Effect of Social Media Sentiment on Donations Received by Npos

Previous literature has analyzed the effect of internet disclosure on NPO donations, specifically, through website disclosure, showing a positive relation between internet disclosure and NPO income. Nonetheless, there is a lack of studies examining the association between sentiment on social media and NPO donations. Therefore, the aim of this study is to examine the effect that sentiment in Twitter messages has on the donations received by NPOs. Using a sample of NPOs listed on the Non-Profit Times 100, we examine whether the sentiment transmitted by the NPOs through Twitter affects their donations. The results show that the sentiment associated with certain specific categories of messages (community messages and information messages about matters not directly related to the NPO) has a significant effect on the amount of donations received.


Introduction
Previous literature has examined the effect of internet disclosure on received donations, showing a positive association between internet disclosures and NPO donations (Gandía, 2011;Saxton et al., 2014;Saxton and Wang, 2014).Although these papers focus on the use of NPO websites, recent papers show the increasing relevance of social media in organizations' communication strategy (Lovejoy and Saxton, 2012;Guo and Saxton, 2014;Zahrai et al., 2022).In this line, several studies have examined the association of social media and NPO donations, showing that NPOs with dependence on donations have higher levels of social media use (Gálvez-Rodríguez et al., 2016;Campbell and Lambright, 2019).
Studies on the use of social media by organizations are interesting because of the differences in user behavior, who tend to behave in a more emotional and impulsive way (Hollebeek et al., 2014;Dwivedi et al., 2019;Zahrai et al., 2022).Nonetheless, to date, no studies have considered how sentiment on social media may affect the donations received by NPOs.Sentiment is understood as the level of polarity transmitted by a text (Kearney and Liu, 2014), and sentiment analysis of NPOs' messages on social media provides an interesting setting to examine how the use of social media by organizations affects user behavior.
In that sense, hierarchy of engagement theory (Lovejoy and Saxton, 2012) and signaling theory (Harris et al., 2021) provide a sound theoretical background that provides support for the hypothesis that the sentiment transmitted by NPOs through social media may have an effect on donations, an effect that may be different depending on the type of message.Therefore, the aim of this study is to examine whether the donations received by NPOs are affected by the sentiment of their social media posts.In particular, we examine whether the sentiment transmitted by NPOs on Twitter influences donations.
To do so, we use a sample composed of NPOs belonging to the Non-Profit Times 100 (NPT100) for the period 2015-2019 and modify the Weisbrod and Domínguez Model of Giving by including variables that proxy for social media engagement: NPO network size, NPO activity on Twitter, and sentiment transmitted by NPO posts.We estimate an aggregate annual measure that proxies for the sentiment transmitted by the NPO's messages for each year of the sample period.
Furthermore, following the hierarchy of engagement classification formulated by Lovejoy and Saxton (2012) and Svensson et al. (2015), we calculate partial measures of sentiment based on this classification that are also incorporated into the model.
The paper makes two main contributions: First, it extends the previous literature on how the use of social media can affect user behaviors; as far as we know, this is the first paper that examines whether the sentiment transmitted by NPOs' social media may have an effect on their fundraising effectiveness.Second, it contributes to the application of the hierarchy of engagement classification theorized by Lovejoy and Saxton (2012) by estimating sentiment measures based on this classification that are applied to economic models.
The paper has the following structure: After the presentation of the study, we develop the theoretical framework on which we base our hypotheses in Section 2. In Section 3, we explain the models used to test our hypotheses, the measurement of sentiment, and the sample composition.Section 4 shows the results obtained from our analysis, on which we establish our conclusions, which are presented in Section 5.

