globals [total ;;total number of informed people n ;;previous number of informed people m-current ;;number of negatively informed strong ties j-current ;;number of negatively informed weak ties mx-current ;;number of positively informed strong ties jx-current ;;number of positively informed weak ties a ;;the power of negative w-o-m over positive w-o-m x ;;recording the highlighted x-coordinate y ;;recording the highlighted y-coordinate tickx] ;;tick numebr when the observed patch change opinion patches-own [state ;;state of patch: not informed (0)/ informed (1) threshold ;;threshold for each patch: randomly assigned alpha ;;advertising strength beta-s ;;the strengh of strong ties beta-w ;;the strengh of weak ties prob ;;probability of changing into negatively informed state m ;;number of negatively informed strong ties j ;;number of negatively informed weak ties content ;;positive (-1) or negative (1) jx ;;number of positively informed weak ties mx ;;number of positively informed strong ties probx] ;;probibility of changing into positively informed state to setup ;;preparing the world ca reset-ticks watch-one ;;recording which patch is being highlighted ask patches [ set probx 0 set jx 0 set mx 0 set content 0 set state 0 set threshold random-float 1 set alpha advertising set beta-s strong-ties set beta-w weak-ties] set tickx 0 ;;setting the starting point of the tick count for the highlighted patch set a 1.133 ;;setting the power of negative w-o-m over positive w-o-m end to watch-one ;;recording which patch is being highlighted ask one-of patches [ set x pxcor set y pycor watch-me] end to go set n count patches with [state = 1] ;;counting the total informed pacth(es) check ;;change into informed or not recolor ;;recoloring the patch(es) according to their state and content reportwatch ;;preparing the value of highlighted patch to be reported to user set total count patches with [state = 1] ;;recount the new total number of informed patch(es) ifelse (n != total) [tick] [stop] ;;stop if the number of informed people is not changing end to check ;;change into informed or not and what the content of the patch is if (ticks = 2) ;;When the tick = 2 (after there are people who are informed through advertisement, assign the content value to them) [ask n-of (%-positive-rumor / 100 * n) patches with [state = 1 and content = 1] [set content -1]] ask patches with [state = 0 or ( pxcor = x and pycor = y)] ;;start checking the uninformed patches one by one for potential power to change into informed [set m count neighbors with [state = 1 and content = 1] ;;setting up the value for m, mx j and jx set mx count neighbors with [state = 1 and content = -1] set j (n - m - count patches with [content = -1 and state = 1]) set jx (n - mx - count patches with [content = 1 and state = 1]) ifelse (ticks < 2) ;;when the ticks < 2, no content difference yet so different formula will be used [set prob (1 - ((1 - alpha) * ((1 - beta-w) ^ j) * ((1 - beta-s) ^ m)))] [ifelse (count patches with [content = -1 and state = 1] > 0) ;;when ticks > 2, there will be two different content so probility will be counted seperately [set prob (1 - ((1 - alpha) * ((1 - beta-w) ^ j) * ((1 - beta-s) ^ m))) set probx (1 - ((1 - alpha) * ((1 - beta-w) ^ jx) * ((1 - beta-s) ^ mx))) set probx probx * a] [set prob (1 - ((1 - alpha) * ((1 - beta-w) ^ j) * ((1 - beta-s) ^ m)))]] ifelse (probx > 0 and prob > 0) ;;checking whether there are a probability to change state [if ( abs (prob - probx) >= threshold) ;;checking whether the value is still able to exceed threshold after taking other content into consideration [set state 1 ;;changing state to informed ifelse (prob > probx) ;;if the probability for negative content is bigger than the positive content, change content to negative and the other way round [set content 1] [set content -1]]] [ifelse (probx = 0) ;;if no positive content [if (prob >= threshold) [ ;;change into negative once the probability reached the threshold set state 1 set content 1]] [if (probx >= threshold) [ ;;change into postive once the probability reached the threshold set state 1 set content -1]]]] end to recolor ask patches with [state = 1] [ ifelse (content = 1) [ set pcolor red ] [set pcolor blue]] end to reportwatch ;;preparing the value of highlighted patch to be reported to user ask patches [if (pxcor = x) and (pycor = y) [ set m-current count neighbors with [state = 1 and content = 1] set mx-current count neighbors with [state = 1 and content = -1] set j-current (count patches with [state = 1] - m-current - count patches with [content = -1 and state = 1]) set jx-current (count patches with [state = 1] - mx-current - count patches with [content = 1 and state = 1]) if ((tickx = 0) and [state] of patch-at x y = 1) [set tickx ticks]] ] ;;counting when the highlighted patch change state end @#$#@#$#@ GRAPHICS-WINDOW 440 11 860 452 20 20 10.0 1 10 1 1 1 0 1 1 1 -20 20 -20 20 0 0 1 ticks 30.0 SLIDER 23 56 220 89 weak-ties weak-ties 0 0.1 0.015 0.001 1 NIL HORIZONTAL TEXTBOX 32 38 182 56 Strength 