Ount of data, say for two or three weeks, is needed to give a good idea of the sentiment of a Twitter community, and if a drastic change in sentiment does occur within a community, this is a rare event and may indicate that something important has happened to or within the community.community sentiment (MC)1.2 1.rsos.royalsocietypublishing.org R. Soc. open sci. 3:…………………………………………0.8 0.6 0.4 0.2 0 0.community sentiment (SS) community sentiment (L)0.4 0.2 0 ?.2 ?.4 5.0 4.0 3.0 2.0 1.0Figure 12. The daily mean sentiment in community 2 (Indian politics) from 22 September 2014 to 1 March 2015. Five interesting events are identified.Looking at the daily PD168393 site average sentiment in each community, that is, looking at a higher resolution, more detail is evident. Figure 12 shows the daily mean sentiment in community 2 (Indian politics), also for the period 22 September 2014 to 1 March 2015. Large day-to-day variations can be seen, and we have noticed that often such abrupt changes can be traced to real events affecting the community. In figure 12, we have highlighted five dates where the sentiment measures show spikes or troughs. By examining the tweets sent on those dates we identified the significant event that drove the sentiment change:S 29 ep 2 S 01 06 ep 2 4 O 01 13 ct 2 4 O 01 20 ct 2 4 O 01 27 ct 2 4 O 0 03 ct 14 N 20 10 ov 14 N 20 17 ov 14 N 20 24 ov 14 N 201 o 4 1 v 20 D 14 e 8 c 20 D 1 15 ec 2 4 01 D 22 ec 2 4 D 01 29 ec 2 4 D 01 ec 4 5 20 Ja 1 12 n 2 4 Ja 01 19 n 2 5 Ja 01 26 n 2 5 Ja 015 2 n 20 Fe 1 5 9 b 20 Fe 1 16 b 2 5 F 01 23 eb 2 5 Fe 01 b 5 20– 24 September 2014: India’s Mars Orbiter Mission space probe entered orbit around Mars, and people celebrated. — 23 October 2014: the beginning of the Diwali festival. — 16 December 2014: gunmen affiliated with the Tehrik-i-Taliban conducted a terrorist attack in the northwestern Pakistani city of Peshawar. — 1 January 2014: New Year’s Day. — 7th January 2015: gunmen attacked the offices of the French satirical weekly newspaper Charlie Hebdo in Paris.5. An agent-based model of sentiments dynamics in communitiesIt has been discovered time after time that the collective behaviour of Torin 1 side effects populations of interacting individuals is difficult to understand, challenging to predict and sometimes even seemingly paradoxical. In order to be able to predict the likely evolution of sentiment within a community and to explore its dynamics under various change scenarios, such as the departure of particular users or the arrival of a new vocal user, we built an ABM of our Twitter communities. This includes modelling the sentiment of individuals in the network, and how sentiment spreads from one user to another. The agents in the model represent Twitter users, and they are arranged in a static undirected graph; only pairs of agents connected by an edge are able to exchange messages. The simulation proceeds in discrete time steps; the number of these steps per day is a parameter of the model. At each time step the following things happen: — Each agent performs an action which consists of sending a burst of messages to all/some/none of its neighbours, influenced by the agent’s current state. — Each agent evolves into a new state, influenced by the actions of other agents in this step, i.e. influenced by the messages it has received this step. Specifically, an action by an agent consists of: a subset of neighbours who will be messaged at this time step; for each neighbour messaged,.Ount of data, say for two or three weeks, is needed to give a good idea of the sentiment of a Twitter community, and if a drastic change in sentiment does occur within a community, this is a rare event and may indicate that something important has happened to or within the community.community sentiment (MC)1.2 1.rsos.royalsocietypublishing.org R. Soc. open sci. 3:…………………………………………0.8 0.6 0.4 0.2 0 0.community sentiment (SS) community sentiment (L)0.4 0.2 0 ?.2 ?.4 5.0 4.0 3.0 2.0 1.0Figure 12. The daily mean sentiment in community 2 (Indian politics) from 22 September 2014 to 1 March 2015. Five interesting events are identified.Looking at the daily average sentiment in each community, that is, looking at a higher resolution, more detail is evident. Figure 12 shows the daily mean sentiment in community 2 (Indian politics), also for the period 22 September 2014 to 1 March 2015. Large day-to-day variations can be seen, and we have noticed that often such abrupt changes can be traced to real events affecting the community. In figure 12, we have highlighted five dates where the sentiment measures show spikes or troughs. By examining the tweets sent on those dates we identified the significant event that drove the sentiment change:S 29 ep 2 S 01 06 ep 2 4 O 01 13 ct 2 4 O 01 20 ct 2 4 O 01 27 ct 2 4 O 0 03 ct 14 N 20 10 ov 14 N 20 17 ov 14 N 20 24 ov 14 N 201 o 4 1 v 20 D 14 e 8 c 20 D 1 15 ec 2 4 01 D 22 ec 2 4 D 01 29 ec 2 4 D 01 ec 4 5 20 Ja 1 12 n 2 4 Ja 01 19 n 2 5 Ja 01 26 n 2 5 Ja 015 2 n 20 Fe 1 5 9 b 20 Fe 1 16 b 2 5 F 01 23 eb 2 5 Fe 01 b 5 20– 24 September 2014: India’s Mars Orbiter Mission space probe entered orbit around Mars, and people celebrated. — 23 October 2014: the beginning of the Diwali festival. — 16 December 2014: gunmen affiliated with the Tehrik-i-Taliban conducted a terrorist attack in the northwestern Pakistani city of Peshawar. — 1 January 2014: New Year’s Day. — 7th January 2015: gunmen attacked the offices of the French satirical weekly newspaper Charlie Hebdo in Paris.5. An agent-based model of sentiments dynamics in communitiesIt has been discovered time after time that the collective behaviour of populations of interacting individuals is difficult to understand, challenging to predict and sometimes even seemingly paradoxical. In order to be able to predict the likely evolution of sentiment within a community and to explore its dynamics under various change scenarios, such as the departure of particular users or the arrival of a new vocal user, we built an ABM of our Twitter communities. This includes modelling the sentiment of individuals in the network, and how sentiment spreads from one user to another. The agents in the model represent Twitter users, and they are arranged in a static undirected graph; only pairs of agents connected by an edge are able to exchange messages. The simulation proceeds in discrete time steps; the number of these steps per day is a parameter of the model. At each time step the following things happen: — Each agent performs an action which consists of sending a burst of messages to all/some/none of its neighbours, influenced by the agent’s current state. — Each agent evolves into a new state, influenced by the actions of other agents in this step, i.e. influenced by the messages it has received this step. Specifically, an action by an agent consists of: a subset of neighbours who will be messaged at this time step; for each neighbour messaged,.