Sentiment on social media and NPO donations
Previous studies have analyzed how NPO donations are associated with online information disclosure (Gandía, 2011;Saxton et al., 2014;Saxton and Wang, 2014).Gandía (2011) measures the level of internet disclosure in a sample of Spanish NPOs and finds evidence that the level of disclosure is positively related to the future funds received by NPOs.Furthermore, Saxton et al. (2014) examine the relationship between donations and the level of internet disclosure of 400 US NPOs, and they find a significant association between donations and internet disclosure; furthermore, they find evidence that the disclosures about mission-related performance are more relevant than those referred to financial reporting.
We must note that information disclosure is not limited to NPO websites, and social media has increasing relevance in NPO communication strategies (Lovejoy and Saxton, 2012;Guo and Saxton, 2014;Zahrai et al., 2022).Social media can be particularly interesting for NPOs as a fundraising channel.Gálvez-Rodríguez et al. (2016) analyze the determinants of the use of Twitter by NPOs, and they find evidence that NPOs with a greater dependence on donations make a greater effort to use Twitter as a communication mechanism.Similarly, Cambpell and Lambright (2019) find that NPOs reliant on program service fees and government funding have lower levels of social media adoption.On the other hand, Lee (2020) finds that moderate usage of Facebook is associated with increased volunteering, showing that social media may be useful for stimulating episodic volunteering.
On the other hand, we must note that social media users tend to adopt more emotional and impulsive behavior (Hollebeek et al., 2014;Dwivedi et al., 2019;Zahrai et al., 2022), taking decisions that are not based on rational expectations about the reported information (Saxton and Wang, 2014).In this line, Hollebeek et al. (2014) examine consumer brand engagement in social media brands, and they find an association between emotional dimensions and social media brands.Dwivedi et al. (2019) develop a model that outlines how emotional brand attachment with social media explains social media consumer-based brand equity.On the other hand, Zahrai et al. (2022) find that excessive consumers of social media are driven more by their implicit attitudes than explicit beliefs in their consumption, and they provide evidence of an inconsistency between conscious attitudes and actual behavior toward social media.
With regard to the association between emotional behavior in social media and donations, Saxton and Wang (2014) examine the donations made through social media, specifically Facebook Causes, and their results suggest that donations through social media are not determined by the same factors as in the traditional environment, suggesting a "social network" effect, where the main drivers of donations are more related to impulsive concerns than to rational ones.In this line, Galiano and Ravina (2021) examine the influence of emotions and social marketing on messages about volunteering on Facebook, and they find that the number of likes received by NPOs is associated with positive emotions in messages that talk about volunteering.
The results of these studies are linked to the Hierarchy of Engagement theory developed by Lovejoy and Saxton (2012), who classify the NPO tweets into three categories: i) information messages; ii) community messages; and iii) action messages.This classification determines the process by which the organization gets its supporters involved in the NPO's projects: i) In the first stage, the NPO tries to reach out to people through the dissemination of information of interest (information); ii) in the second stage, the NPO tries to keep the flame alive by building an online community between the organization and the users (community); and iii) as a last step, the NPO tries to convince its followers to step out to action, either through donations, volunteering activities, lobbying or participation in events (action).
Several studies have analyzed in more detail the hierarchy of engagement.Campbell and Lambright (2020) examine the factors that explain differences in engagement choices by human service organizations, and they find significant associations between organizational characteristics and social media content, showing that resource dependence urges NPOs to take more proactive behavior on social media.Furthermore, based on stewardship theory, they also find that social media may be helpful as a primary mode of engagement.On the other hand, Harris et al. ( 2021) also find a significant association between audience engagement (represented by countersignaling from users, such as likes, comments, and shares) and donations.In addition to audience engagement, Harris et al. (2021) also consider social media presence and organizational effort (proxied by the number of messages).Their results suggest that the three signaling social media dimensions affect donations, thus acting as substitutes for traditional fundraising expenditures.
Considering the relevance of social media engagement on donations, as well as the more "emotional" behavior of users in social media, we must bear in mind that the willingness of users may be affected not only by the information provided by the NPOs' messages but also by the sentiment conveyed by this information.We define sentiment as the level of polarity that is transmitted from the reported text, i.e., whether the opinion expressed in the text is positive, neutral or negative, as well as other dimensions (Kearney and Liu, 2014), and textual sentiment analysis refers to the use of techniques to identify and extract subjective and qualitative information from the texts under study (Gandía and Huguet, 2021).
In the case of NPOs, we hypothesize that sentiment expressed through social media can affect the level of donations received.Theoretical support for this hypothesis is based on both social media engagement and signaling theory: On the one hand, positive messages may increase the level of engagement between the NPO and its donors, thus increasing the level of donations.On the other hand, in line with Harris et al. (2021), the sentiment transmitted by the messages may send a signal to users.Therefore, we formulate our first hypothesis: H1: The sentiment in NPOs' social media posts has a significantly positive effect on the level of NPO donations.
Nevertheless, we must note that the association between sentiment in social media and donations may be affected by the type of messages: A message that calls followers to action is not going to have the same effect as one where the NPO is simply passing on Christmas greetings; similarly, the effect of the sentiment transmitted in these messages on donations may be different depending on the message's category.Based on the hierarchy of engagement (Lovejoy and Saxon, 2012;Svensson et al., 2015;Campbell and Lambright, 2020), we set out separate hypotheses for the three categories of messages (i.e., information, community, and action), which we develop in the following subsections.