13 0.0 1 SLIDER 23 89 220 122 strong-ties strong-ties 0 0.1 0.071 0.001 1 NIL HORIZONTAL SLIDER 23 122 220 155 advertising advertising 0 0.01 0.0090 0.001 1 NIL HORIZONTAL BUTTON 259 59 326 92 setup setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 337 58 400 91 go go T 1 T OBSERVER NIL NIL NIL NIL 1 MONITOR 257 116 404 169 total informed people total 0 1 13 MONITOR 257 169 404 222 total uninformed people world-height * world-width - total 0 1 13 SLIDER 23 164 221 197 %-positive-rumor %-positive-rumor 0 100 64 1 1 % HORIZONTAL MONITOR 74 251 229 304 Total negative w-o-m count patches with [content = 1] 17 1 13 MONITOR 228 251 377 304 Total positive w-o-m count patches with [content = -1] 17 1 13 MONITOR 88 317 174 370 Strong-ties m-current + mx-current 0 1 13 MONITOR 39 370 129 423 (-) strong-ties m-current 0 1 13 MONITOR 129 370 222 423 (+) strong-ties mx-current 0 1 13 MONITOR 271 317 350 370 Weak-ties j-current + jx-current 0 1 13 MONITOR 222 370 313 423 (-) weak-ties j-current 0 1 13 MONITOR 313 370 408 423 (+) weak-ties jx-current 0 1 13 MONITOR 188 317 257 370 tick-stop tickx 0 1 13 @#$#@#$#@ ## WHAT IS IT? In brief, this simulation is built on as a continuation to the previous model on Talk of the Network (Goldenberg et al., 2001) with an add-on feature. This simulation will let user observe how information is being spread in the community. For this simulation, members of the community will be stationary. If the collection of information coming from others with strong and weak ties is strong enough to pass their threshold, they will turn from the state of “uninformed” to “informed”. Each member of the community will have its own unique threshold of getting into the informed state. And just as in real life, members will put higher value in the information spread by someone close to them than those who are not directly connected to them. In general Word-of-Mouth (w-o-m) can be defined as the sharing of information (opinions or recommendations) through communication among people about products and services (Westbrook, 1987). In business area, a lot of companies are aiming to increase their brands knowledge through various word-of-mouth methods – offline (e.g. face-to-face) as well as online (e.g. through SNS). Of course, advertisement also plays a role in the sharing of information by being the initial contact as well as one of the influencers in getting people to know a brand. But, contrary to popular belief, in long-term, advertisement is not as powerful as w-o-m in contributing to the spread of information (Buttle, 1998; Keller & Berry, 2003; Laborie, 2006). Not only brand knowledge, researchers also found that w-o-m influenced almost 70% of customer’s decision to buy (Balter, 2008). For this simulation, there will be two kinds of relationship defined – strong ties and weak ties. The differentiation of the relationship is based on “the strength of weak ties” theory by Granovetter (1973). By observing the micro-level differences, a lot of differences can be observed on the macro-level phenomena. According to Granovetter, as contrary to weak ties, strong ties are characterized with a more stable, frequent and intimate interaction. Using this logic, in this simulation, strong ties are understood as those who are closely connected to the individual while weak ties are understood as those who are not directly connected to the individual. As a add-on feature, current simulation will also be observing the effect of different valance of the information (e.g. positive or negative) towards the rate and end-result of the spreading of information. Moreover, according to the result collected by Deitz and Cakim (2005), the number of negative w-o-m overpower positive w-o-m by 1.133 times. The add-on feature in this simulation is based on a journal article titled "On Braggarts and Gossips: A Self-Enhancement Account of Word-of-Mouth Generation and Transmission" by Angelis M.D. et al. (2012). ## HOW IT WORKS As have been mentioned before, each member has its TRESHOLD. To start off, user can adjust a set of variables which will be constant throughout the simulation - STRONG-TIES, WEAK-TIES, ADVERTISING and %-OF-POSITIVE-RUMOR. Besides that, each member also has its own STATE - informed "1" or not "0"). With the implementation of watch-one procedure, user can observe and learn more about the detail of the process from one of the randomly selected member which is being put into the spotlight. From this member, user can track the NO. OF WEAK TIES which can tell us the number of "informed" others in the community which are not directly connected to the member. The other one is to track the NO. OF STRONG TIES which is the number of "informed" others in the surrounding which are directly connected to the member (neighbors). The probability of a member to move from not knowing to knowing is defined using a formula stated in the journal article (Goldenberg et al., 2001): prob = (1 - ((1 - alpha) * ((1 - beta-w) ^ j) * ((1 - beta-s) ^ m))) alpha: ADVERTISING beta-w: WEAK-TIES