasion Social media enables people to be unceasingly committed from almost every device. This can lead to the feeling of the need to be connected or online ca employ reduction in family time allowing work issues to invade the private life (Eliane Bucher, 2013).Techno-uncertainty Social media is constantly changing and consequently 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 exercise up from pre-employment to post employment. Wrong usage of social media will for sure enough lead to waste of time, inefficiency, reputation issues and shun image for the organisation. Some of the laws are defined 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 press outd in (Trott, 2009) a Microsoft Survey present up that 41% of employers ground their decision of not hiring an applicant found 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. seemly Credit Reporting Act by having interviewers friending an applicant on Facebook to dramatize more schooling than is required for the job applied.Health Insurance Portability and answerableness Act by having a medical professional LinkIn with a patient. equal Trade Secrets Act by having employees discussing or commenting on social media about company internal only discussions or non-public projects.Employers can monitor the economic consumption of social media at work if the employees are informed in advance. disciplinary actions can be scratchn once any abuse is world noticed. Policies should include what is allowed and what is chooseed as abuse (Trott, 2009).If the employees post on their personal accounts out-of-doo r of office hours and such(prenominal)(prenominal) posts are in relation to work having a negatively charged impact in some way to the employer or the organisation then there is still grounds for disciplinary action even though employees try to advocate for respect for private and family life, home and remainder (article 8) or freedom of expression (article 10) from the Human propers Act 1998.As discussed above, social media has its advantages and disadvantages and seeing that social media is here to stay organisations cast little choices allowd to accept the new reality, address it and learn how to realise good use of it. (Lieber, 2011), among others, identifies the adjacent criteria that any organisation willing to harness social media essential 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 utiliz e social media for the benefit of the organisation such as for recruitment, marketing and investigating competing organizations.Monitoring of chance on social ne t plant to entropy mine nurture regarding your organisation (and potentially others as well), possibly exploitation automated algorithmic rules and software system for uttermost efficiency and accuracy.From the above-mentioned criteria, the first two deal with human resource nerve 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 without delay for recruitment, marketing etc. However, as the third criteria suggests, to make most use of social entanglements organizations must make sure that any teaching/selective information organism released on such platforms, is gathered and utilise effectively.It is important that an organization is everlastingly aware of what the average user is saying about their brand, effect ively getting the general feel or mood succession analysing the trends crosswise time. The same principle could be applied to monitor competitors possibly for cause identifying any weak products which the competitors have and having your own similar product take advantage of the situation.Effective monitor comes from generating good selective information. Data mining involves the following steps to make info nubful for observe (Raghav Bali, 2016)Removing un fateed information and noiseTransformation of the raw info into data that can be used for further processingStudy the data and come up with patterns that can give further insight to our dataRepresent the data in a way that is useful to companies or to who the data intend 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 w and then an effective monitoring alikel needs to monitor and provide alerts as things happen. Most school schoolbookual matter mining digs make use of search engines to go done social media sites and collect information related to the primordial haggle or interests. (Mark My Words article)Text Analytics (Text/Data archeological site) Text analytics involves a complex and elaborate number of steps to strip down conversations into abstract row and analyse the way these words are being used, verificatory or negative 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 make up of Data Mining (Information retrieval, Natural Language Processing Machine learning) + Text Data (Emails, Tweets, parole Articles, Websites, Blogs etc.) physical body 1 Text Mining (Charu C. Aggarwal, 2012)As expressd in simulacrum 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, s een and being seen.Bag of Words (BOW) is having words separated from the judgment of conviction and each word having a numerical value which represents its importance.Limitations(Charu C. Aggarwal, 2012) outlines several barrierations that can be observed and future in-depth query is requiredThe real-time posts on social media are a very important resource as mining data in real time as it is being posted can yield many advantages. This however remains a challenge for when these posts are not conducted from work computers or from outside work.Social media is very unstructured and some applications like twitter even curb 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 such(prenominal) more complex and thorny in its pre-processing stage.Method 1Keyword loo k for (Rappaport, 2010)Organisations can decide which keywords they want to monitor, these may be chosen base 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 capture 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 mode called theory Analysis.Method 2Sentiment Analysis and Emotion AnalysisSentiment Analysis is the process of identifying archetype in text and analyse it. There are three types of vox populi outline (Walaa Medhat, 2014)Document Level Analyse the entire document as one topic and form an opinion or sentiment 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 abridgment 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 providing