Sentiment in information messages and donations
The information category encompasses those messages whose main purpose is to provide information to the organization's followers on social media.This generic category covers messages with very diverse content, ranging from information on the activities carried out by the organization to information on events or other issues of interest to the followers.Following Lovejoy and Saxton (2012), Svensson et al. (2015) separate information messages into those that refer to the NPO's programme and those about other matters.
In line with the subcategories proposed by Svensson et al. (2015), we consider that the association between sentiment and donations may be different depending on the subcategory: With regard to messages about the NPO, a positive sentiment may increase the engagement with the NPO's followers, and thus, there will be a positive association between messages' polarity and the received donations; furthermore, positive messages may signal that the NPO is being successful in its programme, and this signal may encourage followers to donate.Therefore, we formulate our hypothesis related to this subcategory as follows: H2a: The sentiment in information messages about the NPO has a significantly positive effect on donations.
Regarding messages about other matters, we can expect a different association: Negative messages may signal warnings about present or future problems (e.g., natural disasters, famines, or wars) that could be dealt with by the NPO, and resources will be needed to face them.Therefore, the negative sentiment in these messages prepares the audience for the call to action, thus having a positive effect on the donations received by the NPO.Therefore, we expect an inverse association to that from the information messages about the NPO: Given that negative messages have a positive effect on donations, the association between sentiment in messages about other matters and donations will be negative: H2b: The sentiment in information messages about other matters has a significantly negative effect on donations.

Sentiment in community messages and donations
Community messages are those whose main purpose is the creation and maintenance of an online community between the organization and its followers.These messages may include giving recognition and thanks, acknowledgment of current and local events, reply messages, and response solicitations.Community messages are crucial to obtain and maintain engagement with followers; as stated by Lovejoy and Saxton (2012), their purpose is "keeping the flame alive".
With regard to the association of the sentiment in community messages and donations, we consider their role in the hierarchy of engagement: Positive messages may increase the affective connection between NPOs and followers, who may decide to strengthen their ties with the NPO, thus having a positive effect on donations.Therefore, we formulate our hypothesis on community messages as follows: H3: The sentiment in community messages has a significantly positive effect on donations.

Sentiment on action messages and donations
Action messages have the purpose of encouraging the organization's followers to act in a certain way, such as promoting an event, donating, buying a product, or lobbying, among others (Lovejoy and Saxton, 2012).With regard to the effect of sentiment in action messages, the reasoning is similar to that for information messages about other matters: A negative message may signal urgency or need for resources that increase the willingness to donate.Given that the effect on donations may be driven by the signaling effect of negative messages, the expected association between sentiment and donations is negative: H4: The sentiment in community messages has a significantly negative effect on the received donations.
Nevertheless, we have to note that, given that the action messages actually invite to action, the sentiment may play a secondary role on donations, and thus sentiment in action messages may not have a significant effect on donations.