beta-s: STRONG-TIES j: number of "informed" members in the community who are not directly connected to the highlighted member m: number of "informed" members who are directly connected to the highlighted member The member need to have a prob >= threshold to move from uninformed to informed. As there are two kinds of rumor being spread, the probability for both negative (PROB) and positive rumor (PROBX) will be counted seperately. As been mentioned above, negative w-o-m will have more power over positive w-o-m. So, PROBX = PROBX * A with a = the power of negative w-o-m. After that, members can only move from "uninformed" to "informed" only if: abs (prob - probx) >= treshold ## HOW TO USE IT As a starter, user can use the sliders provided to adjust the WEAK-TIES, STRONG-TIES, ADVERTISING and %-POSITIVE-RUMOR. STRONG-TIES: This is to set how strong the effect of others with a strong connection to the member knowing the information towards the probability of the member knowing the information. WEAK-TIES: This is to set how strong the effect of others who the member is not directly connected to knowing the information towards the probability of the member knowing the information. ADVERTISING: This is to set how strong the effect of the advertisement towards the initial as well as the add-on probability of the member knowing the information. %-POSITIVE-RUMOR: Using this, user can set the specific percentage of positive rumor in the early stage of information transmission. After that, user can simply click on the SETUP button then the GO button to begin the simulation. Members who have moved from "uninformed" to "informed" will be given a blue color code (positive) and red color code (negative). This will not change until the end of the simulation after the division of the percentage of positive rumor. ## THINGS TO NOTICE User can observe how the number of TOTAL INFORMED PEOPLE and TOTAL UNINFORMED PEOPLE changes throughout the simulation. A "slower" speed is recommended to fully observe the changes as this simulation is quite fast. Furthermore, user also can see how different percentage of positive rumor can affect the number of people informed. Another thing to observe is the TICK-STOP which will tell user when the highlighted member changes from "uninformed" to "informed". ## THINGS TO TRY User can try to play around with the sliders. As mentioned before, user can observe how different percentage of positive rumor can affect the end number of people being informed. Besides that, user can also change the power of negative w-o-m over positive w-o-m by changing the setting for a at the code bar. User may also try out the BehaviorSpace prepared to help them to see the complete quantitative data in Microsoft Excel format. The settings are adjusted according to Goldenberg et al. (2001) except for the percentage of positive rumor. User may also be interested to change the settings to match with the user's needs. ## EXTENDING THE MODEL One of the important things to be upgraded is the count of ticks. If it's possible to make it a larger count, maybe user will be able to observe the differences in different conditions. And if researchers manage to find a fix number of power for negative vs. positive w-o-m, it will be very useful and user just need to change the a. For future simulation, can add "self-enhancement" as been stated in article by Angelis et al. (2012) as an extra variable. ## RELATED MODELS The previous model of similar topic titled "Talk of the Network" which is based on a journal article titled "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth" by Goldenberg et al. (2001). ## CREDITS AND REFERENCES Angelis M.D., Bonezzi A., Peluso A. M., Rucker D.D. & Costabile M. "On Braggarts and Gossips: A Self-Enhancement Account of Word-of-Mouth Generation and Transmission". Journal of Marketing Research Vol. XLIX, 551-563. 2012. Balter, Dave (2008), The Word of Mouth Manual, Vol. 2. Boston: Bzz Pubs. Buttle FA. (1998). “Word-of-Mouth: Understanding and Managing Referral Marketing,” Journal of Strategic Marketing, 6, 241-254. Deitz, Sarah, and Idil Cakim. "Online Influence and the Tech-fluentials," July 2005: [URL: http://www.efluentials.com/documents/wommaconferencepaperjuly 132005.pdf]. Goldenberg J., Libai B. & Muller E. "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth". Marketing Letters 12:3, 211-223. 2001. Kluwer Academic Publishers, The Netherlands. Granovetter MS. (1973). “The Strength of Weak Ties,” American Journal of Sociology, 78(May), 1360-1380. Keller, Ed, and Jon Berry. The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy. New York: Free Press, 2003. Laborie, Jean-Louis. "The Theory Behind Engagement and Integration's Early Experience Across Media." Paper presented at ReThink: 52nd Annual Advertising Research Foundation Annual Conference and Expo, March 20-22, 2006: [URL: http://mail.thearf.org/roymorgan/ Engagement/2006.rethink.ARRThe%20Theory.pres.Laborie.pdf]. Westbrook, Robert A. 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