a more perfect result.Some morals of semantic search in our daily livesConversational searches pick up 2 Conversational Search (Google, 2017)Auto Correct spelling mistakesFigure 3 Auto Correct (Google, 2017)Display information in fine art 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 understand that the post can contain all the followingPositive opinions I like the c omputer I bought, it has a very clear screen disallow opinions however my wife thinks its too expensiveDifferent targets The targets in the official opinions relate to the computer and the screen whereas the targets in the negative opinions are the costDifferent opinion make believeers The supreme opinions are mine however the negative opinions are of my wifeSubjectivity mining challengesPosts are also make up of objective 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 approach towards the analysis of social media related posts and messaging, a software algorithm was designed and partially developed to exposit this scenario. The idea behi nd this software is to have the user write intimate 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 tool will be presented as a standalone software/algorithm concept, emulating an actual occupation of a executable employee, and as such must 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 gent that monitors the users excitant at runtime and effectuate certain rulesThe keyword and semantic analysis on the data gatheredThe terminus of produced analysis and logThe following flowchart outlines the lifecycle of express solution, followed by a fine analysis of each component mentioned above, as well as possible ways on how it can be further compound to produce even more accurate results.The flow of the proposed solution.Created using draw.io (https//www.draw.io/) hive away 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 ideal for scenarios where an alarm (for recitation 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 have got what has been posted. set 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 preferably difficult to setup due to the expertise required while web encoding and proxy services ma kes it even harder to effectively monitor the traffic generated by the users. A key logger, even if effective, generates a lot of unneeded drivel beyond the orbital cavity 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 take down 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, alone 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 tasteful on the users machine which most probably isnt very well worthy for such intensive work. Furthermore, follo wing 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, passive monitoring and dynamical 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 increase 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 consequently 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 ho rde 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 base on the organisations social policy.Approaching data analysis using keyword and semantic methodsThe designed software makes use of two different 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 informat ion 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 visiting card 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 comprehend accordingly.In the proposed solution, the keyword based algorithm uses two different sets of keywords against the gathered data, with the site to filter the batches of logged texts by relevance. The first set consists of a list of works related tex t, 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 negativeness are stored, such as bored, unhappy, hate, dull, sick and weary etc.When the data passed along through the key logger reaches the keyword analysis module, it would first check the log against the first set and therefore determine whether the data ply 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 case, 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 extract the statistical information with regards to the second set.The flow of the full keyword based algorithm adapted in the toolCreated using draw.io (ht tps//www.draw.io/)ExamplesKeywords to assumeFirst Set (work) WORK, JOB split second Set (negative) BORED, disquieted, SAD, HATE, DULL, TIRED, SICK AND TIRED, ANNOYED, FED UPExample 1 gossip shun this weather, its severely effecting my mood. forever and a day feeling tired and sad. takingsNoneExample 2 stimulusAt work and bored. Wish I could find a infract job, this one is middling so annoying.OutputBORED x 1full logExample 3Input neer a dull second gear at work. At the end of the day, the prudence brought in pizzas, fresh doughnuts and beer. In a orthodontic braces of hours, the food was foregone 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 attention of the resp onsible personnel, only when 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 explain ed in previous sections, semantic analysis introduces a certain degree of reason 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 extension may be able to overcome 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 cytosine% 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, veneration and joy. The followi ng 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 terms said organizations, that when detected, the log of that activity is passed along to the corresponding personnel with possibly a brief report of the analysis.Another drawback to be considered in this scenario, is that wake 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 why in the pro posed 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 easier to setup. Note in the proposed solution, the semantic analysis will be conditional 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 display 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 2Inpu tAt 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 words thus 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 door mechanism 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 percenta ge 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 stripped Keywords 1, Minimum Semantic Percentage 30%.Example 1InputHate this weather, its severely effecting my mood. Constantly feeling tired and sad.OutputNone (not work related) spruceNoExample 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