Model
We test our hypotheses with the following regression models: These models are based on the model of giving used by Weisbrod and Domínguez (1986), which has been extensively used in previous literature (Marcuello and Salas, 2000;Jacobs and Marudas, 2009;Gandía, 2011;Tinkelman and Neely, 2011;Saxton et al., 2014).The W&D model assumes that the demand for a particular collective good depends on its price, the quality of the good, and the information available to the buyer about both the price and the quality of the product (Gandía, 2011).Potential donors observe the good's price (their contribution) but are uncertain about the quality of the good (the organization's use of the donation).Therefore, organizations have incentives to provide information about the characteristics of their products, both through traditional channels, such as financial information (Christensen and Mohr, 2003;Andrés-Alonso et al., 2006), and through the internet (Gandía, 2011;Saxton et al., 2014).
The dependent variable in the original W&D Model is the natural log of donations received by the organization (LN_DON), which is a proxy for the firm's demand.With regard to the control variables used in the original Model, LN _FUND is the natural log of fundraising expenditure; it shows the positive effect that fundraising expenses have on donations, analogous to the effect that advertising expenditures have on sales in corporations.LN_PRICE is the natural log of the current year's price 3 .The variable proxies the cost to a donor of purchasing one euro's worth of the organization's output.The lower the price, the more efficient the NPO is at providing program services (Gandía, 2011).AGE is the NPO's age, representing a proxy for the organization's reputation, and AGE*LN_FUND is the interaction between AGE and LN_FUND, which reflects that additional expenses in fundraising will be less effective for well-established organizations.
In addition to the variables employed by the original W&D Model, and following Saxton and Wang (2014), Model [1a] also includes two variables related to the social network size: i) the natural log of the number of followers (FOLLOW) and ii) the natural log of the number of friends (FRIENDS) of the NPO's Twitter account.These variables proxy for the social media network size; it is expected that NPOs with a greater social network receive higher donations.With regard to these variables, we have to note that historical data were not available, so we had to use the most recent data.Finally, we also include the natural log of the number of tweets published every year (TWEETS) as a proxy for the NPO's activity on Twitter; we expect that more active NPOs

Measurement of textual sentiment
The Models explained in Section 3.1 include test variables that proxy for the sentiment in the NPOs' Twitter accounts, so we need to measure the sentiment/polarity on the NPOs' messages.
Sentiment analysis is performed through two main approaches (Gandía and Huguet, 2021): i) the use of dictionaries and ii) machine learning or natural language processing (hereinafter NPL).
Sentiment analysis through dictionaries is based on the classification of words, phrases or sentences from the document that are to be examined on the basis of predefined categories (Li, 2010a).Documents are considered bags of words with an associated semantic orientation (Goel and Uzuner, 2016).Machine learning is based on the application of one or more algorithms that "learn" from a training sample, which has been manually examined to identify the sentiment contained in the sample documents.Once the algorithm has examined the sentiment patterns in the training sample, it is applied to the entire corpus to derive an index textual sentiment (Kearney and Liu, 2014).
Comparing the two approaches, machine learning is harder and time-consuming to implement because the training set must be manually classified, but it can be used when there is no specific dictionary to the language or type of document that is to be analyzed (Li, 2010a).Furthermore, unlike the dictionary-based approach, machine learning techniques do take into consideration the context of a sentence.Studies that have used machine learning show that its accuracy is usually higher than when using the dictionary-based approach (Li, 2010b;Huang et al., 2014).Therefore, we opt for the use of machine learning techniques in this study.
Once the approach to conduct sentiment analysis has been decided, the next step is to decide on which dimensions to measure the sentiment present in the messages; a classification based on the polarity of the text (negative, positive, or neutral) has been a common approach to it (Kearney and Liu, 2014;Loughran and McDonald, 2016).To determine text polarity, we use Textblob, a Python library for textual data processing, which provides a simple API for machine learning tasks, including text classification and sentiment analysis.Depending on the polarity of the text, Textblob assigns a score (ranging from -1 to 1) to the analyzed text (each of the tweets published by the NPOs).Tweets with scores below -0.05 are considered negative, tweets with scores higher than 0.05 are classified as positive, and those with scores between -0.05 and 0.05 are considered neutral.
We must note that we cannot test the isolated effect of individual tweets on NPO income since NPOs' financial information is reported on a yearly basis.Therefore, we need to transform the individual tweets' sentiment of a specific NPO into an aggregate annual measure.To do this, we estimate a standardized measure (Tetlock et al., 2008): The sentiment of a specific NPO over a year (POLARITY) is calculated as the sum of the sentiment of each of the tweets (both positive, negative, and neutral) from the NPO on a specific year, divided by the total number of tweets from the NPO during the year: However, as we explained in Section 2.1, the aggregate sentiment may not be significantly associated with donations: As explained in Section 2, the effect that the messages' sentiment has on the donations received by the NPOs may depend on the category of the tweets.In that sense, as explained in Section 2.1, Lovejoy and Saxton (2012) classified tweets into three categories (information, community, and action).This classification has been commonly used in previous literature (Svensson et al.;2014;Svensson et al., 2015;Cambpell and Lambright, 2020), so we also use this categorization.To classify the tweets into these categories, we created a set of keywords that are linked to each category, as shown in Figure 1.Categories are defined as follows: 1. Information category: Tweets containing information about the organization's activities, event highlights, or any other news, facts or relevant information to stakeholders.Their main purpose is merely to inform.Following Svensson et al. (2015), we classify these information tweets into 2 subcategories: 1) tweets that inform about the NPOs' activity and 2) tweets that inform about other matters.
2. Community category: Tweets aimed at creating an online community between the organization and its followers, which are also classified into two subcategories: 1) gratitude tweets to volunteers and/or participants and tweets acknowledging local and current events; and 2) interaction tweets with Twitter users (either by responding to users, by requesting a response from users, or by mentioning them).
3. Action category: Tweets whose purpose is to encourage their followers to act in a certain way: 1) tweets encouraging to make donations or buy products; 2) tweets encouraging to participate in campaigns and events; 3) tweets encouraging to take part in volunteering; and 4) tweets explaining how to help (tutorials).
(Figure 1) Once the categories are defined, we use the list of keywords to classify the tweets in a specific category.We must note that, based on the keyword list, some tweets may appear in more than one category.To avoid this problem, we give preference to some categories over others.
Therefore, we classify a tweet in the community category when we find at least one word from the community list of keywords.Moreover, we classify a tweet in the action category when we find at least one word from the action list of keywords (and there are no words belonging to the community list).The rest of tweets are classified as information tweets: When the tweet mentions the NPO, it is classified as an information message about the NPO's activity.If there are no references to the NPO, the tweet is classified as an information post that informs about other matters.
Hence, based on the classification of tweets into the three categories (information, community, and action), we also decompose the aggregate sentiment into three variables, referred to as each category (INFO_POLARITY, COM_POLARITY, and ACT_POLARITY).In a further analysis, we also decompose INFO_POLARITY into two variables: INFO_NPO_POL (sentiment from messages about information on the NPO) and INFO_OTHERS_POL (sentiment from messages about information on other matters).

Sample
To test our models, we gathered data from the 100 largest US NPOs in terms of total revenue, which are listed in the Times NPT100 list.Several studies on NPOs in the United States have used this list (Jacobs and Marudas, 2006;Marudas and Jacobs, 2006;Lovejoy and Saxton, 2012), which is explained by the relevance of these organizations in the US nonprofit sphere.The NPOs' financial data are directly collected from the website https://www.thenonprofittimes.com/.We have gathered financial data for the period 2012-2019.However, because of the limitations in social media data collection that we explain below, we limit our analysis to the period 2015-2019.
With regard to the data about social media sentiment and activity, we have gathered them from Twitter, which is explained by the relevance of Twitter in capturing people's global reactions (Svensson et al., 2015;Chen et al., 2019;Neu et al., 2019).For the collection of the tweets, we first obtained the Twitter accounts of the NPOs using the 2016 NPT100 list.Of the 100 listed NPOs, we found Twitter accounts for 99 of them; 8 of them were listed in 2016 but were no longer listed in 2017.For these outgoing NPOs, we collected their financial data from their Form-990.
Once we had the Twitter accounts, we extracted the tweets that were used for the estimation of the sentiment.To do this, we used a Python programming language code specifically written to interact with Twitter's application programming interface (API), allowing us to download the last 3,200 tweets from each of the organizations to a relational database.The limit of 3,200 tweets means that the temporal horizon of the Twitter activity we are able to capture depends on the level of activity of each NPO: The downloaded tweets covered a period of between 1 and 9 years; therefore, we have a shorter sample period (fewer observations) for the more active NPOs.To mitigate this limitation and obtain a more complete database, we gathered data at two separate times: October 2017 and January 2021.This procedure allows us to obtain the total sample of tweets for the period 2017-2019, but the sample suffers losses for the period 2015-2016.We tackle this limitation by performing an additional analysis including only the period 2017-2019, which is shown in Section 4.3.
Table 1 shows the distribution of tweets according to the three categories described in Section 3.2.We can see that community tweets are the most common messages (52.28% of the total sample), followed by information tweets (41.43%) and action tweets (6.29%).These percentages are quite similar to those obtained by Svensson et al. (2015), with percentages of 52.90% for information tweets, 42.80% for community tweets, and 4.30% for action tweets.With regard to the community tweets, 61.83% of them have a neutral polarity; in the action category, the most frequent polarity is positive (45.54%), while information tweets have a more even distribution (44.15% positive, 33.02% neutral).Table 2 shows the descriptive statistics of the variables included in the models.

Preliminary analysis: Aggregate sentiment measure
Table 3 shows the correlation matrix between the variables under study.We can see high correlations between the variables from the original W&D Model.In line with Lovejoy and Saxton (2012), we also observe a significant positive correlation between LN_REV and ACTION_T.
(Table 3) Table 4 shows the regression results of the original W&D Model and Models [1a] and [1b].With regard to the W&D Model, we can see that all the variables are significant.After the inclusion of the social media variables, we can see that FRIENDS is significantly positive.With regard to social media activity, TWEETS shows a significantly negative coefficient, suggesting that NPOs that have more active profiles receive fewer funds.We consider that these results may be driven by the effect of omitted variables, so we will examine them in more depth in the following regressions.Finally, POLARITY is not significant in Model 1b, suggesting that the tone of the messages does not have an effect on the received funds.Nevertheless, as explained in Section 2.1, we have to note that this lack of significance may be because the effect of the tweets' polarity may depend on the category of the message.
(Table 4) Therefore, considering the unexpected results for TWEETS and POLARITY, we decompose both the number of messages and the messages' polarity based on the hierarchy of engagement classification.The results for these regressions are shown in Section 4.2.

Main analysis: Sentiment decomposition
In this section, we run Models  5 shows the regression results of these models.
(Table 5) We can see that when considering the number of tweets belonging to each category, ACT_T is significantly positive in the three regressions, while COM_T is also significantly positive in two of the three regressions.These results suggest, in line with our hypotheses, that a higher number of tweets belonging to the community and action categories have a positive effect on the donations received by the NPOs.With regard to the community category, whose main objective is "keeping the flame alive", a higher activity helps to strengthen the engagement with the NPO's community and thus has a positive side effect on the received donations.We must note that some of these tweets are directly addressed to followers, who may develop an affective affiliation with the NPO and thus decide to strengthen their ties with the NPOs via donations.Regarding the action category, these tweets have specifically the aim of encouraging certain actions from their followers, donations being among these actions; therefore, the results confirm that tweets in this category are effective.
On the other hand, the results also show a significantly negative effect of INFO_T, suggesting that higher activity in the information category reduces the donations received.Although surprising, these results may be related to the polarity of the tweets, given that we expect that information tweets about matters other than the NPO may have an effect when they have a negative polarity.
With regard to the polarity of the tweets, we can see that ACTION_POL is not significant, while COM_POL is significantly positive.With regard to ACTION_POL, the results suggest that sentiment in these tweets does not have a significant effect on the donations; although we expected a negative effect (linked to the positive effect that negative tweets would have on the received donations), this lack of significance may be explained by the fact that action tweets directly call to the action of the followers, and thus, there is no need to adjust the strength of the message through its sentiment.Regarding COM_POL, the results suggest a positive effect of the sentiment of community tweets on the donations received.In line with the role of this category, positive messages may help to strengthen ties with the NPOs' community, and thus, followers may be more prone to contribute to the NPOs' cause.
Regarding the sentiment in information messages, we can see that INFO_POL is not significant.
Nevertheless, when we split this category into messages belonging to the NPO (INFO_NPO_POL) and other matters (INFO_OTHERS_POL), the results are slightly different: While INFO_NPO_POL remains insignificant, INFO_OTHERS_POL is significantly negative.
These results suggest that sentiment in messages with information about the NPO does not have an effect on the donations received, while sentiment in other matters is negatively associated with donations.We interpret this negative coefficient as driven by the negative messages: When talking to other matters, a negative sentiment may express concerns about dramatic events, such as famine, war, diseases, or natural disasters.Informing in negative terms about these matters will encourage followers to increase their donations, and thus, negative messages will positively affect the received donations.This association may also be related to the negative coefficient of INFO_T.

Additional analysis: 2017-2019
As we have explained in Section 3.3, the data gathering process has the limitation that we were only able to download the last 3,200 tweets of each NPO on the gathering date.Although we tried to mitigate this limitation by gathering the data at two different times, sample losses remain for the period 2015-2016.To test the effect that this limitation has on our results, we perform an additional analysis by running the models from Section 4.2 for the 2017-2019 period.The results are shown in Table 6.We can observe that although some variables lose part of their significance, the results are qualitatively similar and support those obtained in Section 4.2: COM_T loses its significance in Models 2b and 2c, while ACTION_T loses its significance in Model 2a but remains significant for Models 2b and 2c, with the p value being approximately 10%; with regard to the sentiment variables, they maintain the sign and significance of Section 4.2.Considering the results as a whole, they are in line with the previous section.

Conclusions
We have examined whether the effect of sentiment transmitted on social media by NPOs affects NPO donations.Although a preliminary analysis shows that the variable that proxies for the aggregate sentiment of the NPO does not have a significant association with the donations received, we must note that the type of message may affect how sentiment is associated with donations.Based on the hierarchy of engagement classification, we separately examined the effect of sentiment of each category (information, community, and action).The results of this analysis show that sentiment linked to the community category has a positive effect on donations, while the sentiment of information messages about matters other than the NPO has a negative association.
We consider these results to be linked to the hierarchy of engagement and signaling theory.With regard to community messages, a positive sentiment may increase the affective link between NPOs and followers, strengthening their engagement and thus increasing followers' willingness to contribute to the NPO's cause.With respect to the information category, negative messages on matters different from the NPO may send signals about problems that need resources to be dealt with.Therefore, negative information messages prepare followers to the call for action, having a positive effect on the NPO's donations.The results on the variables that proxy for social media network size and social media activity support the hierarchy of engagement classification, suggesting that emotional features have significant relevance in social media.
The results have implications for both NPOs and donors.In the case of NPOs, the study shows the relevance of social media as an alternative communication channel that may complement traditional fundraising campaigns.In particular, NPOs should keep in mind that social media users behave in a more emotional way, so the enhancement of engagement with their online community should be considered critical.Regarding donors, considering how their behavior is driven by emotional factors, they should be aware of the ability of NPOs to manage their emotions via social media.
The study presents several opportunities for future research.First, the association between sentiment and received donations found in Twitter should be explored in other social media, such as Facebook.It is important to the extent that the donors' profile may be more prone to certain media.On the other hand, we have to note that we limit our analysis to text, but some social media (such as Instagram) are more focused on other content, such as images or videos, so the analysis of the impact that this nontextual language may have on user behavior is relevant.
will receive more donations.Model [1b]  extendsModel [1a]  by including POLARITY, which represents the aggregate sentiment for an NPO's tweets in a specific year.The measurement of this variable, as well as the other ones related to the sentiment of the specific categories (which are included in Models [2b] and [2c]), is explained in Section 3.2.With regard to Model [2a], and in line with our hypotheses in Section 2, we consider that there may be differences in the impact of the NPO's activity (proxied by TWEETS) depending on the category of the messages.Therefore, Model [2a] splits TWEETS into three variables (ACT_T, COM_T, and INFO_T) that represent the number of tweets in the action, community, and information categories.Similarly, Models [2b] and [2c] include the decomposition of POLARITY into several categories: Model [2b] includes three variables (ACT_POL, COM_POL, and INFO_POL) that show the sentiment in action, community, and information messages.Model [2c] goes one step further and decomposes INFO_POL into two variables: sentiment from messages about information on the NPO (INFO_NPO_POL) and sentiment from messages about information on other matters (INFO_OTHERS_POL).